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
This office action is in response to the claims filed on 11/05/2021.
Claims 21-40 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) filed 03/31/2025 is in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
6. The claim 16 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skills in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non- structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such as claim limitation(s): “executable portion” in the claim 16
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-
AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C.
112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/those being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 16
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16 recite “executable portion” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. “Configured to generate one or more segments, an executable portion configured to identify a user, to determine, using the one or more machine learning models, at least one of the one or more segments to present to the user, configured to present, configured to conduct a series of conversational, configured to assess a level of competency”. Therefore, the claims 1, 2, 3, 6-19 are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a system (claims 1-10), system (claims 11-15) and computer program product (claims 16-20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” and mathematical concept.
Claim 1 recites:
Step 2A: prong 1 analysis:
“determine at least one of the one or more segments to present to a user” this is a mental process, the human can determine the segment to present to a user, (Evaluation);
-“and assess a level of competency of the user in the at least one subject matter associated with the at least one segment presented” this is a mental process, the human can assess/determine a competency level of the user based on the subject matter associated with one presented segment, for example, during an conversation between two people, the human can tell whether other people is interesting to a topic in the conversation, (observation/Evaluation).
-“identify the user” this is a mental process, the human can identify the user, (observation/Evaluation),
-“ determine, the at least one segment to present to the user based, at least, on the one or more of characteristic data and historical learning data of the user;” this is a mental process, the human mind can determine which segment to present to the user based on their behavior and the conversation history with the user, (observation/Evaluation).
-“conduct a series of conversational interactions with the user” this is a mental process, the human can conduct more conversation with the user, (observation/Evaluation).
-“ assess the level of competency of the user in the at least one subject matter associated with the at least one presented segment, based, at least, on the series of conversational interactions with the user” this is a mental process, the human can determine a competency level of the user based on the subject matter associated with one presented segment and the serries of the conversation, (observation, Evaluation).
Step 2A: Prong 2 analysis:
-“ a system for implementing a learning application with conversational interactions, the system comprising: an artificial intelligence system comprising: one or more machine learning models, wherein the one or more machine learning models are trained to, at least: generate one or more segments, wherein each of the one or more segments is associated with at least one subject matter”, “using the one or more machine learning models”, “using the artificial intelligence system”, “using the artificial intelligence system;” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“and a learning application configured to: receive one or more of characteristic data and historical learning data associated with the user” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
-“present to the user the at least one determined segment;” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“ a system for implementing a learning application with conversational interactions, the system comprising: an artificial intelligence system comprising: one or more machine learning models, wherein the one or more machine learning models are trained to, at least: generate one or more segments, wherein each of the one or more segments is associated with at least one subject matter”, “using the one or more machine learning models”, “using the artificial intelligence system”, “using the artificial intelligence system;” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“and a learning application configured to: receive one or more of characteristic data and historical learning data associated with the user” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). (this evidence is applied for data gathering/storing data).
-“present to the user the at least one determined segment;” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Claim 2 recites:
Step 2A: Prong 2 analysis:
“wherein the learning application is an interactive user application and wherein assessing the level of competency of the user comprises analyzing the conversational interactions with the user conducted through the learning application.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
Step 2B analysis:
“wherein the learning application is an interactive user application and wherein assessing the level of competency of the user comprises analyzing the conversational interactions with the user conducted through the learning application.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
Claim 3 recites:
Step 2A: prong 1 analysis:
“wherein analyzing user interactions comprises determining the time taken by the user to respond in the conversational interactions” This is a mental process, the human can determine/control the time taken to respond in the conversation, (observation/Evaluation).
Step 2A: Prong 2 analysis:
“the conversational interactions with the artificial intelligence system” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
Step 2B analysis:
“the conversational interactions with the artificial intelligence system” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
Claim 4 recites:
Step 2A: prong 1 analysis:
-“ conducting the series of conversational interactions with the user until a threshold level of competency is determined to have been achieved by the user based at least on one of the at least one subject matter associated with the one or more presented segments and the historical learning data associated with the user.” This is a mental process, the human can conduct the series of conversation interaction with user until some level of competency is determined cased on the topic during the conversation and the historical data with the user, (observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
Claim 5 recites:
Step 2A: Prong 2 analysis:
-“ wherein the learning application is configured to present the at least one determined segment to the user in a pop-up window while the user is both actively engaged with a secondary application and not currently engaged with the learning application.” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“ wherein the learning application is configured to present the at least one determined segment to the user in a pop-up window while the user is both actively engaged with a secondary application and not currently engaged with the learning application.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Claim 6 recites:
Step 2A: prong 1 analysis:
-“ assess the level of competency of the user in the at least one subject matter associated with the at least one presented segment” this is a mental process, the human mind can assess the level of competency of the user in one topic associated with the presented segment, (observation/evaluation).
Step 2A: Prong 2 analysis:
-“the learning application” the additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“presented segment via the pop-up window.” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“the learning application” the additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“presented segment via the pop-up window.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Claim 7 recites:
Step 2A: Prong 2 analysis:
-“ wherein the artificial intelligence system triggers the learning application to present at least one of the one or more segments when at least one subject matter that the at least one segment is associated with is related to content in interactions of the user with the secondary application or to information presented in the secondary application.” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“ wherein the artificial intelligence system triggers the learning application to present at least one of the one or more segments when at least one subject matter that the at least one segment is associated with is related to content in interactions of the user with the secondary application or to information presented in the secondary application.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Claim 8 recites:
Step 2A: prong 1 analysis:
-“ wherein the one or more subject matters associated with the one or more segment changes over time” this is a mental process, the human can change the topic of the conversation, (observation/Evaluation).
