DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims in application 18/388,452 filed on 3/5/2026 .
The Pre-Grant publication #US20250157351 is published on 5/15/2025.
Claims 1-20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/5/2026 has been entered.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention teaches a processing and computer devices. Thus falls within one of the four statutory categories (Step 1: YES).
Claims 1, 10 and 19 are directed to a computer-implemented method and system where a plurality of answers are obtained by different artificial intelligence (Al) chatbots to questions presented. The answer is analyzed in order to determine updated first domain expertise scores of the Al chatbots. An answer that selected is provided to a first user device based on the updated first domain expertise scores. All these steps involved is drawn to a concept categorized as an actions that are receiving, obtaining, identifying, evaluating and judging of inputs and outputs. A concept that are mental processes and by including updating domain expertise score generating feedback processing of Chatbot question and answers is like organizing of certain human activities. The analysis by machine-learned model and analyzing of answer generated by different artificial intelligence (Al) chatbots or determination updating first domain expertise scores of Al chatbots could also be categorized as a use of mathematical calculations within some mathematical concepts They are generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, "network remote storage", "databases of digital content with predetermined AI chat box “,”updated first domain expertise scores of Al chatbots”,” engaging natural language conversation applications” ” are merely use of generic computer implemented functions and disclosed in the Applicant’s specification (Paragraphs 0001, 0002) in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) .” MPEP 2106.07(a)(III)(A). Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art. Fig.1 of the instant specification indicates Hatbox interaction with the computer system communication and memory presenting answers to determine scores code with peripheral device set for a hardware/ software in a standard computing system. As an example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
The dependent claims 2-9,11-18 and 20 describe additional limitations that serve only to modify and further describe the abstract idea. The selection, domain groups, relevance-consistency, calculations, thresholds, outputting, feedback , chatbox question and variable sentence structure, scores , displaying of data and reviewing of activities are further description of elements not making abstract idea less abstract. Any improvement resulting from the claimed invention has nothing to do with the claimed computing devices, e.g., being able to run faster, use less power, and/or be manufactured more cheaply as a result of the invention. Instead, the improvement, if there is one, is in terms of the applicant’s particular method for collecting data, analyzing data, and providing an output based on that analysis. That would not be a patent eligible improvement and when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. § 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4,8,10,11-13,17,19,20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190199658 A1 to Kim et al.(Kim) in view of US 20190164016 A1 to Allen. et al. (Allen).
Claim 1. Kim teaches a computer-implemented method, comprising:
obtaining a plurality of answers to a chatbot question, wherein the answers are generated by different artificial intelligence (Al) chatbots (Para 0005-0007 answer generated by a plurality of different property and multiple chatbbots service providers as in fig.3A element 300 )
chatbots, wherein the different Al chatbots are hosted on different Al chatbot servers ( Fig.3A chatbots on possible different hosting servers as in Para 0004 ) ;
analyzing the answers to determine updated first domain expertise scores of the Al chatbots (Para 0008 ranking of answers a provider chatbot reply; Para 0071 analyzer analyzing the replies received from the provider chatbots through the conversation performed; and a purpose completion determiner determining whether or not the purpose of the user i.e. an updated first domain expertise has been completed by analyzing final replies from the provider chatbots; analyzer may include a means for analyzing whether or not each of the replies sent by the provider or a domain expertise chatbots, in response to the inquiry from the consumer chatbot, contains additional information requested by the provider domain chatbox);
selecting, based on the updated first domain expertise scores, one of the answers (Para 0072 select a chatbot for providing an answer to the user device based on the derived scores as in figure 3B); and
causing the selected answer to be provided to a first user device (Para 0072,0073 derived and ranked selected answer provided to the user device ).
wherein the causing the selected answer to be provided to the first user device includes: rendering the selected answer according to a predetermined sentence structure, wherein the rendering reduces an amount of data that would otherwise be transmitted to the first user device (Para 0072,0073 casing derived and ranked selected answer provided to server and user device; Text chat are in sentence structure as in Para 0003).
But Kim, though include different hosting servers, does not elaborate receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions directly while communicating with servers . Allen, however, teaches the receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions (Para 0014 provide valuable feedback to Subject matter experts in order to refine and improve the accuracy of their i.e. possible AI chatbot future candidate answer ratings).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions as taught by Allen, into the computer-implemented method of Kim, so that improved predictable answers are obtained to benefit the user.
