DETAILED ACTION
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the Amendment filed on 26 February 2026, has been entered.
Status of the Claims
The pending claims in the present application are claims 1-20 of the Amendment.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 16 September 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner. The IDSs of 09 December 2025 and 11 March 2026 also are in compliance, and thus, are being considered.
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. The steps of the multi-step eligibility analysis (outlined in MPEP 2106) resulting in the rejection of claims 1-20 are discussed in the paragraphs below.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “computing system” of claims 1-11 constitutes a machine under 35 USC 101, the “computer-implemented method” of claims 12-19 constitutes a process under the statute, and the “computer-readable storage medium storing non-transitory computer-readable instructions” of claim 20 constitutes a manufacture under the statute. Accordingly, claims 1-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“... for training employees interactively and effectively ..., comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... present a question ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... receive ... a textual input including an answer associated with the question; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate ... values associated with one or more metrics of the answer; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate ... a textual explanation associated with the answer based upon the values associated with the one or more metrics of the answer; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... present, in response to receiving the textual input, the textual explanation ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: managing personal behavior or relationships or interactions between people, associated with teaching or training individuals, including employees, which falls under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “present” and “receive” steps), and evaluation, judgment, and/or opinion (e.g., the recited “generate” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “training” is performed by a “computing system” and involves “a chatbot implementing a trained machine learning (ML) model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “computing system” includes “one or more processors, and a non-transitory memory storing one or more instructions, the instructions, when executed by the one or more processors, cause the one or more processors to” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “present” is “in a chatbot interface” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “receive” is “via the chatbot interface” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generate” is “via the chatbot” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generate” is “via the chatbot” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “present” is “in the chatbot interface” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... wherein training the ML model includes: creating a first set of vectors associated with first training data; training the ML model in a first stage using the first set of vectors; creating a second set of vectors associated with second training data, wherein the second training data includes objects comprising questions, answers associated with the questions, values associated with metrics of the answers, and/or prompts for evaluating the answers; and training the ML model in a second stage using the second set of vectors.” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, delivering broadcast content to a portable electronic device such as a cellular telephone (when claimed at a high level of generality), and selecting a particular generic function for computer hardware to perform (e.g., buffering content) from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify credit card transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, e.g., using the Internet to gather data, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-11, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “select a second question from a plurality of questions based upon the values associated with the one or more metrics of the answer; and present ... an indication of the second question” of claim 2, the “generate ... a second question based upon the values associated with the one or more metrics of the answer; and present ... the second question” of claim 3, the “wherein a detail level of the textual explanation is based upon the values associated with the one or more metrics of the answer” of claim 4, the “wherein to generate the values associated with the one or more metrics of the answer, ... generate values associated with the one or more metrics corresponding to a plurality of sub-answers of the answer” of claim 5, the “wherein to present the textual explanation, ... generate ... the textual explanation associated with the question and the answer, detail levels of a plurality of aspects of the textual explanation based upon the values associated with the one or more metrics corresponding to the plurality of sub-answers of the answer; and present ... the textual explanation” of claim 6, the “wherein the one or more metrics of the answer includes: an accuracy rate of the answer, a thoroughness of the answer, a time of completing the answer, an efficiency of finding references for the answer, and/or a sentiment of the answer” of claim 7, the “textual explanations associated with the values associated with the metrics of the answers” of claim 8, the “extracting text from documents; splitting the text into semantic clusters” of claim 9, and the “wherein at least one of the semantic clusters is one or more words, a portion of a word, or a character” of claim 11). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “computing system ..., wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: ... via the chatbot interface” of claim 2, the “computing system ..., wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: ... via the chatbot ... via the chatbot interface” of claim 3, the “computing system” of claim 4, the “computing system ... the instructions, when executed by the one or more processors, further cause the one or more processors to” of claim 5, the “computing system ... the instructions, when executed by the one or more processors, further cause the one or more processors to: ... via the chatbot ... via the chatbot interface” of claim 6, the “computing system” of claim 7, the “computing system ... wherein the objects in the second training data” of claim 8, the “computing system ... wherein creating the first set of vectors associated with the first training data includes: ... encoding the semantic clusters as the first set of vectors, wherein a distance between the vectors depends on a relevance between the semantic clusters corresponding to the vectors” of claim 9, the “computing system ... wherein the ML model is a first ML model, and encoding the semantic clusters as input vectors is further via a second ML model comprising a plurality of parameters, the ML model being trained with articles comprising a plurality of semantic clusters, the plurality of parameters being iteratively updated during training” of claim 10, and the “computing system” of claim 11). Accordingly, claims 2-11 also are rejected as ineligible under 35 USC 101.