Step 2A: Prong 2 analysis:
“and wherein the one or more machine learning models are further trained to incorporate the changes into the segment.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (machine learning models) (See MPEP 2106.05(f)).
Step 2B analysis:
“and wherein the one or more machine learning models are further trained to incorporate the changes into the segment.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (machine learning models) (See MPEP 2106.05(f)).
Claim 9 recites:
Step 2A: prong 1 analysis:
-“ determine which of the one or more segments to present to the user and to assess the level of competency of the user.” This is a mental process, the human mind can determine which segment to present to the user and assess the level of competence of the user (observation/Evaluation).
Step 2A: Prong 2 analysis:
-“wherein the one or more machine learning models are further trained to use the characteristic data and historical learning data of a plurality of users of the learning application” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
Step 2B analysis:
-“wherein the one or more machine learning models are further trained to use the characteristic data and historical learning data of a plurality of users of the learning application” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
Claim 10 recites:
Step 2A: Prong 2 analysis:
-“ wherein historical learning data associated with the user comprises data gathered by the artificial intelligence system when conducting the series of conversational interactions with the user and wherein the artificial intelligence system is configured to update the historical learning data during and after the series of conversational interactions with the user.” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“ wherein historical learning data associated with the user comprises data gathered by the artificial intelligence system when conducting the series of conversational interactions with the user and wherein the artificial intelligence system is configured to update the historical learning data during and after the series of conversational interactions with the user.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). (this evidence is applied for data gathering/storing data).
Claims 11-15 are rejected for the same reason as claims 1-5, since these claims recite the same limitations.
Claim 16 recites:
Step 2A: prong 1 analysis:
-“identify a user” this is a mental process, the human mind can identify the user, (observation/Evaluation).
-“ determine, at least one of the one or more segments to present to the user based at least on the one or more of characteristic data and historical data of the user” this is a mental process, the human mind can determine one or more segment to present to user based on the user’s behaviors and the history data with the user, (observation/Evaluation)
-“assess a level of competency of the user in the at least one subject matter associated with the at least one segment presented … based, at least, on the series of conversation interactions with the user.” this is a mental process, the human can assess/determine a competency level of the user based on the subject matter associated with one presented segment, for example, during an conversation between two people, the human can tell whether other people is interesting to a topic in the conversation, (observation/Evaluation).
-“ to conduct a series of conversational interactions with the user” this is a mental process, the human can conduct the series of the conversation interaction with the user, (observation/Evaluation).
Step 2A: Prong 2 analysis:
-“ an executable portion configured to generate one or more segments using one or more machine learning models, wherein each of the one or more segments is associated with at least one subject matter”, This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
-“ an executable portion configured to”, “using the one or more machine learning models,” , “an executable portion configured using the one or more machine learning models; These additional limitation are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“and receive one or more of characteristic data and historical learning data associated with the user”, These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
-“an executable portion configured to present the at least one determined segment to the user; These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“ an executable portion configured to generate one or more segments using one or more machine learning models, wherein each of the one or more segments is associated with at least one subject matter” These additional limitation are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“ an executable portion configured to”, “using the one or more machine learning models,” , “an executable portion configured using the one or more machine learning models” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself.
-“ and receive one or more of characteristic data and historical learning data associated with the user”, These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
-“an executable portion configured to present the at least one determined segment to the user” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Claims 17-20 are rejected for the same reason as claims 2-5, since these claims recite the same limitations.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims1-4, 8-10, 11-14, 16-19are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Aggarwal et al. (Pub. No. US20240202284– hereinafter, Aggarwal).
Regarding to claim 1, Aggarwal teaches a system for implementing a learning application with conversational interactions, the system comprising: an artificial intelligence system comprising one or more machine learning models (Aggarwal, [Par.0025], “In the second aspect, a system for monitoring and improving conversational alignment to develop an alliance between an artificial intelligence (AI) chatbot and a user is provided. The system includes a device processor and a non-transitory computer readable storage medium storing one or more sequences of instructions, which when executed by the device processor, causes a method by performing the steps. The method includes providing a first prompt to the user by the AI chatbot to obtain a first response from the user for the first prompt. The first response is at least one of a text input or a voice input. The method includes extracting sentiment or at least one contextual feature from the first response when the at least one contextual feature is present in the first response. Extracting the at least one contextual feature comprises detecting that the first response of the user includes at least one of emotion, medicalized terms or domain, wherein (i) the emotion is detected using an emotion detecting artificial intelligence (AI) model, (ii) the medicalized terms are detected using a medicalized term detecting AI model, and (iii) the domain is detected using a domain detecting AI model. The method includes generating, using an AI model, a second prompt based on the at least one contextual feature includes at least one of the emotion, the medicalized terms or the domain.”) ,