{Note: A chatbot (originally chatterbot)[1] is a software application or web interface designed to have textual or spoken conversations.[2][3][4] Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner. Such chatbots often use deep learning and natural language processing, but simpler chatbots have existed for decades}.
Claim 2.Kim teaches the computer-implemented method of claim 1, comprising: prioritizing the answers in a list based on the updated first domain expertise scores, wherein the selected answer is selected based on having the relatively highest updated first domain expertise score in the list (Para 0072,0073 select a chatbot for providing an answer to the user device based on the derivation scores i.e. selection could be based on relatively highest score as in figure 3B);.
Claim 3.Kim teaches the computer-implemented method of claim 1, wherein each of the Al chatbots have a plurality of expertise scores for a plurality of different domains, wherein the plurality of different domains include the first domain (Para 0072 select a chatbot for providing an answer to the user device based on the derived scores as in figure 3A element 110, 210);.
Claim 4.Kim teaches the computer-implemented method of claim 3, wherein the domains are selected from the group consisting of: geography, language, mathematics, science and programming (Para 0060 domains of music, weather service etc. that may well include language, science and programming).
Claim 8.Kim teaches the computer-implemented method of claim 1, wherein causing the selected answer to be provided to the first user device includes: rendering the selected answer according to a predetermined sentence structure, and outputting the rendered selected answer to the first user device (Para 0072,0073 casing derived and ranked selected answer provided to server and user device; Text chat are in sentence structure as in Para 0003).
Claim 10. Kim teaches a computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith (Para 0084 program module) , the program instructions readable and/or executable by a computer to cause the computer to:
obtain a plurality of answers to a chatbot question, wherein the answers are generated by different artificial intelligence (Al) chatbots ((Para 0005-0007 answer generated by a plurality of different property and multiple chatbbots service providers as in fig.3A element 300);
analyze the answers to determine updated first domain expertise scores of the Al chatbot Para 0008 ranking of answers a provider chatbot reply; Para 0071 analyzing the replies received from the provider chatbots through the conversation performed; and a purpose completion determiner determining whether or not the purpose of the user i.e. an updated first domain expertise has been completed by analyzing final replies from the provider chatbots; analyzer may include a means for analyzing whether or not each of the replies sent by the provider or a domain expertise chatbots, in response to the inquiry from the consumer chatbot, contains additional information requested by the provider domain chatbot);
select, based on the updated first domain expertise scores, one of the answers (Fig.3A,3B domain expertise and evaluation); and
cause the selected answer to be provided to a first user device (Para 0072,0073 derived and ranked selected answer provided to the user device ).
wherein the causing the selected answer to be provided to the first user device includes: rendering the selected answer according to a predetermined sentence structure, wherein the rendering reduces an amount of data that would otherwise be transmitted to the first user device (Para 0072,0073 casing derived and ranked selected answer provided to server and user device; Text chat are in sentence structure as in Para 0003).
But Kim, though include different hosting servers, does not elaborate receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions directly while communicating with servers . Allen, however, teaches the receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions (Para 0014 provide valuable feedback to Subject matter experts in order to refine and improve the accuracy of their i.e. possible AI chatbot future candidate answer ratings).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions as taught by Allen, into the computer program product of Kim, so that improved predictable answers are obtained to benefit the user.
Claim 11.Kim teaches the computer program product of claim 10, the program instructions readable and/or executable by the computer (Para 0084 program module and readable instruction storage) to cause the computer to: prioritize the answers in a list based on the updated first domain expertise scores, wherein the selected answer is selected based on having the relatively highest updated first domain expertise score in the list (Para 0072,0073 select a chatbot for providing an answer to the user device based on the derivation scores i.e. selection be based on relatively highest as in figure 3B ).
Claim 12.Kim teaches the computer program product of claim 10, wherein each of the Al chatbots have a plurality of expertise scores for a plurality of different domains, wherein the plurality of different domains include the first domain (Para 0071-0073 derived expertise scores and domains).
Claim 13.Kim teaches the computer program product of claim 12, wherein the domains are selected from the group consisting of: geography, language, mathematics, science and programming (Para 0060 domains of music, weather service etc. could easily be consist of language, science and programming).