Regarding claims 12-19, while the claims are of different scope relative to claims 1-6, 8, and 9, the claims recite limitations similar to the limitations of claims 1-6, 8, and 9. As such, the rejection rationales applied to reject claims 1-6, 8, and 9 also apply for purposes of rejecting claims 12-19. Claims 12-19 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding claim 20, while the claim is of different scope relative to claims 1 and 12, the claim recites limitations similar to the limitations of claims 1 and 12. As such, the rejection rationales applied to reject claims 1 and 12 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-12, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over KR Pat. App. Pub. No. 20230018065 A to Kim et al. (hereinafter referred to as “Kim”), in view of U.S. Pat. App. Pub. No. 2018/0150739 A1 to Wu (hereinafter referred to as “Wu”), and further in view of U.S. Pat. App. Pub. No. 2018/0090137 A1 to Horling et al. (hereinafter referred to as “Horling”).
Regarding claim 1, Kim discloses the following limitations:
“A computing system for training employees interactively and effectively using a chatbot ..., the computing system comprising: ...” - Kim discloses, “The present invention relates to a foreign language education service method utilizing an AI-based chatbot which evaluates a foreign language conversation sentence input by a user in real time using a conversation service through an AI tutor” (English-language translation, p. 1.), “FIG. 1 shows an exemplary diagram for explaining a network environment according to an embodiment of the present invention, and the network environment of FIG. 1 includes a plurality of electronic devices 110, 120, 130, and 140, and a plurality of servers” (English-language translation, p. 3), and “FIG. 2 shows an exemplary view for explaining the internal configuration of the electronic device and server shown in FIG. 1, and in FIG. 2, a first electronic device 110 as an example of one electronic device that is a terminal owned by a user. ), and the internal configuration of the server 150 will be described as an example for one server communicating with the user's terminal” (English-language translation, p. 4). The network environment and computing hardware for foreign language education using the AI-based chatbot and AI tutor conversing with the user, in Kim, reads on the recited limitation.
“... one or more processors, and ...” - Kim discloses “processors 212” (English-language translation, p. 4).
“... a non-transitory memory storing one or more instructions, the instructions, when executed by the one or more processors, cause the one or more processors to: ...” - Kim discloses, “Instructions may be provided to processors 212 and 222 by memories 211 and 221 or communication modules 213 and 223. For example, the processors 212 and 222 may be configured to execute instructions received according to program codes stored in a recording device such as the memories 211 and 221” (English-language translation, p. 4).
“... present a question in a chatbot interface; ...” - Kim discloses, “The input/output interface 214 may be a means for interface with the input/output device 215 . For example, the input device may include a device such as a keyboard or mouse, and the output device may include a device such as a display for displaying a communication session of an application. As another example, the input/output interface 214 may be a means for interface with a device in which functions for input and output are integrated into one, such as a touch screen. As a more specific example, the processor 212 of the first electronic device 110 uses data provided by the server 150 or the second electronic device 120 in processing the command of the computer program loaded into the memory 211. The service screen or content configured as above may be displayed on the display through the input/output interface 214” (English-language translation, p. 5), and “Referring to FIG. 6, taking a conversation between a user and a random artificial intelligence tutor as an example, in a conversation situation between an artificial intelligence tutor Rozy and a user, when Rozy asks the questions ‘Are you hungry?’” (English-language translation, p. 7). The presenting of the question from the artificial intelligence tutor, via the input/output interface, in Kim, reads on the recited limitation.
“... receive, via the chatbot interface, textual input including an answer associated with the question; ...” - See the aspects of Kim that have been cited above. Kim also discloses, “when Rozy asks the question ‘Are you hungry?’, the user responds with text and A foreign language dialogue sentence 404 ‘I’m much hungry’” (English-language translation, p. 7). Receiving, via the input/output interface, the user’s text response to the artificial intelligence tutor’s question, in Kim, reads on the recited limitation.
