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 .
Response to Amendments:
The amendment filed on July 16, 2026, has been entered. Claims 1-20 remain pending in the application. The correction in paragraph [0046] of the specification has been entered.
Response to Arguments:
In reference to Applicant’s arguments:
-Claim rejections under 35 U.S.C. 101.
Examiner’s response: Rejections under 35 U.S.C. 101 are held
Regarding the applicant’s arguments on page 11, paragraphs 1-2 stating “Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) … Applicant respectfully disagrees with this analysis. Applicant submits that even if the claims do recite abstract ideas, which Applicant does not concede, they would integrate those abstract ideas into practical applications that apply, rely on, or use the alleged abstract ideas in a manner that imposes meaningful limits, thereby rendering the claims subject matter eligible,” the examiner fully considers the argument, however, the examiner was unable to identify the practical application reflected in the claim language. The limitations of analyzing data from user activities, classifying users according to a defined readability level from that analysis, updating a knowledge graph and a personalized knowledge graph, as well as identifying a topic of the question, reciting mental processes that a person can perform with pen and paper.
Regarding the applicant’s arguments in paragraph 3 of page 11, stating “if a claim recites a judicial exception (a law of nature, a natural phenomenon, or an abstract idea...), it must then be analyzed to determine whether the recited judicial exception is integrated into a practical application of that exception,” the examiner was unable to identify the practical application or improvements reflected in the claim language.
Regarding the applicant’s arguments in section B. Argument, on paragraph 2 of page 12, stating “Applicant respectfully submits, for reasons which will now be discussed, using claim 1 as an example, that the present claims do not satisfy the requirements of Prong One or Prong Two, and that therefore the claims are not directed to an abstract idea,” the examiner fully considers the argument, however, the examiner was unable to identify the practical application reflected in the claim language.
Regarding the applicant’s arguments on page 13, stating “First, regarding Prong One, the Office Action states, on page 3, that claim 1 is directed to an abstract idea of mental processes and generic computer components. Applicant respectfully disagrees,” the examiner fully considers the argument, however, the examiner submits that while the limitations of the generating, by the answer generator of the one or more processors, is part of a system that is not implementing an abstract idea, this limitation is part of an additional element to apply the abstract ideas (from the limitations of analyzing data from user activities, classifying users according to a defined readability level from that analysis, updating a knowledge graph and a personalized knowledge graph, as well as identifying a topic of the question, which recite mental processes). Therefore, the 35 U.S.C. 101 rejections are maintained. See the 35 U.S.C. 101 section for details.
The examiner fully considers from page 14 the specification paragraph [0009] support stating “Chatbots typically lack any ability to personalize generated responses to match a user's knowledge level and/or emotional state. This may result in inaccurate or irrelevant responses, which in turn frustrate users and reduce engagement with, and subsequent use of, the chatbot,” however, the examiner submits that while personalization, initially seem by applicant’s argument to recite an improvement, this is not reflected in the claim language of how personalization of chatbots improve the invention as a whole.
The examiner fully considers on page 14, the specification paragraph [0010] stating “in an embodiment, a chatbot adapts to the user's knowledge level and requests user context information during real-time interactions. A chatbot uses advanced Natural Language Processing (NLP) and Natural Language Understanding (NLU) to analyze the user's inputs and to generate individualized responses which match the needs and expectations of the user. In this embodiment, disclosed methods and systems collect and analyze real-time and historical data from user interactions to build customized knowledge graphs and to refine individual user profiles. This approach improves the accuracy and relevance of responses, enhances user engagement and satisfaction with the chatbot,” while the examiner regards this information to be a potential improvement to the invention, however, the examiner could not find this improvement reflected within the claim language.