Claim 17.Kim teaches the computer program product of claim 10, wherein causing the selected answer to be provided to the first user device includes: rendering the selected answer according to a predetermined sentence structure, and outputting the rendered selected answer to the first user device (Para 0072,0073 casing derived and ranked selected answer provided to server and user device; Text chat are in sentence structure as in Para 0003).
Claim 19. A system, comprising: a processor; and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor (Para 0005-0007 answer generated by a plurality of different property and multiple chatbbots service providers logic),the logic being configured to: obtain a plurality of answers to a chatbot question, wherein the answers are generated by different artificial intelligence (Al) chatbots; (Para 0008 ranking of answers a provider chatbot reply; Para 0071 analyzer analyzing the replies received from the provider chatbots through the conversation performed; and a purpose completion determiner determining whether or not the purpose of the user i.e. an updated first domain expertise has been completed by analyzing final replies from the provider chatbots; analyzer may include a means for analyzing whether or not each of the replies sent by the provider or a domain expertise chatbots, in response to the inquiry from the consumer chatbot, contains additional information requested by the provider domain chatbox) as in fig.3A element 300), analyze the answers to determine updated first domain expertise scores of the Al chatbots ( ) ; select, based on the updated first domain expertise scores, one of the answers ((Para 0072 select a chatbot for providing an answer to the user device based on the derived scores as in figure 3B) ); and cause the selected answer to be provided to a first user device( Para 0072 select a chatbot for providing an answer to the user device based on the derived scores as in figure 3A element 110, 210).
wherein the causing the selected answer to be provided to the first user device includes: rendering the selected answer according to a predetermined sentence structure, wherein the rendering reduces an amount of data that would otherwise be transmitted to the first user device (Para 0072,0073 casing derived and ranked selected answer provided to server and user device; Text chat are in sentence structure as in Para 0003).
Kim, though include different hosting servers, does not elaborate receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions directly while communicating with servers . Allen, however, teaches the receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions (Para 0014 provide valuable feedback to Subject matter experts in order to refine and improve the accuracy of their i.e. possible AI chatbot future candidate answer ratings).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate receiving and using user feedback to refine an accuracy of answers produced by the Al chatbots for subsequent chatbot questions as taught by Allen, into the computer-implemented method of Kim, so that improved predictable answers are obtained to benefit the user.
Claim 20.Kim teaches the system of claim 19, the logic being configured to: prioritize the answers in a list based on the updated first domain expertise scores, wherein the selected answer is selected based on having the relatively highest updated first domain expertise score in the list (Para 0072,0073 select a chatbot for providing an answer to the user device based on the derivation scores i.e. selection could be based on relatively highest as in figure 3B).
Claims 5-7, 9, 14-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number US 20190199658 A1 to Kim et al.(Kim) in view of US 20190164016 A1 to Allen. et al. (Allen) and further in view of US Patent Application Publication Number US 20180053100 A1 to Appel et al. (Appel).
Claim 5. Kim teaches the computer-implemented method of claim 1, comprising:
evaluating the chatbot question for determining a domain associated with the chatbot question, wherein the first domain is determined to be associated with the chatbot question during the evaluation (Para 0072 select a chatbot for providing an answer to the user device based on the derived scores as in figure 3A element 110, 210).
averaging, for each chatbot of a pool of candidate AI chatbots, current domain expertise scores associated with the plurality of domains ( Para 0071-0073 score calculations and probability determination from a pool of candidate AI chatbots could be based on averaging calculations)
But Kim does not elaborate selecting of an Al chatbots from a pool of candidate Al chatbots, wherein the Al chatbots are selected in response to a determination that the Al chatbots currently have first domain expertise scores that exceed a predetermined threshold. Appel however, teaches the selecting the Al chatbots from a pool of candidate Al chatbots, wherein the Al chatbots are selected in response to a determination that the Al chatbots currently have first domain expertise scores that exceed a predetermined threshold (subsequent user input is less than a predetermined threshold time for a determination that the Al chatbots currently have first domain expertise scores as in claim 9 of prior art Appel).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate selecting of Al chatbots from a pool of candidate Al chatbots, wherein the Al chatbots are selected in response to a determination that the Al chatbots currently have first domain expertise scores that exceed a predetermined threshold as taught by Appel, into the computer-implemented system of Kim, so that Improved AI response could be formulated.