“... generate, via the chatbot, values associated with one or more metrics of the answer; ...” - See the aspects of Kim that have been cited above. Kim also discloses, “For example, if the user inputs the conversational sentence 'I'm much hungry', Rozy evaluates the grammatical and fluency of the conversational sentence 'I'm much hungry' entered by the user, that is, the conversation topic, conversation situation, and conversation. Perform expressive evaluation appropriate to the flow. At this time, when the evaluation results for each of grammatical and fluency are evaluated as one star, two stars, and three stars out of five stars, the present invention 400 provides the aspect of the foreign language conversation sentence 404 input by the user in the chat window. A thumbs down emoticon 405 is provided, and if it is rated as four or five stars out of five stars, a thumbs up on the side of the foreign language conversation sentence 404 entered by the user in the chat window ) emoticons 406 may be provided” (English-language translation, p. 7). Generating, by the artificial intelligence tutor, numbers of stars, associated with grammar and fluency of the user’s response, in Kim, reads on the recited limitation.
The combination of Kim and Wu (hereinafter referred to as “Kim/Wu”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Kim:
The claimed “chatbot” is one “implementing a trained machine learning (ML) model” - Kim discloses, “an AI-based chatbot” (English-language translation, p. 1). Wu discloses, “The systems and method as disclosed herein are directed to an artificial intelligence (AI) interview chat bot” (para. [0069]), and “several of the systems and models of the chat bot 100 utilize learning algorithms and/or models for performing accurate analysis. The learning algorithms as utilized herein include deep learning, machine learning, and/or statistical modeling techniques. These models must be trained before use in order to build an effective interview chat bot 100” (para. [0146]). The AI-based chatbot, in Kim, including the machine learning and model training, in Wu, reads on the recited limitation.
“... wherein training the ML model includes: creating a first set of vectors associated with first training data; ...” - See the aspects of Wu that have been cited above. Wu also discloses, “In FIG. 17A, one training sample includes three elements: question; good answer; and bad answer. For example, a question of, ‘How to detect a loop in a linked list?’, a good response of ‘The idea is to have two references to the list and move them at different speeds. Move one forward by 1 node and the other by 2 nodes,’ and a bad response of ‘When we implement these operations with the disjoint-set data structure, the key idea is that every set is represented by a leader’ is listed in FIG. 17A. The embedding layer maps these input one-hot expressions into dense vector representations” (para. [0092]), and “When these matrices are obtained, the testing process can be then performed. Given a question and a corresponding candidate answer, we can go through the network to compute the similarity of the question and the candidate answer to obtain a similarity score. FIG. 17B illustrates a left-to-right expedition of the question sequence by using GRU formula shown in FIG. 17A for the forward process. FIG. 17C illustrates an output layer 1702, which takes the connection of the two vector (left-to-right and right-to-left) as input and compute the margin. The error (if the similarity of <q, a+> is small than the similarity of <q, a−>, the distance is taken as error) will be back propagated from output layer and then hidden layer and finally the embedding layer” (para. [0093]). Creating the vector representations associated with the questions and answers, for training the models, in Wu, reads on the recited limitation.
“... training the ML model in a first stage using the first set of vectors; ...” - See the aspects of Wu that have been cited above. Training the models using the vectors, in Wu, reads on the recited limitation.
“... creating a second set of vectors associated with second training data, wherein the second training data includes objects comprising questions, answers associated with the questions, values associated with metrics of the answers, and/or prompts for evaluating the answers; and ...” - See the aspects of Wu that have been cited above. Continual operation of the system to create vector representations based on questions and answers, for training the models, in Wu, reads on the recited limitation.
“... training the ML model in a second stage using the second set of vectors.” - See the aspects of Wu that have been cited above. Continual training of the models, in Wu, reads on the recited limitation.
Wu discloses, “Chat bots are designed to conduct a conversation with a user” (para. [0001]), similar to the claimed invention and to Kim. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the artificial intelligence tutor, of Kim, to include the vector and training aspects, of Wu, “for performing accurate analysis,” per Wu (para. [0146]).