wherein the one or more machine learning models are trained to, at least: generate one or more segments, wherein each of the one or more segments is associated with at least one subject matter (Aggarwal, [Par.0021], “The method includes extracting sentiment or at least one contextual feature from the first response when the at least one contextual feature is present in the first response. Extracting the at least one contextual feature comprises detecting that the first response of the user includes at least one of emotion, medicalized terms or domain, wherein (i) the emotion is detected using an emotion detecting artificial intelligence (AI) model, (ii) the medicalized terms are detected using a medicalized term detecting AI model, and (iii) the domain is detected using a domain detecting AI model. The method includes generating, using an AI model, a second prompt based on the at least one contextual feature includes at least one of the emotion, the medicalized terms or the domain.”) ;
determine at least one of the one or more segments to present to a user (Aggarwal, [Par.0021], “The method includes generating, using an AI model, a second prompt based on the at least one contextual feature includes at least one of the emotions, the medicalized terms or the domain.” Examiner’s note, the AI chatbot determines the second response to present to the user, as the example in [Par.0073], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102.”.);
and assess a level of competency of the user in the at least one subject matter associated with the at least one segment presented (Aggarwal, [Par.0021, 0047], “[0021], The method includes determining, using the AI model, if a conversation between the user and the AI chatbot has a conversational alignment. The conversational alignment is an alignment with respect to the user sharing more context with the AI chatbot and agreeing to suggestions or interpretations made by the AI chatbot. The method includes increasing, using the AI model, a conversational alignment score for a second response of the user in the conversation if the conversational alignment is determined. The method includes monitoring, using the AI model, the conversation to determine if there is a misalignment in the conversation between the user and the AI chatbot and reduce the conversational alignment score if the misalignment is detected.” And [Par.0047], “The AI models may be trained on training data obtained from multiple sources, such as user messages from conversations in the past with any Personal Identifiable Information (PII) replaced by synthetic data, manually created examples (i.e., synthetic statements manually created by data scientists based on the expected response of the user 102), third-party public datasets, e.g., Kaggle, and Web scraping, e.g., from Reddit, Twitter by Twitter, Inc, news websites, etc.” Examiner’s note, determining the conversation alignment based on a relevant contextual feature between the user and the AI chat bot, therefore, the conversation alignment is considered as the level of competency of the user.);
and a learning application configured to: identify the user (Aggarwal , [Par.0047], “In some embodiments, the server 108 includes one or more AI models to detect and extract one or more relevant conversational features of the user text. The one or more relevant features correspond to any aspect of the user message that are used to assist the AI chatbot 110 to understand and respond to the user 102 better so that the user 102 feels heard and understood. In some embodiments, detection of the one or more relevant conversational features include detection of whether the user 102 is agreeing (e.g., “Yes, that's right.”) or disagreeing with the chatbot 110 (e.g., “I don't think so”), whether the user 102 is in distress and might need immediate help (e.g., “I'm having a panic attack”), confused (e.g., “What do you mean?”), unhappy with the chatbot 110 (e.g., “You're not even helping!”), uncertain about how to respond (e.g., “Umm . . . I don't really know”), lacking trust in the AI chatbot 110 (e.g., “I don't think you can help me”), etc. In addition, the AI model 112 extracts one or more contextual features that are used to assist the chatbot 110 to respond to the user 102 better. The contextual features may include a domain, e.g., what the user 102 is talking about (relationship, education, health, money, politics, sports, etc.); entity, e.g., who the user 102 is talking about (self, family member, coworker, friend, etc.); sentiment, i.e., whether the tone of the user 102's message is positive, negative or neutral; emotion, e.g., how the user 102 is feeling (sad, angry, happy, frustrated, scared, etc.); among other things the user 102 might mention (activities, medicalized terms, events, etc.).” Examiner’s note, the AI model detects who is the user talking about whether self or someone else, that is corresponding to identifying the user.);
receive one or more of characteristic data and historical learning data associated with the user (Aggarwal, [Par.0048], “The AI models may be trained on training data obtained from multiple sources, such as user messages from conversations in the past with any Personal Identifiable Information (PII) replaced by synthetic data, manually created examples (i.e., synthetic statements manually created by data scientists based on the expected response of the user 102), third-party public datasets, e.g., Kaggle, and Web scraping, e.g., from Reddit, Twitter by Twitter, Inc, news websites, etc.”);
determine, using the one or more machine learning models, the at least one segment to present to the user based, at least, on the one or more of characteristic data and historical learning data of the user (Aggarwal, [Par.0049], “In some embodiments, the open-ended prompts or the closed-ended prompts are generated based on predefined base prompts written by conversation designers. These base prompts are parameterized with the user's context to personalize them. For example, an open-ended prompt could comprise a context-based empathetic statement followed by a predefined base prompt like. “How did (context) make you feel?”. Thus, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement based on this context like “I understand things are not going well with your colleague at the moment.”, followed by the contextualized base prompt like “How did this conflict make you feel?”. In some embodiments, the predefined base prompts are stored in the server 108.” Examiner’s note, the AI model determines a response prompt to present to the user based on a previously responded from the user.) ;
present to the user the at least one determined segment; conduct a series of conversational interactions with the user using the artificial intelligence system (Aggarwal, [Par.00073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110. [0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”. The user 102 may respond with 312 to the open-ended prompt 310 provided by the AI chatbot 110, e.g., “I'm also a little lonely because my mother is scared of COVID.” The emotion detecting AI model 206 detects the emotion, e.g., “lonely” of the user 102. The conversational alignment monitoring module 212 increases the conversational alignment score by 5 to a total of 10 because the user shared further context.” Examiner’s note, the AI model continuously monitors previous user’s response to determine the segment to present to the user.) ;