Regarding the applicant’s arguments on page 15 stating “Knowledge level determined using readability factors such as vocabulary - including jargon and technical language usage, sentence length, paragraph length, and writing style including the use of humor, metaphors, and other literary devices set forth in paragraph [0026]. The system provides a solution to this technical problem…The claimed method offers the improvement of continuously updating readability level and personalized user knowledge graphs according to analyzed data from user activities,” the examiner was unable to find the ‘continuously updating’ element reflected in the claim language that reflects this improvement.
In reference to Applicant’s arguments:
-Claim rejections under 35 U.S.C. 103 are held.
Examiner’s response:
Regarding the applicant’s arguments on page 17 stating “The prior art reference (or references when combined) must teach or suggest all the claim limitations,” and on page 18 stating “A prima facie case of obviousness under 35 USC §103 has not been properly established,” the examiner fully considers the arguments, however, the reference Wu still teaches the limitations of analyzing data from user activities, " A computer implemented method for providing individualized answers to questions, the method comprising: analyzing data from user activities;” (where Wu in paragraph [0087] mentions "analyzes the user inputs 130 or collection to determine one or more topics"),
Wu teaches "receiving, by one or more processors, a question input by a user" (where WU in [0078] describes "A user query 127 as utilized herein refers to any question or request for the chat bot 100 from one or more users that requires or is intended to illicit a response or action by the chat bot 100. Each user 102 may provide his or her input 130 as text, video, audio, and/or any other known method for gathering user input." Here, WU describes a chatbot collecting user input in the form of text, video, audio, and various other information.
Wu also teaches "identifying, by the one or more processors, a topic of the question, according to the question", (where Wu shows in [0027] of using a processor, and in [0017-0023] showed "analyzing the collection to determine topics in the conversation"), Wu also teaches "generating, by the answer generator of the one or more processors, a response to the question according to the answer generator, …, and the generalized knowledge graph, (where Wu teaches in [0077] of using a chatbot to provide or generate a response, and Wu teaches in [0126] of using a knowledge graph unit 135), Wu also teaches "and providing, by the one or more processors, the response to the user” (where Wu teaches in [0126] of giving a response to one or more users when a user enters a query).
The SINGARAJU reference teaches the limitation updating a knowledge graph…, see SINGARAJU in [0008] note “adding entities and links identified by the second set of entries to the seed graph to generate an updated customized knowledge graph” shows updating a knowledge graph.
Further, the examiner fully considers the applicant's arguments regarding some amended limitations that include the amended elements "personalized knowledge graph" or “generalized knowledge graph”, however, the arguments are moot since they do not apply to the combination of references used in the current rejection, since the new ground of rejection did not rely on the Quarteroni and Weng references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 therefore, 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 (mental process) without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “a computer implemented method for providing individualized answers to questions, the method comprising: analyzing data from user activities; classifying users according to a defined readability level using the analysis of the data; updating a knowledge graph for a defined readability level; updating a personalized knowledge graph of the user according to the knowledge graph; receiving, by one or more processors, a question input by a user; identifying, by the one or more processors, a topic of the question, according to the question; [[and]] determining a user's reading level according to the question; mapping, by the one or more processors, the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level; generating, by the answer generator of the one or more processors, a response to the question according to the answer generator, the user's personalized knowledge graph, and the generalized knowledge graph; and providing, by the one or more processors, the response to the user,” and a method is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
analyzing data from user activities; (This is a mental process, since a person can mentally evaluate and analyze data from viewing user activities, see MPEP 2106.04(a)(2)(III)),
classifying users according to a defined readability level using the analysis of the data; (This is considered a mental process, since a person can mentally evaluate and classify a user by a defined readability level from analyzing collected data, see MPEP 2106.04(a)(2)(III)),
updating a knowledge graph for a defined readability level; (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph according to the defined readability level by use of pen and paper, see MPEP 2106.04(a)(2)(III)),
updating a personalized knowledge graph of the user according to the knowledge graph; (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph from viewing the knowledge graph by use of pen and paper, see MPEP 2106.04(a)(2)(III)),
identifying, …, a topic of the question, according to the question; (This is considered a mental process, since a person can mentally evaluate and identify a topic of the question and identify a user’s reading level, see MPEP 2106.04(a)(2)(III)),
and determining a user's reading level according to the question; (This is considered a mental process, since a person can mentally evaluate and determine a user’s reading level by viewing a question, see MPEP 2106.04(a)(2)(III)),
mapping, …the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level; (This recites a mental process, since a person can mentally evaluate and map the topic to a knowledge graph from a user’s reading level, see MPEP 2106.04(a)(2)(III)),