Kim in combination further causing the chatbot question to be sent to servers associated with the selected Al chatbots (Para 0072,0073 casing derived and ranked selected answer provided to server and user device chatbots questions). wherein the selected AI chatbots are further selected from the pool of candidate AI chatbots based on the selected AI chatbots having relatively greater average current domain scores than the chatbots of the pool of candidate AI chatbots that are not selected (Para 0071 chatbot may derive scores of the multiple chatbots and select a chatbot for providing an answer to the user device 100 based on the derived score),
Kim in combination, however, does not explicitly selecting wherein the selected AI chatbots are further based on user preference information of a user that entered the chatbot question on the first user device and causing the chatbot question to be sent to the different servers associated with the selected AI chatbots. It would however institutive to selected AI chatbots further selected based on user preference information of a user that entered the chatbot question on the first user device; and causing the chatbot question to be sent to the different servers associated with the selected AI chatbots (Para 0072 derive scores based on the predicted probability values and preferences obtain from average of instant domain scores of each ChatBot or combination as determined by relay connections) to achieve optimum results.
Claim 6. Kim teaches the computer-implemented method of claim 1, wherein analyzing the answers to determine the updated first domain expertise scores of the Al chatbots that include calculating relevance-consistency values for each of the answers, wherein each relevance-consistency value indicates an extent of similarity that a given one of the answers has with the other answers (Para 0011 a receiving unit that receives, from a messenger server, a question message is relevantly consistent to a service selected from multiple services and service account information corresponding to the selected service) . But it does not elaborate wherein each relevance-consistency value is a difference of a predetermined threshold value and sum different answer value, wherein the sum different answer value characterizes, for a given one of the answers, a percentage of the answers that the given answer differs from.
Appel however, teaches the relevance-consistency value (Para 0029 expertise score depending on relevancy of user input timings depending on time) is a difference of a predetermined threshold value and sum different answer value (claim 9 subsequent user input is less than a predetermined threshold time as in claim of prior art Appel), wherein the sum different answer value characterizes, for a given one of the answers, a percentage of the answers that the given answer differs from ( Para 0034,0035 it would have been a matter of obvious design choice to one having ordinary skill in the art before the effective filing date of the claimed invention to make calculated expertise score of a user based on the analyzed user input and the processed background in which the user can adjust the score accordingly that could base on sum different answer value characterizing a percentage of the answers that the given answer differs).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate relevance-consistency value is a difference of a predetermined threshold value and sum different answer value wherein the sum different answer value characterizes, for a given one of the answers, a percentage of the answers that the given answer differs from as taught by Appel, into the computer-implemented system of Kim, so that relevance-consistency could be easily determined by simple mathematical calculations for different characterization of answer values.
Claim 7. Kim in combination with Appel teaches the computer-implemented method of claim 6, wherein the updated first domain expertise score of a given one of the Al chatbots is calculated as a sum of a first predetermined variable and a second predetermined variable, wherein the first predetermined variable is a current first domain expertise score of the given Al chatbot, wherein the second predetermined variable is a product of the current first domain expertise score of the given Al chatbot and the calculating relevance- consistency value of the given Al chatbot (Para 0011, 0012 it would have been a matter of obvious design choice to one having ordinary skill in the art before the effective filing date of the claimed invention to expertise score domain chatbots linked to the relay chatbot to determine a sum of a first predetermined variable and a second predetermined variable to derive the answer through at least one of the multiple chatbots to find an update domain expertise score).
Claim 9. Kim teaches the computer-implemented method of claim 8, but not comprising of feedback receiving feedback about the rendered selected answer. Appel however, teaches the Feedback receiving feedback about the rendered selected answer (0027 feedback ); determining whether the feedback is positive feedback (Para 0027,0027 expertise score is adjusted according to a feedback of the user for a response by a chat bot);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate feedback receiving feedback about the rendered selected answer (0027 feedback ); determining whether the feedback is positive feedback (Para 0027,0027 expertise score is adjusted according to a feedback of the user for a response by a chat bot); as taught by Appel, into the computer-implemented system of Kim, to fine tune some of the answer responses.
Kim also does not in response determine if feedback is not positive feedback, decreasing, a predetermined amount, the updated first domain expertise score of the Al chatbot that generated the selected answer. Appel, however, teaches that in response to a determination that the feedback is not positive feedback, decreasing, a predetermined amount, the updated first domain expertise score of the Al chatbot that generated the selected answer (Para 0029 he expertise score of the user in a subsequent user input is increased (decreased) based on an amount of time between the input by the user and the subsequent user input. Thereby, the calculating calculates the expertise score in step 103 as a function to add a time factor to the input).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate in response to determine if feedback is not positive feedback, decreasing, a predetermined amount, the updated first domain expertise score of the Al chatbot that generated the selected answer; as taught by Appel, into the computer-implemented system of Kim, so that a close answer responses is selected.