assess the level of competency of the user in the at least one subject matter associated with the at least one presented segment, , using the artificial intelligence system and based, at least, on the series of conversational interactions with the user. (Aggarwal, [Par,0049-0051], “In some embodiments, the open-ended prompts or the closed-ended prompts are generated based on predefined base prompts written by conversation designers. These base prompts are parameterized with the user's context to personalize them. For example, an open-ended prompt could comprise a context-based empathetic statement followed by a predefined base prompt like. “How did (context) make you feel?”. Thus, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement based on this context like “I understand things are not going well with your colleague at the moment.”, followed by the contextualized base prompt like “How did this conflict make you feel?”. In some embodiments, the predefined base prompts are stored in the server 108. In some embodiments, the open-ended prompts or the closed-ended prompts are provided alternately to the user 102 based on a conversational alignment score. [0050] The AI model 112 continuously monitors the conversation between the user 102 and the AI chatbot 110 to determine if the conversation between the user 102 and the AI chatbot 110 has conversational alignment. The conversational alignment is an alignment with respect to the user 102 sharing more context or agreeing with the AI chatbot 110 when prompted by the AI chatbot 110. For example, if the AI chatbot 110 provides the open-ended prompt, (e.g., “What bothered you the most about this situation?”) or the closed-ended prompt (e.g., “Do you sometimes wish that you had more control over how others act?”), the user 102 may share more (e.g., “That I am not considered important by my family”) or agree with the chatbot 110 (e.g., “Yes, I think so.”), thus indicating conversational alignment. In some embodiments, the AI model 112 determines a misalignment by the user's confusion, (e.g., “What do you mean by that?”), dissatisfaction (e.g., “You don't understand me!”), disagreement (e.g., “No, that's not what I meant”), lack of trust (e.g., “A bot can't help me”), or uncertainty (e.g., “I'm not sure”). [0051] The AI model 112 maintains the conversational alignment score, which indicates the strength of the alliance formed between the AI chatbot 110 and the user 102. The conversational alignment score is updated after each message exchange based on the conversation of the user 102 with the AI chatbot 110.” Examiner’s note, Using the AI model to determine the conversation alignment, wherein the conversation alignment indicates the strength of the conversation between the user and AI chatbot, therefore, level of competency is considered as the conversation alignment, wherein the conversation alignment associated with the at least one presented segment. ).
Regarding claim 2, Aggarwal teaches the system of claim 1, wherein the learning application is an interactive user application (Aggarwal, [par.0042], “FIG. 1 is a block diagram 100 that illustrates a system that monitors and improves a conversational alignment between an artificial intelligence (AI) chatbot 110 and a user 102 according to some embodiments herein. The block diagram 100 includes a user device 104 associated with the user 102, a network 106, and a server 108 that includes an artificial intelligence (AI) model 112. In some embodiments, the user device 104 includes the Artificial Intelligence (AI) chatbot 110. In some embodiments, the user device 104, without limitation, may be selected from a mobile phone, a Personal Digital Assistant (PDA), a tablet, a desktop computer, or a laptop.” Examiner’s note, Fig.1 discloses the interaction between the user device and server includes the AI model.)
wherein assessing the level of competency of the user comprises analyzing the conversational interactions with the user conducted through the learning application (Aggarwal, [Par,0049-0051], “In some embodiments, the open-ended prompts or the closed-ended prompts are generated based on predefined base prompts written by conversation designers. These base prompts are parameterized with the user's context to personalize them. For example, an open-ended prompt could comprise a context-based empathetic statement followed by a predefined base prompt like. “How did (context) make you feel?”. Thus, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement based on this context like “I understand things are not going well with your colleague at the moment.”, followed by the contextualized base prompt like “How did this conflict make you feel?”. In some embodiments, the predefined base prompts are stored in the server 108. In some embodiments, the open-ended prompts or the closed-ended prompts are provided alternately to the user 102 based on a conversational alignment score. [0050] The AI model 112 continuously monitors the conversation between the user 102 and the AI chatbot 110 to determine if the conversation between the user 102 and the AI chatbot 110 has conversational alignment. The conversational alignment is an alignment with respect to the user 102 sharing more context or agreeing with the AI chatbot 110 when prompted by the AI chatbot 110. For example, if the AI chatbot 110 provides the open-ended prompt, (e.g., “What bothered you the most about this situation?”) or the closed-ended prompt (e.g., “Do you sometimes wish that you had more control over how others act?”), the user 102 may share more (e.g., “That I am not considered important by my family”) or agree with the chatbot 110 (e.g., “Yes, I think so.”), thus indicating conversational alignment. In some embodiments, the AI model 112 determines a misalignment by the user's confusion, (e.g., “What do you mean by that?”), dissatisfaction (e.g., “You don't understand me!”), disagreement (e.g., “No, that's not what I meant”), lack of trust (e.g., “A bot can't help me”), or uncertainty (e.g., “I'm not sure”). [0051] The AI model 112 maintains the conversational alignment score, which indicates the strength of the alliance formed between the AI chatbot 110 and the user 102. The conversational alignment score is updated after each message exchange based on the conversation of the user 102 with the AI chatbot 110.” Examiner’s note, AI model to determine the conversation alignment, wherein the conversation alignment indicates the strength of the conversation based on the exchanged messages between the user and AI chatbot. )
Regarding claim 3, Aggarwal teaches the system of claim 2, wherein analyzing user interactions comprises determining the time taken by the user to respond in the conversational interactions with the artificial intelligence system (Aggarwal, [Par.0046-0047], “In some embodiments, the AI chatbot 110 may not wait for the alliance to be established before recommending a solution to the user 102 if the user message indicates an urgent need that must be immediately addressed… whether the user 102 is in distress and might need immediate help (e.g., “I'm having a panic attack”)...” and [Par.0056], “In some embodiments, if the medicalized term detecting AI model 208 detects one or more medicalized terms, the medicalized term detecting AI model 208 then checks if the user 102 is mentioning those terms with respect to themselves, and if the user 102 is in distress and needs immediate help. The domain detecting AI model 210 extracts a topic of the conversation such as relationships, work, education, and abuse from the user text. In some embodiments, if multiple contextual features (e.g., emotions, domains, medicalized terms) are detected in the context gathered from the user 102, they are prioritized in the following order to decide which direction to take the conversation in: (i) medicalized terms, (ii) domain, and (iii) emotion. In some embodiments, the relevant pieces of contextual information extracted from the user's texts are stored in the database 200 of the server 108.” Examiner’s note, the AI model detects that the user needs immediately help, so the response can be immediately addressed.).