generating, … , a response to the question according to the answer generator, the user's personalized knowledge graph, and the generalized knowledge graph, (This is a mental process, since a person can mentally evaluate and generate a response to the question from a knowledge graph, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a computer implemented method for providing individualized answers to questions, the method comprising: … (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
receiving, by one or more processors, a question input by a user; (In step 2A, prong 2, this recites mere data receiving or gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g)),
… by the answer generator of the one or more processors, (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
identifying, by the one or more processors,… (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
mapping, by the one or more processors, … (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
… by the answer generator of the one or more processors, (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and providing, by the one or more processors, the response to the user, (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g)),
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements ix, xi, xii, xiii, and xiv recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more. The additional elements x and xv recite mere data gathering or data receiving, and are considered insignificant extra-solution activities. In step 2B, these insignificant extra-solution activities are well understood routine and conventional activities, which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), – see MPEP 2106.05(d) (II)(i)), as well as from data gathering and outputting, court case Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; and court case OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 2:
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites the following abstract idea:
and altering, …, the user's personalized knowledge graph according to the feedback, (This is considered a mental process, since a person can mentally evaluate and change a knowledge graph from feedback, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 2 recites the following additional elements:
The computer implemented method according to claim 1, further comprising: receiving, …feedback regarding the response, from the user; (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),). In step 2B, this insignificant extra-solution activity is well understood routine and conventional activity which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) – see MPEP 2106.05(d) (II)(i),
… by the one or more processors, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3:
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites the following abstract ideas:
The computer implemented method according to claim 1, further comprising: monitoring, … computer interactions of the user, (This is considered a mental process, since a person can mentally evaluate and monitor computer interactions of the user by observing, see MPEP 2106.04(a)(2)(III)),
analyzing, …, the collected data according to defined dimensions; (This is considered a mental process, since a person can mentally evaluate and analyze data according to defined dimensions, see MPEP 2106.04(a)(2)(III)),
classifying, …, the user according to the analysis of the collected data; (This is considered a mental process, since a person can mentally evaluate and classify a user from analyzing collected data, see MPEP 2106.04(a)(2)(III)),
and updating, …, a knowledge graph according to the classification, (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph according to the classification, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 3 recites the following additional elements:
… by the one or more processors, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
collecting data, …, from the monitored computer interactions; (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),). In step 2B, this insignificant extra-solution activity is well understood routine and conventional activity which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) – see MPEP 2106.05(d) (II)(i),
updating, …, a user profile according to the classification; (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, from specification [0046] where in figure 2 notes that a user profile 248 is part of server application 210 – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites the following additional elements:
The computer implemented method according to claim 1, further comprising: receiving, … input regarding a service profile, (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),). In step 2B, this insignificant extra-solution activity is well understood routine and conventional activity which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) – see MPEP 2106.05(d) (II)(i),
… by the one or more processors, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and updating, …, the service profile according to the input, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5:
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites the following additional elements:
The computer implemented method according to claim 1, further comprising: receiving, … input regarding a user profile: (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),). In step 2B, this insignificant extra-solution activity is well understood routine and conventional activity which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) – see MPEP 2106.05(d) (II)(i),
… by the one or more processors, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and updating, … the user profile according to the input, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites the following additional elements:
The computer implemented method according to claim 1, further comprising: receiving, … input regarding a classification criteria, (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g),). In step 2B, this insignificant extra-solution activity is well understood routine and conventional activity which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) – see MPEP 2106.05(d) (II)(i),