Claim 14.Kim in combination with Appel teaches the computer program product of claim 10, the program instructions readable and/or executable by the computer (Para 0084 program module and readable instruction storage) to cause the computer evaluating the chatbot question for determining a domain associated with the chatbot question, wherein the first domain is determined to be associated with the chatbot question during the evaluation (Para 0072 select a chatbot for providing an answer to the user device based on the derived scores as in figure 3A element 110, 210).
But Kim does not elaborate selecting of an Al chatbots from a pool of candidate Al chatbots, wherein the Al chatbots are selected in response to a determination that the Al chatbots currently have first domain expertise scores that exceed a predetermined threshold. Appel however, teaches the selecting the Al chatbots from a pool of candidate Al chatbots, wherein the Al chatbots are selected in response to a determination that the Al chatbots currently have first domain expertise scores that exceed a predetermined threshold (subsequent user input is less than a predetermined threshold time for a determination that the Al chatbots currently have first domain expertise scores as in claim 9 of prior art Appel).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate selecting of Al chatbots from a pool of candidate Al chatbots, wherein the Al chatbots are selected in response to a determination that the Al chatbots currently have first domain expertise scores that exceed a predetermined threshold as taught by Appel, into the computer-implemented system of Kim, so that Improved AI response could be formulated.
and Kim in combination further causing the chatbot question to be sent to servers associated with the selected Al chatbots (Para 0072,0073 casing derived and ranked selected answer provided to server and user device).
Kim in combination further causing the chatbot question to be sent to servers associated with the selected Al chatbots (Para 0072,0073 casing derived and ranked selected answer provided to server and user device). wherein the selected AI chatbots are further selected from the pool of candidate AI chatbots based on the selected AI chatbots having relatively greater average current domain scores than the chatbots of the pool of candidate AI chatbots that are not selected (Para 0071 chatbot may derive scores of the multiple chatbots and select a chatbot for providing an answer to the user device 100 based on the derived score),
Kim in combination, however, does not explicitly selecting wherein the selected AI chatbots are further based on user preference information of a user that entered the chatbot question on the first user device and causing the chatbot question to be sent to the different servers associated with the selected AI chatbots. It would however institutive to selected AI chatbots further selected based on user preference information of a user that entered the chatbot question on the first user device; and causing the chatbot question to be sent to the different servers associated with the selected AI chatbots (Para 0072 derive scores based on the predicted probability values and preferences obtain from average of instant domain scores of each ChatBot or combination as determined by relay connections) to achieve optimum results.
Claim 15.Kim in combination teaches the computer program product of claim 10, wherein analyzing the answers to determine the updated first domain expertise scores of the Al chatbots that include calculating relevance-consistency values for each of the answers, wherein each relevance-consistency value indicates an extent of similarity that a given one of the answers has with the other answers (Para 0011 a receiving unit that receives, from a messenger server, a question message is relevantly consistent to a service selected from multiple services and service account information corresponding to the selected service) . But it does not elaborate wherein each relevance-consistency value is a difference of a predetermined threshold value and sum different answer value, wherein the sum different answer value characterizes, for a given one of the answers, a percentage of the answers that the given answer differs from. Appel however, teaches the relevance-consistency value (Para 0029 expertise score depending on relevancy of user input timings depending on time) is a difference of a predetermined threshold value and sum different answer value (claim 9 subsequent user input is less than a predetermined threshold time as in claim of prior art Appel), wherein the sum different answer value characterizes, for a given one of the answers, a percentage of the answers that the given answer differs from ( Para 0034,0035 it would have been a matter of obvious design choice to one having ordinary skill in the art before the effective filing date of the claimed invention to make calculated expertise score of a user based on the analyzed user input and the processed background in which the user can adjust the score accordingly that could base on sum different answer value characterizing a percentage of the answers that the given answer differs).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate relevance-consistency value is a difference of a predetermined threshold value and sum different answer value wherein the sum different answer value characterizes, for a given one of the answers, a percentage of the answers that the given answer differs from as taught by Appel, into the computer-implemented system of Kim, so that relevance-consistency could be easily determined by simple mathematical calculations for different characterization of answer values.