Regarding claim 4, Aggarwal teaches the system of claim 1, wherein assessing the level of competency of the user further comprises conducting the series of conversational interactions with the user until a threshold level of competency is determined to have been achieved by the user based at least on one of the at least one subject matter associated with the one or more presented segments and the historical learning data associated with the user (Aggarwal, [Par.0049-0053], “[0049], In some embodiments, the open-ended prompts or the closed-ended prompts are generated based on predefined base prompts written by conversation designers. These base prompts are parameterized with the user's context to personalize them. For example, an open-ended prompt could comprise a context-based empathetic statement followed by a predefined base prompt like. “How did (context) make you feel?”. Thus, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement based on this context like “I understand things are not going well with your colleague at the moment.”, followed by the contextualized base prompt like “How did this conflict make you feel?”...[0053], The AI model 112 continuously monitors the conversational alignment score and compares the conversational alignment score to a threshold after every message exchange and score update. In some embodiments, the threshold for the conversational alignment score is determined based on data analysis to determine which value results in best performance over a validation dataset. When the conversational alignment score exceeds the threshold, the server 108 dynamically provides an alliance confirmation prompt to the user 102 to confirm the establishment of the alliance and checks if the user 102 is now ready to accept an intervention, e.g., “Thanks for sharing that with me. I have something that could help you manage this better. Would you like to try it out?”. In some embodiments, when the alliance is established, the alliance may be similar to a therapeutic alliance between a human therapist and the user 102. In some embodiments, when the alliance is established and confirmed by the user 102, the AI chatbot 110 may recommend some interventions or solutions to the user 102. In some embodiments, a recommended activity is a therapeutic intervention, e.g., a breathing exercise, a physical activity, writing down and reframing thoughts, etc. ” Examiner’s note, the contextual feature (subject matter) is extracted based on an user’s response, and each of the user’s response associated with the conversational alignment score, for example, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement. The conversational alignment between the user and AI chatbot is determined based on the response from the user during the conversation, for example, the AI model continuously monitors the conversational alignment score and compares the conversational alignment score to a threshold after every message exchange and score update. When the conversational alignment score exceeds the threshold, the server 108 dynamically provides an alliance confirmation prompt to the user 102 to confirm the establishment of the alliance and checks if the user 102 is now ready to accept an intervention. then. Therefore, assessing the level of competency (determining the conversational alignment) comprises conducting the series of conversational interactions with the user until a threshold level of competency is determined to have been achieved associated with particular subject matter (extracted contextual feature).).
Regarding claim 8, Aggarwal teaches the system of claim 1, wherein the one or more subject matters associated with the one or more segment changes over time, ([Par.0073-0074], “[0073], In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110. [0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”..” Examiner’s note, the response prompt to the user will be changed based on a changing of the topic of the user’s response, For example, if the emotion detecting AI model detects the medicalized term in the initial response from the user, then the server will generate the next prompt to user based on a medical issue, if the emotion detecting AI model detect emotion in the user’s response from the user, then the server will generate the next prompt to user based on an emotion issue).
and wherein the one or more machine learning models are further trained to incorporate the changes into the segment ([Par.0073-0075], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110…[0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”. The user 102 may respond with 312 to the open-ended prompt 310 provided by the AI chatbot 110, e.g., “I'm also a little lonely because my mother is scared of COVID.” The emotion detecting AI model 206 detects the emotion, e.g., “lonely” of the user 102. The conversational alignment monitoring module 212 increases the conversational alignment score by 5 to a total of 10 because the user shared further context. [0075] The prompt generating module 211 generates and provides an empathetic closed-ended prompt 314 to the user 102 through the AI chatbot 110 based on the emotion of the user 102, e.g., “Loneliness can feel like nobody understands us, or that we're disconnected from our own selves. And is this bringing up other feelings as well for you, Alex?” The user 102 may respond with 316 to the closed-ended prompt 314 provided by the AI chatbot 110, e.g., “Yes, I feel ostracized.” The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score by 5 to 15 because the user 102 shared more when prompted.” Examiner’s note, the machine learning models are trained to determine a next response to present to the user based on a particular topic is extracted in a previous response from the user. For example, if the emotion detecting AI model detects the medicalized term in the initial response from the user, then the server will generate the next prompt to user based on a medical issue, if the emotion detecting AI model detect emotion in the initial response from the user, then the server will generate the next prompt to user based on an emotion issue.).