by the one or more processors, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and updating, … the classification criteria according to the input, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites the following abstract idea:
The computer implemented method according to claim 1, further comprising defining, … a data structure including relevant user data, (This is considered a mental process, since a person can mentally evaluate and define a data structure that includes relevant user data, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 7 recites the following additional element:
… by the one or more processors, (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)), (In step 2B, this is also considered mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8:
Regarding claim 8, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer program product for providing individualized answers, the computer program product comprising one or more computer readable storage media and collectively stored program instructions on the one or more computer readable storage media, the stored program instructions which, when executed, cause one or more computer systems to: analyzing data from user activities; classifying users according to a defined readability level using the analysis of the data; updating a knowledge graph for a defined readability level; updating a personalized knowledge graph of the user according to the knowledge graph; receiving, by one or more processors, a question input by a user; identifying, by the one or more processors, a topic of the question, according to the question; [[and]] determining a user's reading level according to the question; mapping, by the one or more processors, the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level; generating, by the answer generator of the one or more processors, a response to the question according to the answer generator, the user's personalized knowledge graph, and the generalized knowledge graph; and providing, by the one or more processors, the response to the user,” and a method is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
to: analyzing data from user activities; (This is a mental process, since a person can mentally evaluate and analyze data from viewing user activities, see MPEP 2106.04(a)(2)(III)),
classifying users according to a defined readability level using the analysis of the data; (This is considered a mental process, since a person can mentally evaluate and classify a user by a defined readability level from analyzing collected data, see MPEP 2106.04(a)(2)(III)),
updating a knowledge graph for a defined readability level; (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph according to the defined readability level by use of pen and paper, see MPEP 2106.04(a)(2)(III)),
updating a personalized knowledge graph of the user according to the knowledge graph; (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph from viewing the knowledge graph by use of pen and paper, see MPEP 2106.04(a)(2)(III)),
identifying, …, a topic of the question, according to the question; (This is considered a mental process, since a person can mentally evaluate and identify a topic of the question and identify a user’s reading level, see MPEP 2106.04(a)(2)(III)),
and determining a user's reading level according to the question; (This is considered a mental process, since a person can mentally evaluate and determine a user’s reading level by viewing a question, see MPEP 2106.04(a)(2)(III)),
mapping, …the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level; (This recites a mental process, since a person can mentally evaluate and map the topic to a knowledge graph from a user’s reading level, see MPEP 2106.04(a)(2)(III)),
generating, … , a response to the question according to the answer generator, the user's personalized knowledge graph, and the generalized knowledge graph, (This is a mental process, since a person can mentally evaluate and generate a response to the question from a knowledge graph, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A computer program product for providing individualized answers, the computer program product comprising one or more computer readable storage media and collectively stored program instructions on the one or more computer readable storage media, the stored program instructions which, when executed, cause one or more computer systems… (In step 2A, prong 2, this is considered a generic computer component being used as a tool. – see MPEP 2106.05(f)),
receiving, by one or more processors, a question input by a user; (In step 2A, prong 2, this recites mere data receiving or gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g)),
… by the answer generator of the one or more processors, (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
identifying, by the one or more processors,… (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
mapping, by the one or more processors, … (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
… by the answer generator of the one or more processors, (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and providing, by the one or more processors, the response to the user, (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g)),
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements xi, xii, xiii, and xiv recite mere instructions to apply the judicial exception using generic computer components, and additional element ix recite a generic computer component being used as a tool, which are not indicative of significantly more. The additional elements x and xv recite mere data gathering or data receiving, and are considered insignificant extra-solution activities. In step 2B, these insignificant extra-solution activities are well understood routine and conventional activities, which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), – see MPEP 2106.05(d) (II)(i)), as well as from data gathering and outputting, court case Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; and court case OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 9-14:
Regarding claim 8, all of claim 8’s dependent claims follow the deficiencies of their parent claim. Since claims 9-14 recite similar limitations as corresponding claims 2-7 listed above, they are rejected for similar reasons under 35 U.S.C. 101.