Claim 16.Kim in combination teaches the computer program product of claim 15, wherein the updated first domain expertise score of a given one of the Al chatbots is calculated as a sum of a first predetermined variable and a second predetermined variable, wherein the first predetermined variable is a current first domain expertise score of the given Al chatbot, wherein the second predetermined variable is a product of the current first domain expertise score of the given Al chatbot and the calculating relevance- consistency value of the given Al chatbot (Para 0011, 0012 it would have been a matter of obvious design choice to one having ordinary skill in the art before the effective filing date of the claimed invention to expertise score domain chatbots linked to the relay chatbot to determine a sum of a first predetermined variable and a second predetermined variable to derive the answer through at least one of the multiple chatbots to find an update domain expertise score).
Claim 18.Kim teaches the computer program product of claim 17, the program instructions readable and/or executable by the computer (Para 0084 program module and readable instruction storage ) but cause the computer not to receiving of feedback about the rendered selected answer. Appel however, teaches the Feedback receiving feedback about the rendered selected answer (0027 feedback ); determining whether the feedback is positive feedback (Para 0027,0027 expertise score is adjusted according to a feedback of the user for a response by a chat bot);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate feedback receiving feedback about the rendered selected answer (0027 feedback ); determining whether the feedback is positive feedback (Para 0027,0027 expertise score is adjusted according to a feedback of the user for a response by a chat bot); as taught by Appel, into the computer-implemented system of Kim, to fine tune some of the answer responses.
Kim also does not in response determine feedback is not positive feedback, decreasing, a predetermined amount, the updated first domain expertise score of the Al chatbot that generated the selected answer. Appel, however, teaches that in response to a determination that the feedback is not positive feedback, decreasing, a predetermined amount, the updated first domain expertise score of the Al chatbot that generated the selected answer (Para 0029 he expertise score of the user in a subsequent user input is increased (decreased) based on an amount of time between the input by the user and the subsequent user input. Thereby, the calculating calculates the expertise score in step as a function to add a time factor to the input).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate in response to determine if feedback is not positive feedback, decreasing, a predetermined amount, the updated first domain expertise score of the Al chatbot that generated the selected answer; as taught by Appel, into the computer-implemented system of Kim, so that an appropriate answer responses is selected.
Response to Arguments/Remarks
Applicant's arguments/amendments filed on March 5, 2026 have been considered.
Upon further consideration, a new ground(s) of rejection is made as necessitated by amendments changing the scope of the claims.
Some of examiner’s response may cite a different portions of an applied reference but do not go further and merely elaborates upon, what is taught in the previously cited portion of a reference. Thus the rejection not constituting a new ground of rejection.
35USC 101
Applicant from pages 10-12 asserted that claim 1 amended to include limitations that are not a mental process/idea of itself that can otherwise be performed in the human mind. For example, the limitations specify infrastructure that is dependent on a grid of physical hardware, e.g., a plurality of chatbot servers. Second, applicant asserts that the limitations of claim 1 have been amended to distinguish improvements to the technical field of network communications and question fulfillment because the added limitations of claim 1 specify an iterative accuracy improvement technique, e.g., feedback is used to iteratively increase an accuracy of the Al chatbots over time.
Examiner however finds that different Al chatbots hosted on pool of Al chatbot servers for positive feedbacks such as in paragraphs 0055,0070 of the present application, are basically communication channels with active plurality of servers. These are well-known in art including those cited in prior art on record Kim et al. (Fig.1 of prior art Kim et al.). The steps in the recited claims are a well-understood, routine, and conventional activities known in art. Fig.2, 3 of the instant specification indicates interface interactions with the computer system communication and memory presenting answers and to determine scores code with peripheral device set for a hardware/ software connections in a standard computing system.
The abstract idea in claim is in terms of being directed to the mental process of collecting data (e.g., a plurality of answers), analyzing that data (e.g., analyzing the answers), and providing outputs (e.g., providing the selected answers) based on that analysis, as held the CAFC in, e.g., Electric Power Group, University of Florida Research Foundation, and/or Yousician (non-precedential). It could also be characterized as being abstract as a method of organizing human activity in terms of a method of providing a conversation with a human being. It could also be characterized as being directed to training/employing a machine learning model in a particular technological environment (“AI chatbots”) and thereby abstract under the CAFC’s decision in Recentive Analytics.