The combination of Kim, Wu, and Horling (hereinafter referred to as “Kim/Wu/Horling”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Kim/Wu:
“... generate, via the chatbot, a textual explanation associated with the answer based upon the values associated with the one or more metrics of the answer; and ...” - See the aspects of Kim that have been cited above. Kim also discloses, “when the evaluation results for each of grammatical and fluency are evaluated as one start, two starts, and three stars out of five stars, the present invention 400 provides the aspect of the foreign language conversation sentence 404 input by the user in the chat window. A thumbs down emoticon 405 is provided, and if it is rated as four or five starts out of five stars, a thumbs up on the side of the foreign language conversation sentence 404 entered by the user in the chat window ) emoticons 406 may be provided” (English-language translation, p. 7). Generating, by the artificial intelligence tutor, emoticons, evaluation results, corrected dialogue, and other feedback, associated with the user’s response, based on the number of stars given to the user’s response, in Kim, reads on the recited “generate, via the chatbot, a ... explanation associated with the answer based upon the values associated with the one or more metrics of the answer” limitation. Horling discloses, “if a chatbot (e.g., 56) determines during a first session with a user that the user has a particular sentiment, the chatbot may output a statement during a subsequent session, such as the next session, that corresponds to the prior sentiment. For example, if a user expresses a negative sentiment during a first session, the chatbot may select and output one of the four statements in the top group of statements. As noted above, in other implementations, the chatbot may form (e.g., assemble) statements to output from a plurality of candidate words, phrases, and/or statements. Additionally or alternatively, in some implementations, the chatbot may select and output one or more images, symbols, and/or ideograms (such as one or more so-called “emojis”) to convey empathy or otherwise respond to a user's expressed sentiment” (para. [0058]). The use of statements and emojis, or in place of emojis, in Horling, reads on the recited “textual” limitation.
“... present, in response to receiving the textual input, the textual explanation in the chatbot interface, ...” - See the aspects of Kim and Horling that have been cited above. Displaying the feedback regarding the text answer via the input/output interface, including the chat window, in Kim, wherein the feedback includes emojis and statements, or just statements, as in Horling, reads on the recited limitation.
Horling discloses “tailoring chatbot output” (para. [0002]), similar to the claimed invention and to Kim and Wu. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the use of emoticon feedback, of Kim, to include statements (in addition to, or in place of, the emojis), as in Horling, as such forms of expression are art-recognized equivalents, per Horling (para. [0058]), and to “elicit positive responses,” per Horling (para. [0060]).
Regarding claim 4, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 1, wherein a detail level of the textual explanation is based upon the values associated with the one or more metrics of the answer.” - See the aspects of Kim and Horling that have been cited above. Kim also discloses, “FIG. 9(a) shows a case where an evaluation of 4 stars out of 5 stars is received. If the fluency is 4 stars or more, examples are not provided and only grammatically corrected dialogue sentences can be provided. 9(b) shows a case where a five-star evaluation was received out of five stars, and in this case, neither an example in terms of fluency nor corrected feedback in terms of grammar is provided, and only visual evaluation results can be provided” (English-language translation, p. 7), and “in the evaluation providing unit 1320, the evaluation grade is evaluated as one star, two stars, and three stars out of five stars, and a thumbs down emoticon is provided on the side of the foreign language conversation sentence input by the user In this case, the conversation feedback unit 1330 is a grammatically correctly corrected conversation sentence for the foreign language conversation sentence input by the user, and an example of a conversation expression that is more expressively (or fluency) appropriate for the conversation topic, conversation situation, and conversation flow. can suggest” (English-language translation, p. 10). The providing of feedback based on the evaluation grade given to the answer, indicative of quality of the answer, in Kim, wherein the feedback includes statements, as in Horling, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 4.
Regarding claim 5, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 1, wherein to generate the values associated with the one or more metrics of the answer, the instructions, when executed by the one or more processors, further cause the one or more processors to: generate values associated with the one or more metrics corresponding to a plurality of sub-answers of the answer.” - See the aspects of Kim that have been cited above. Kim also discloses, “the evaluation of the foreign language learning progress according to the conversation service can be provided by designating numerical values and ratings on a daily, weekly, and monthly basis. For example, the conversation between the user and the artificial intelligence tutor may be evaluated daily, weekly, monthly, or yearly, and the average score based on the response rate to the tutor's questions and the evaluation grade of foreign language conversation sentences may be quantified and provided” (English-language translation, p. 6). Designating numerical values and ratings for answers in conversations, via execution of instructions by the processor, causing the processor to average individual scores, in Kim, reads on the recited limitation.
Regarding claim 6, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 5, wherein to present the textual explanation, the instructions, when executed by the one or more processors, further cause the one or more processors to: generate, via the chatbot, the textual explanation associated with the question and the answer, detail levels of a plurality of aspects of the textual explanation based upon the values associated with the one or more metrics corresponding to the plurality of sub-answers of the answer; and ...” - See the aspects of Kim and Horling that have been cited above. The processor executing the instructions to generate, via the artificial intelligence tutor, the evaluations and other feedback associated with the questions and the user’s responses, forms of the evaluations and feedback of lesser or greater information based on the ratings of the user’s responses averaged over periods of time, in Kim, wherein the evaluations and feedback include statements, as in Horling, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 6.