Claim 15:
Regarding claim 15, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer system for providing, the computer system comprising: one or more computer processors; one or more computer readable storage media; and stored program instructions on the one or more computer readable storage media for execution by the one or more computer processors, the stored program instructions which, when executed, cause the one or more computer processors to: analyzing data from user activities; classifying users according to a defined readability level using the analysis of the data; updating a knowledge graph for a defined readability level; updating a personalized knowledge graph of the user according to the knowledge graph; receiving, by one or more processors, a question input by a user; identifying, by the one or more processors, a topic of the question, according to the question; [[and]] determining a user's reading level according to the question; mapping, by the one or more processors, the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level; generating, by the answer generator of the one or more processors, a response to the question according to the answer generator, the user's personalized knowledge graph, and the generalized knowledge graph; and providing, by the one or more processors, the response to the user,” and a method is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
to: analyzing data from user activities; (This is a mental process, since a person can mentally evaluate and analyze data from viewing user activities, see MPEP 2106.04(a)(2)(III)),
classifying users according to a defined readability level using the analysis of the data; (This is considered a mental process, since a person can mentally evaluate and classify a user by a defined readability level from analyzing collected data, see MPEP 2106.04(a)(2)(III)),
updating a knowledge graph for a defined readability level; (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph according to the defined readability level by use of pen and paper, see MPEP 2106.04(a)(2)(III)),
updating a personalized knowledge graph of the user according to the knowledge graph; (This is considered a mental process, since a person can mentally evaluate and update a knowledge graph from viewing the knowledge graph by use of pen and paper, see MPEP 2106.04(a)(2)(III)),
identifying, …, a topic of the question, according to the question; (This is considered a mental process, since a person can mentally evaluate and identify a topic of the question and identify a user’s reading level, see MPEP 2106.04(a)(2)(III)),
and determining a user's reading level according to the question; (This is considered a mental process, since a person can mentally evaluate and determine a user’s reading level by viewing a question, see MPEP 2106.04(a)(2)(III)),
mapping, … the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level; (This recites a mental process, since a person can mentally evaluate and map the topic to a knowledge graph from a user’s reading level, see MPEP 2106.04(a)(2)(III)),
generating, … , a response to the question according to the answer generator, the user's personalized knowledge graph, and the generalized knowledge graph, (This is a mental process, since a person can mentally evaluate and generate a response to the question from a knowledge graph, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A computer system for providing, the computer system comprising: one or more computer processors; one or more computer readable storage media; and stored program instructions on the one or more computer readable storage media for execution by the one or more computer processors, the stored program instructions which, when executed, cause the one or more computer processors… (In step 2A, prong 2, this is considered a generic computer component being used as a tool. – see MPEP 2106.05(f)),
receiving, by one or more processors, a question input by a user; (In step 2A, prong 2, this recites mere data receiving or gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g)),
… by the answer generator of the one or more processors, (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
identifying, by the one or more processors,… (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
mapping, by the one or more processors, … (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
… by the answer generator of the one or more processors, (In step 2A, prong 2, this recites mere instructions to apply an exception using generic computer – see MPEP 2106.05(f)),
and providing, by the one or more processors, the response to the user, (In step 2A, prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity – see MPEP 2106.05(g)),
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements xi, xii, xiii, and xiv recite mere instructions to apply the judicial exception using generic computer components, and additional element ix recite a generic computer component being used as a tool, which are not indicative of significantly more. The additional elements x and xv recite mere data gathering or data receiving, and are considered insignificant extra-solution activities. In step 2B, these insignificant extra-solution activities are well understood routine and conventional activities, which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), – see MPEP 2106.05(d) (II)(i)), as well as from data gathering and outputting, court case Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; and court case OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 16-20:
Regarding claim 15, all of claim 15’s dependent claims follow the deficiencies of their parent claim. Since claims 16-20 recite similar limitations as corresponding claims 2-6 listed above, they are rejected for similar reasons under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 over Wu, X. (US PG Pub. No. US20180196796A1), published on July 12, 2018, (hereafter, WU), in view of Quarteroni S., in “Personalized question answering,” published in 2010, available at: https://aclanthology.org/2010.tal-1.4.pdf , (hereafter, Quarteroni), further in view of Singaraju, G. et al., (US PG Pub. No. US20200057946A1), published on February 20, 2020, (hereafter, SINGARAJU), further in view of Weng, J., et al, in “Construction and application of teaching system based on crowdsourcing knowledge graph,” published on October 18, 2020, available at : https://arxiv.org/pdf/2010.08995, (hereafter, Weng).