Regarding claim 9, Aggarwal teaches the system of claim 1, wherein the one or more machine learning models are further trained to use the characteristic data and historical learning data of a plurality of users of the learning application to determine which of the one or more segments to present to the user (Aggarwal, [Par.0073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110.[0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”” Examiner’s note, the response prompt is present to the user based on the topic/contextual feature in the user previously responded. Therefore, the previous response of the user is considered as the historical learning data of a plurality of users. )
and to assess the level of competency of the user (Aggarwal, [Par.0073-0074], “ In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110. [0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”. The user 102 may respond with 312 to the open-ended prompt 310 provided by the AI chatbot 110, e.g., “I'm also a little lonely because my mother is scared of COVID.” The emotion detecting AI model 206 detects the emotion, e.g., “lonely” of the user 102. The conversational alignment monitoring module 212 increases the conversational alignment score by 5 to a total of 10 because the user shared further context.” Examiner’s note, the conversational alignment score is generated based on the user’s response. )
Regarding claim 10, Aggarwal teaches the system of claim 1, wherein historical learning data associated with the user comprises data gathered by the artificial intelligence system when conducting the series of conversational interactions with the user ([Par.0048], “[0048] The AI models may be trained on training data obtained from multiple sources, such as user messages from conversations in the past with any Personal Identifiable Information (PII) replaced by synthetic data, manually created examples (i.e., synthetic statements manually created by data scientists based on the expected response of the user 102), third-party public datasets, e.g., Kaggle, and Web scraping, e.g., from Reddit, Twitter by Twitter, Inc, news websites, etc.”)
and wherein the artificial intelligence system is configured to update the historical learning data during and after the series of conversational interactions with the user (Aggarwal, [Par.0073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110.[0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”” Examiner’s note, the AI model will generate the next response to the user based on the previous response from the user.).
Regarding claims 11-14 are rejected for the same reason as claims 1-4, since these claims recite the same limitations.
Regarding claim 16, Aggarwal teaches a computer program product for implementing learning applications with conversational interactions, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer readable code portions comprising: an executable portion configured to generate one or more segments using one or more machine learning models (Aggarwal, [Par.0025], “In the second aspect, a system for monitoring and improving conversational alignment to develop an alliance between an artificial intelligence (AI) chatbot and a user is provided. The system includes a device processor and a non-transitory computer readable storage medium storing one or more sequences of instructions, which when executed by the device processor, causes a method by performing the steps. The method includes providing a first prompt to the user by the AI chatbot to obtain a first response from the user for the first prompt. The first response is at least one of a text input or a voice input. The method includes extracting sentiment or at least one contextual feature from the first response when the at least one contextual feature is present in the first response. Extracting the at least one contextual feature comprises detecting that the first response of the user includes at least one of emotion, medicalized terms or domain, wherein (i) the emotion is detected using an emotion detecting artificial intelligence (AI) model, (ii) the medicalized terms are detected using a medicalized term detecting AI model, and (iii) the domain is detected using a domain detecting AI model. The method includes generating, using an AI model, a second prompt based on the at least one contextual feature includes at least one of the emotion, the medicalized terms or the domain. The method includes determining, using the AI model, if a conversation between the user and the AI chatbot has a conversational alignment. The conversational alignment is an alignment with respect to the user sharing more context with the AI chatbot and agreeing to suggestions or interpretations made by the AI chatbot. The method includes increasing, using the AI model, a conversational alignment score for a second response of the user in the conversation if the conversational alignment is determined.”).,
wherein each of the one or more segments is associated with at least one subject matter (Aggarwal, [Par.0021], “The method includes extracting sentiment or at least one contextual feature from the first response when the at least one contextual feature is present in the first response. Extracting the at least one contextual feature comprises detecting that the first response of the user includes at least one of emotion, medicalized terms or domain, wherein (i) the emotion is detected using an emotion detecting artificial intelligence (AI) model, (ii) the medicalized terms are detected using a medicalized term detecting AI model, and (iii) the domain is detected using a domain detecting AI model. The method includes generating, using an AI model, a second prompt based on the at least one contextual feature includes at least one of the emotion, the medicalized terms or the domain.”) ;
an executable portion configured to identify a user (Aggarwal , [Par.0047], “In some embodiments, the server 108 includes one or more AI models to detect and extract one or more relevant conversational features of the user text. The one or more relevant features correspond to any aspect of the user message that are used to assist the AI chatbot 110 to understand and respond to the user 102 better so that the user 102 feels heard and understood. In some embodiments, detection of the one or more relevant conversational features include detection of whether the user 102 is agreeing (e.g., “Yes, that's right.”) or disagreeing with the chatbot 110 (e.g., “I don't think so”), whether the user 102 is in distress and might need immediate help (e.g., “I'm having a panic attack”), confused (e.g., “What do you mean?”), unhappy with the chatbot 110 (e.g., “You're not even helping!”), uncertain about how to respond (e.g., “Umm . . . I don't really know”), lacking trust in the AI chatbot 110 (e.g., “I don't think you can help me”), etc. In addition, the AI model 112 extracts one or more contextual features that are used to assist the chatbot 110 to respond to the user 102 better. The contextual features may include a domain, e.g., what the user 102 is talking about (relationship, education, health, money, politics, sports, etc.); entity, e.g., who the user 102 is talking about (self, family member, coworker, friend, etc.); sentiment, i.e., whether the tone of the user 102's message is positive, negative or neutral; emotion, e.g., how the user 102 is feeling (sad, angry, happy, frustrated, scared, etc.); among other things the user 102 might mention (activities, medicalized terms, events, etc.).” Examiner’s note, the AI model detects who is the user talking about whether self or someone else, that is corresponding to identifying the user.);