“... present, via the chatbot interface, the textual explanation.” - See the aspects of Kim that have been cited above. Presenting, via the chat window of the input/output interface, the evaluations and feedback, in Kim, including the statements, in Horling, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 6.
Regarding claim 7, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 1, wherein the one or more metrics of the answer includes: an accuracy rate of the answer, a thoroughness of the answer, a time of completing the answer, an efficiency of finding references for the answer, and/or a sentiment of the answer.” - See the aspects of Kim that have been cited above. The ratings and emoticons for the user responses indicating grammatical correctness, and/or indicating fluency, in Kim, reads on the recited limitation. For example, the “thoroughness” alternative.
Regarding claim 8, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 1, wherein the objects in the second training data further include textual explanations associated with the values associated with the metrics of the answers.” - See the aspects of Kim, Wu, and Horling that have been cited above. Elements of training samples, in Wu, read on the recited “wherein the objects in the second training data further include” limitation. The evaluations and feedback having different forms based on the ratings of the user responses, in Kim, wherein the feedback includes statements, as in Horling, reads on the recited “textual explanations associated with the values associated with the metrics of the answers” limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 8.
Regarding claim 9, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 1, wherein creating the first set of vectors associated with the first training data includes: ...” - See the aspects of Wu that have been cited above. Generating the dense vector representations, and/or other vectors, in Wu, reads on the recited limitation.
“... extracting text from documents; ...” - See the aspects of Wu that have been cited above. Wu also discloses, “Word ngrams: unigrams and bigrams for words in the text input” (para. [0102]), “Character ngrams: for each word in the text, character ngrams are extracted” (para. [0103]), and “the question and answer generation system 124 may searches textbooks, articles, and/or coding websites for the desired computer science technical knowledge within the world knowledge. Once the technical knowledge 128 has been identified, the question and answer generation system 124 parses sentences from the technical knowledge with a trained sentence parser utilizing a syntactic dependency tree to form parsed sentences” (para. [0133]).
“... splitting the text into semantic clusters; and ...” - See the aspects of Wu that have been cited above. Wu also discloses, “Word2vec cluster ngrams: Word2vec tool may be utilized to learn 100-dimensional word embedding from a social network dataset, next a K-means algorithm and L2 distance of word vectors is employed to cluster the million-level vocabulary into 200 classes that represent generalized words in the text” (para. [0109]).
“... encoding the semantic clusters as the first set of vectors, wherein a distance between the vectors depends on a relevance between the semantic clusters corresponding to the vectors.” - See the aspects of Wu that have been cited above. Operation of the Word2vc tool, including determining distances of word vectors for clustering vocabulary into classes, in Wu, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of independent claim 1, also apply to this rejection of claim 9.
Regarding claim 10, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 9, wherein the ML model is a first ML model, and encoding the semantic clusters as input vectors is further via a second ML model comprising a plurality of parameters, the second ML model being trained with articles comprising a plurality of semantic clusters, the plurality of parameters being iteratively updated during training.” - See the aspects of Wu that have been cited above. Wu also discloses, “A multiple class support vector machine (SVM) model is trained utilizing these features to determine the sentiment of user answers” (para. [0109]), and “several of the systems and models of the chat bot 100 utilize learning algorithms and/or models for performing accurate analysis. The learning algorithms as utilized herein include deep learning, machine learning, and/or statistical modeling techniques. These models must be trained before use in order to build an effective interview chat bot 100” (para. [0146]). Clustering the vocabulary into classes as word vectors using machine learning algorithms trained on new training data including textbooks, in Wu, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 9.
Regarding claim 11, Kim/Wu/Horling teaches the following limitations:
“The computing system of claim 9, wherein at least one of the semantic clusters is one or more words, a portion of a word, or a character.” - See the aspects of Wu that have been cited above. The clustering of vocabulary into classes representing words in text, and using word or character ngrams, in Wu (see paras. [0102] and [0103]), reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 9.
Regarding claims 12 and 15-19, while the claims are of different scope relative to claims 1, 4-6, 8, and 9, the claims recite limitations similar to those recited by claims 1, 4-6, 8, and 9. As such, the rationales used to reject claims 1, 4-6, 8, and 9 also apply for purposes of rejecting claims 12 and 15-19. Claims 12 and 15-19 are, therefore, also rejected under 35 USC 103 as obvious in view of Kim/Wu/Horling.
Regarding independent claim 20, while the claim is of different scope relative to independent claims 1 and 12, the claim recites limitations similar to those recited by claims 1 and 12. As such, the rationales applied to reject claims 1 and 12 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected under 35 USC 103 as obvious in view of Kim/Wu/Horling.