To the extent that they claim, e.g., computing devices, an artificial intelligence chatbot, these are all well-known, routine, and conventional devices and/or software techniques as evidence by the limited disclosure in Applicant’s specification (cite spec sections) in regard how to make and/or use these devices and thereby do not constitute “significantly more” than the claimed abstract idea(s). None of these devices are improved qua devices, in other words, in terms of none of them will, e.g., run faster, use less power, and/or be manufactured more cheaply as a result of embodying Applicant’s invention.
In regard to arguments, transmitting data over a network is well-known, routine, and conventional use of computers and thereby not “significantly more”. See, e.g., the CAFC’s opinion in Ultramercial in regard to claiming the user of the “internet” to transmit data. Furthermore, to the extent that their method may require transmitting less data than some other abstract idea requiring transmitting data that it merely an artifact of their embodying their abstract idea as computer code and not, e.g., a general improvement to their claimed computer devices. In other words, the invention does not result in their computing devices generally being able to transmit all data faster, which would be patent eligible. If argument is persuasive (which it is not) then any time an Applicant claims to transmit less data than they otherwise might (e.g., “I claim a computer method of sending less data than I otherwise might have sent”) that would render patent eligible subject matter, which is not a sensical remark since, again, there’s no actual improvement to the speed/ability of the underlying computer components themselves.
35USC102/103
Applicant asserted on Pages 14-15 that independent and dependent claim amendments, supported by written description of paragraphs 0069 and 0073 of the present application, makes AI Chatbots answer with improved sentence structure and reduces processing operations by saving on Chatbox activities processed to clarifying request for specific domain determinations.
Examiner respectfully traverses and finds no substantial argument in support of claim recitation overcoming the prior art on record. The art Kim illustrates on Para 0072,0073 that casing derived and ranked selected answer provided to server and user device. Text chat are considered to a sentence structure as in Para 0003. The machine learning model configured to predict probability values of processing a question message by the multiple chatbots and derive scores based on the predicted probability values. A processing ratio of a chatbot that has previously derived an answer provided to the user device 100, a processing ratio of a chatbot that has derived an answer preferred by the user device 100, or a processing ratio of a chatbot that has derived an answer considered as the most appropriate answer based on context information of a chat between the user device 100 and the chatbot, or may be calculated based on a processing ratio of a chatbot that has derived an answer provided to multiple user devices or a processing ratio of a chatbot that has derived an answer preferred by the multiple user devices. Hence an improved score based Chatbox selected reducing processing activities for AI response.
Applicant also had alleged that chatbots as found in prior art reference Kim et al., even within
embodiments including relays, to be associated with the same single server (see chatbot server 120 in FIG. 1, FIG. 3A, FIG. 3B, 4B, etc.). But examiner respectfully traverses and point out that a person with ordinary skills in art may consider first and second chatbot from prior art combinations to be equivalent to be distinguishable servers from plurality of servers. Accordingly and in combination of references it is understood that a simple configuration modification may change the principle of
Chatbot server 120 operation in Kim's reference to a domain based server for a predictable outcome to overcome possible 35 USC 102/103 rejections, the amendments not entered.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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The chatbot system and method illustrates a purpose of conversation of a user, select chatbots to which the purpose of the user is to be transferred, converse with the selected chatbots in place of the user, and present the user with a result of conversation undertaken
US 11928139 B2 Patnaik; Pratyus et al.
Systems for processing queries may first determine correspondence between the parameters of the query and a set of existing data entries, a set of previous queries that have been received, or both the existing data entries and the previous queries.
US 12020130 B2 Chaudhari; Dhruv et al.
Machine learning engine that determines training documents and validation documents from a plurality of documents.
US 11409996 B2 Allen; Corville O. et al.
Clustering subject matter experts based on question and answer ratings
US 20200403945 A1 BOXWELL; Stephen et al.
METHODS AND SYSTEMS FOR MANAGING CHATBOTS WITH TIERED SOCIAL DOMAIN ADAPTATION
US 10795921 B2 Berajawala; Suneil H. et al.
Determining answers to questions using a hierarchy of question and answer pairs
US 10366332 B2 Bruno; Nicholas V. et al.
Tailoring question answering system output based on user expertise
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/S.Z/Examiner, Art Unit 3715 June 22, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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