and receive one or more of characteristic data and historical learning data associated with the user (Aggarwal, [Par.0048], “The AI models may be trained on training data obtained from multiple sources, such as user messages from conversations in the past with any Personal Identifiable Information (PII) replaced by synthetic data, manually created examples (i.e., synthetic statements manually created by data scientists based on the expected response of the user 102), third-party public datasets, e.g., Kaggle, and Web scraping, e.g., from Reddit, Twitter by Twitter, Inc, news websites, etc.”);
an executable portion configured to determine, using the one or more machine learning models, at least one of the one or more segments to present to the user based at least on the one or more of characteristic data and historical data of the user(Aggarwal, [Par.0049], “In some embodiments, the open-ended prompts or the closed-ended prompts are generated based on predefined base prompts written by conversation designers. These base prompts are parameterized with the user's context to personalize them. For example, an open-ended prompt could comprise a context-based empathetic statement followed by a predefined base prompt like. “How did (context) make you feel?”. Thus, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement based on this context like “I understand things are not going well with your colleague at the moment.”, followed by the contextualized base prompt like “How did this conflict make you feel?”. In some embodiments, the predefined base prompts are stored in the server 108.” Examiner’s note, the AI model determines a response prompt to present to the user based on a previously responded from the user.) ;
an executable portion configured to present the at least one determined segment to the user (Aggarwal, [Par.0021], “The method includes generating, using an AI model, a second prompt based on the at least one contextual feature includes at least one of the emotions, the medicalized terms or the domain.” Examiner’s note, the AI chatbot determines the second response to present to the user, as the example in [Par.0073], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102.”.);;
an executable portion configured to conduct a series of conversational interactions with the user using the one or more machine learning models (Aggarwal, [Par.00073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110. [0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”. The user 102 may respond with 312 to the open-ended prompt 310 provided by the AI chatbot 110, e.g., “I'm also a little lonely because my mother is scared of COVID.” The emotion detecting AI model 206 detects the emotion, e.g., “lonely” of the user 102. The conversational alignment monitoring module 212 increases the conversational alignment score by 5 to a total of 10 because the user shared further context.” Examiner’s note, the AI model continuously monitors previous user’s response to determine the next segment to present to the user.);
an executable portion configured to assess a level of competency of the user in the at least one subject matter associated with the at least one segment presented using the one or more machine learning models and based, at least, on the series of conversation interactions with the user (Aggarwal, [Par,0049-0051], “In some embodiments, the open-ended prompts or the closed-ended prompts are generated based on predefined base prompts written by conversation designers. These base prompts are parameterized with the user's context to personalize them. For example, an open-ended prompt could comprise a context-based empathetic statement followed by a predefined base prompt like. “How did (context) make you feel?”. Thus, if the user 102 talks about a conflict with a colleague, the open-ended prompt would comprise an empathetic statement based on this context like “I understand things are not going well with your colleague at the moment.”, followed by the contextualized base prompt like “How did this conflict make you feel?”. In some embodiments, the predefined base prompts are stored in the server 108. In some embodiments, the open-ended prompts or the closed-ended prompts are provided alternately to the user 102 based on a conversational alignment score. [0050] The AI model 112 continuously monitors the conversation between the user 102 and the AI chatbot 110 to determine if the conversation between the user 102 and the AI chatbot 110 has conversational alignment. The conversational alignment is an alignment with respect to the user 102 sharing more context or agreeing with the AI chatbot 110 when prompted by the AI chatbot 110. For example, if the AI chatbot 110 provides the open-ended prompt, (e.g., “What bothered you the most about this situation?”) or the closed-ended prompt (e.g., “Do you sometimes wish that you had more control over how others act?”), the user 102 may share more (e.g., “That I am not considered important by my family”) or agree with the chatbot 110 (e.g., “Yes, I think so.”), thus indicating conversational alignment. In some embodiments, the AI model 112 determines a misalignment by the user's confusion, (e.g., “What do you mean by that?”), dissatisfaction (e.g., “You don't understand me!”), disagreement (e.g., “No, that's not what I meant”), lack of trust (e.g., “A bot can't help me”), or uncertainty (e.g., “I'm not sure”). [0051] The AI model 112 maintains the conversational alignment score, which indicates the strength of the alliance formed between the AI chatbot 110 and the user 102. The conversational alignment score is updated after each message exchange based on the conversation of the user 102 with the AI chatbot 110.” Examiner’s note, Using the AI model to determine the conversation alignment, wherein the conversation alignment indicates the strength of the conversation between the user and AI chatbot, therefore, level of competency is considered as the conversation alignment, wherein the conversation alignment associated with the at least one presented segment.).
Regarding claims 17-19 are rejected for the same reason as claims 2-4, since these claims recite the same limitations.
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 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.
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 5, 6, 15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (Pub. No. US20240202284– hereinafter, Aggarwal) in view of Duan et al. (Pub. No. US20140122619– hereinafter, Duan).
Regarding claim 5, Aggarwal teaches the system of claim 1, wherein the learning application is configured to present the at least one determined segment to the user in a pop-up window (Aggarwal, [Par.0073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”.”)
However, Aggarwal does not teach present the at least one determined segment to the user in a pop-up window while the user is both actively engaged with a secondary application and not currently engaged with the learning application.
On the other hand, Duan teaches while the user is both actively engaged with a secondary application and not currently engaged with the learning application (Duan, [Par.0137], “A chat session is initialized when user 102 enters a first message. During the chat session, chat history namely three previous pairs of input/output messages are displayed while the user visits other web pages. If user 102 remains inactive for over 30 minutes, the chat session expires. If user 102 visits a different web page when the session expires, chatbot system 108 clears the chat history in the chat box and displays the initial message ("What can I do for you today?") as if the user were new. When displaying chat logs, chatbot system 108 organizes all messages by session (typically a session ID)”).