Claims 2, 3, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Wu, further in view of Horling, and further in view of U.S. Pat. App. Pub. No. 2020/0258045 A1 to Knupfer (hereinafter referred to as “Knupfer”).
Regarding claim 2, the combination of Kim, Wu, Horling, and Knupfer (hereinafter referred to as “Kim/Wu/Horling/Knupfer”) teaches limitations below that do not appear to taught in their entirety by Kim/Wu/Horling:
“The computing system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: select a second question from a plurality of questions based upon the values associated with the one or more metrics of the answer; and ...” - See the aspects of Kim that have been cited above. The computer hardware performing continued questioning, in Kim, reads on the recited “wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: select a second question from a plurality of questions” limitation. Knupfer discloses, “Thus, if a user continues to respond well or poorly to the challenges, the challenges 84 presented to the user will increase or decrease in difficulty” (para. [0090]). Increasing or decreasing the difficulty of challenges based on user responses, in Knupfer, when applied in the context of the preparing questions and evaluating responses, in Kim, reads on the recited “based upon the values associated with the one or more metrics of the answer” limitation.
“... present, via the chatbot interface, an indication of the second question.” - See the aspects of Kim that have been cited above. Displaying, via the chat window of the input/output interface, additional questions, in Kim, reads on the recited limitation.
Knupfer discloses assessing skill and trait levels (see title), similar to the claimed invention and to Kim. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the question/response/evaluation process, of Kim/Wu/Horling, to include the increasing or decreasing of difficulty based on the quality of responses, as in Knupfer, so the process is responsive to updated user skill levels, per Knupfer (see paras. [0089] and [0090]).
Regarding claim 3, Kim/Wu/Horling/Knupfer teaches the following limitations:
“The computing system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: generate, via the chatbot, a second question based upon the values associated with the one or more metrics of the answer; and ...” - See the aspects of Kim and Knupfer that have been cited above. The hardware generating, via the artificial intelligence tutor, additional questions, based on the evaluations and feedback (including stars, emoticons, and the like), in Kim, with increasing or decreasing difficulty based on how well or poorly the users respond, per Knupfer, reads on the recited limitation.
“... present, via the chatbot interface, the second question.” - See the aspects of Kim that have been cited above. Displaying, via the chat window of the input/output interface, additional questions, in Kim, reads on the recited limitation.
Regarding claims 13 and 14, while the claims are of different scope relative to claims 2 and 3, the claims recite limitations similar to those recited by claims 2 and 3. As such, the rationales applied to reject claims 13 and 14 also apply for purposes of rejecting claims 2 and 3. Claims 13 and 14 are, therefore, also rejected under 35 USC 103 as obvious in view of Kim/Wu/Horling/Knupfer.
Response to Arguments
On pp. 9-12 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejections under 35 USC 103. The applicant’s arguments have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
On pp. 12-15 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. The applicant argues that “claim 1 provides an improvement of the function of a computer by improving a machine learning (ML) chatbot’s capability of analyzing answers to questions by training the chatbot in two stages,” pointing to Ex Parte Desjardins. (Amendment, pp. 12 and 13.) The applicant also argues that the two stages result in improving the chatbot’s capabilities of analyzing answers to questions, providing analysis results in a human-like manner, to interact with people more effectively. (Amendment, p. 13.) The applicant also argues that the aspects above are reflected in the claims. (Amendment, p. 13.) The applicant also argues that the claims (e.g., claim 9) reflects improved encoding techniques resulting in an improvement to how the trained ML model is trained. (Amendment, pp. 14 and 15.) The examiner finds the arguments unpersuasive. Eligibility in Desjardins is based on the claims in Desjardins reflecting an improvement to how training of machine learning is performed. While the applicant’s claims reflect training of machine learning, it is not clear whether the claimed training steps are an improvement to the training of machine learning, or merely a way in which training of machine learning is performed. All machine learning models undergo training. Training machine learning models is not automatically an improvement to machine learning, or to training of machine learning. If the applicant’s claimed invention is an improvement to machine learning or to the training of machine learning, there must be specification support for such a contention. (MPEP 2106.05(a).) That examiner has not found the specification support for the contention. Rather, the specification merely describes a way in which machine learning training is performed. Similar arguments apply to the applicant’s arguments about the encoding of claim 9. For at least these reasons, the ineligibility rejection is maintained.
Conclusion
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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