Claim 1:
Regarding claim 1, WU teaches “1. A computer implemented method for providing individualized answers to questions, the method comprising: analyzing data from user activities;”
See WU in paragraph [0087] describe "The text or annotated text generated by the LU system 110 for each collected input in the conversation is collected by the topic detection system 112 of the chat bot 100. The one or more inputs in a conversation between the chat bot 100 and/or one or more users may be referred to herein as a collection. The topic detection model 112 analyzes the user inputs 130 or collection to determine one or more topics 128 in the conversation between the chat bot 100 and one or more users 102. The topic detection system 112 may utilize a syntactic dependency parser to parse sentences in session and pick noun words/phrases that are topic candidates." Here, WU shows analyzing data from user inputs to identify topics between the chatbot 100 and one or more users showing that user activities are the user inputs and the conversations directed towards the chatbot.
Further, WU teaches " receiving, by one or more processors, a question input by a user"
See WU in [0078] describe "A user query 127 as utilized herein refers to any question or request for the chat bot 100 from one or more users that requires or is intended to illicit a response or action by the chat bot 100. Each user 102 may provide his or her input 130 as text, video, audio, and/or any other known method for gathering user input." Here, WU describes a chatbot collecting user input in the form of text, video, audio, and various other information.
Regarding any limitation in the claim that describes "one or more processors", see WU describe in [0027] " the disclosure is directed to a system for a multiple topic intelligent chat bot. The system includes at least one processor and a memory. The memory encodes computer executable instruction that, when executed by the at least one processor, are operative to: collect user inputs from a group chat of a first user and a second user to form a collection;” Here, WU describes processors that perform instructions to run a conversational chatbot system.
Further, WU teaches "identifying, by the one or more processors, a topic of the question, according to the question;"
See WU in [0027] describe the system including at least one processor, and see WU in [0017-0023] describe "The method includes: collecting inputs in a conversation to form a collection; analyzing the collection to determine topics in the conversation; assign a sentiment to each topic; scoring an engagement rate for each topic to form an engagement score for each topic; scoring a user interest in each topic to form an interest score for each topic; creating a knowledge graph between the topics that graphs relationships between the topics;" Here, WU in [0017-0023] mentions identifying topics (which includes at least one topic) for each conversation between the system and user.
Also, see WU in [0087] mention "For example, for the sentence “?/Anybody coming to happy hour tonight?” as illustrated in FIG. 3A, the different topics that may be identified by a semantic dependency tree of the topic detection system 112 ... Part-of-speech (POS) tags are also labeled to each word in the tree. Accordingly, the candidate nouns for the example sentence above include “happy hour” and “anyone”. Since “anyone” is closer to a pronoun, “happy hour” is selected as the topic keyword by the topic detection system 112 for the example sentence." Here, WU shows that according to the question 'Anybody coming to happy hour tonight?', a topic of this question (which was happy hour in this case) was extracted and identified. See WU in [0027] for details regarding a processor.