Aggarwal and Duan are analogous in arts because they have the same field of endeavor of generating the AI chat bot.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the learning application is configured to present the at least one determined segment to the user in a pop-up window, as taught by Aggarwal, to includes the least one determined segment to the user in a pop-up window while the user is both actively engaged with a secondary application and not currently engaged with the learning application, as taught by Duan. The modification would have been obvious because one of the ordinary skills in art would be motivated to present the chat window while the user are visiting other application/webpage, (Duan, [Par.0137], “A chat session is initialized when user 102 enters a first message. During the chat session, chat history namely three previous pairs of input/output messages are displayed while the user visits other web pages. If user 102 remains inactive for over 30 minutes, the chat session expires. If user 102 visits a different web page when the session expires, chatbot system 108 clears the chat history in the chat box and displays the initial message ("What can I do for you today?") as if the user were new. When displaying chat logs, chatbot system 108 organizes all messages by session (typically a session ID)”.).
Regarding to claim 6, Aggarwal teaches the system of claim 5, wherein the learning application is configured to assess the level of competency of the user in the at least one subject matter associated with the at least one presented segment via the pop-up window (Aggarwal, [Par.0073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110.[0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”” Examiner’s note, the AI model will generate the next response to the user based on an extracted topic from the user’s response, wherein, each of user’s response is associated with the conversational alignment score.).
Regarding claim 15 is rejected for the same reason as claim 5, since these claims recites the same limitations.
Regarding claim 20 is rejected for the same reason as claim 5, since these claims recites the same limitations.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (Pub. No. US20240202284– hereinafter, Aggarwal) in view of Duan et al. (Pub. No. US20140122619– hereinafter, Duan) and further in view of BHARWAJ et al. (Pub. No. US 20220247700– hereinafter, BHARWAJ).
Regarding claim 7, Aggarwal teaches the system of claim 6, wherein the artificial intelligence system triggers the learning application to present at least one of the one or more segments when at least one subject matter that the at least one segment window (Aggarwal, [Par.0073-0074], “In some embodiments, the AI chatbot 110 is a digital assistant for mental health. FIGS. 3A-3B are mock-up screenshots of user interfaces that illustrate a conversation between the AI chatbot 110 and the user 102 without misalignments according to some embodiments herein. In FIG. 3A of a user interface 300, the AI chatbot 110 starts a conversation with the user 102 with an empathetic statement 302, e.g., “Alex, it's nice to see you! How's your day going so far?”. The user 102 may respond with 304 to the empathetic statement 302 provided by the AI chatbot 110, e.g., “Don't even ask.” The sentiment detecting module 202 detects sentiment of the user's response 304. Based on the sentiment detected by the sentiment detecting module 202 (negative in this case), the prompt generating module 211 provides an open-ended prompt 306, e.g., “Today sounds like one of the days when you could use all the support. What happened, Alex?” to the user 102. The user 102 may respond with 308 to the open-ended prompt 306, e.g., “I'm sick. Down with covid”. The emotion detecting AI model 206 detects emotion, e.g., ‘unwell’ of the user 102 and the medicalized term detecting AI model 208 detects a medicalized term, e.g., ‘covid’. The conversational alignment monitoring module 212 of the AI model 112 increases the conversational alignment score for this conversation by 5 as the user 102 shared some context with the AI chatbot 110.[0074] The prompt generating module 211 provides an empathetic open-ended prompt 310 to the user 102 through the AI chatbot 110 based on the medicalized term, e.g., “I can imagine things are harder with covid. I understand how uncertainty can add on to the stress. Things may seem out of control but right now it's important to stay safe and aware. This too shall pass. Tell me more about this feeling”” Examiner’s note, the AI model will generate the next response to present to the user based on an extracted topic from the user’s response, for example if the user is talking about a medical issue then the chat bot will generate the present relates to the medical issue.).
However, Aggarwal does not teach that the at least one segment is associated with is related to content in interactions of the user with the secondary application or to information presented in the secondary application.
On the other hand, BHARDWAJ teaches that the at least one segment is associated with is related to content in interactions of the user with the secondary application or to information presented in the secondary application (BHARDWAJ, [Abstract, “Provided are systems and methods of a chatbot that can response on behalf of a first user during a three-way chat communication including multiple users and the chatbot. In one example, the method may include receiving a chat message transmitted from a first user device to a second user device, determining, via a machine learning model, an intent of the received chat message, detecting a suggested response based on the determined intent and external data, determining a confidence value for the suggested response based on a source of the suggested response, and outputting the suggested response when the confidence value is above a predetermined threshold via a chatbot of the chat application that is displayed within a user interface of the chat application on the second user device.” Examiner’s note, the chatbot window can display the response to the user based on an intent of the message is received from the other user.).
Aggarwal, Duan and BHARDWAJ are analogous in arts because they have the same field of endeavor of generating the AI chat bot.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to have modified the wherein the artificial intelligence system triggers the learning application to present at least one of the one or more segments when at least one subject matter that the at least one segment window, as taught by Aggarwal, to includes that the at least one segment is associated with is related to content in interactions of the user with the secondary application or to information presented in the secondary application, as taught by BHARDWAJ. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve display response is related to other application, (BHARDWAJ, [abstract], “In one example, the method may include receiving a chat message transmitted from a first user device to a second user device, determining, via a machine learning model, an intent of the received chat message, detecting a suggested response based on the determined intent and external data, determining a confidence value for the suggested response based on a source of the suggested response, and outputting the suggested response when the confidence value is above a predetermined threshold via a chatbot of the chat application that is displayed within a user interface of the chat application on the second user device.”).
Conclusion
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/E.T./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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