Further, WU teaches “generating, by the answer generator of the one or more processors, a response to the question according to the answer generator, …, and the generalized knowledge graph;"
See WU in [0077] describe "When the AI chat bot 100 decides to provide a response, the response prediction system 116 predicts a response utilizing collected inputs, labeled sentences, labeled topics, scored features, and/or the created knowledge graph 135." Here, WU acknowledges the presence of using at least one knowledge graph to gather information from the AI chat bot.
Further, See WU in [0126] describe "In response to the determination to provide a response, the response prediction system 116 predicts a response based on the labeled inputs associated with the topic, such as a query, the labeled topic, the topic knowledge graph 135, scored features 133, and/or world knowledge 118." Here, WU mentions that the response prediction system 116 (i.e. answer generator of the one or more processors), provides a response based on information from the topic knowledge graph 135. See WU in [0027] for details regarding a processor.
Further, WU teaches “and providing, by the one or more processors, the response to the user”
See WU in [0126] describe "As such, the chat bot 100 may provide a response to the conversation with the one or more users in response to a user query 127," Here, WU shows that the chat bot 100 system provides a response to one or more users to a user query.
Further, See WU in [0126] describe "In response to the determination to provide a response, the response prediction system 116 predicts a response based on the labeled inputs associated with the topic, such as a query, the labeled topic, the topic knowledge graph 135, scored features 133, and/or world knowledge 118." WU mentions that the response prediction system 116 (i.e. answer generator of the one or more processors), provides a response based on information from the topic knowledge graph 135. See WU in [0027] for details regarding a processor.
However, WU did not teach:
• “classifying users according to a defined readability level using the analysis of the data;” or “updating a knowledge graph for a defined readability level;”
• “updating a personalized knowledge graph of the user according to the knowledge graph;” or “and determining a user's reading level according to the question;”
• “mapping, by the one or more processors, the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level;
• generating, … a response to the question according to the … user's personalized knowledge graph, … ;”
In an analogous art, Quarteroni teaches “classifying users according to a defined readability level using the analysis of the data;”
See Quarteroni in page 103, section 4. A User Model for open-domain Question Answering, paragraphs 1-2, describe "In this work, the salient feature of a personalized QA system with respect to a traditional one (see section 3) is the presence of a User Modelling component, constructing, maintaining and updating a representation of the current user...To meet this aim, the proposed User Model (UM) is centered on two types of information: on the one hand, an estimation of the user’s age and reading level; on the other, a representation of his/her topics of interest. The objective of such dimensions are an increased readability of answers and a profile-based answer filtering, respectively." Here, Quarteroni describes a personalized Question-Answering (QA) system that customizes its responses based on the reader's reading level along with their interests.
Further, see Quarteroni in page 103, section 4, paragraph 3 mention "the User Model represents students searching for information on the Web for their assignments and consists of three components: 1) age range, a ∈ {7−10, 11−16, adult}; the first two ranges correspond to the primary and secondary school age in Britain, respectively; 2) reading level, r ∈ {basic, medium, advanced}; ..." Here, Quarteroni explicitly defines the readability level from using data analysis of each user in this third paragraph from getting information for a representation of a current user from paragraph 1. See paragraph 5 in page 103 from Quarteroni for details.
Further, Quarteroni teaches “and determining a user's reading level according to the question;”
See Quarteroni mention on page 108, second paragraph "It may be worth highlighting that such a model of personalization affects the results to all types of questions, regardless of their expected answer classes. Thus, both factoid and non-factoid questions can receive personalized answers according to the proposed algorithm. The need to personalize answers to non-factoid questions may appear as the most intuitively justified,.... For instance, the IR engine will respond to the question: What is “Ginger and Fred”? with documents relating to a film, a building and a dancing couple... However, personalization can also affect the factoid domain; for instance, it is intuitive that the answer to When did the Middle Ages begin?– clearly a temporal (i.e. factoid) question– can be different depending on the age and reading abilities of the reader. While a child might welcome the answer The Middle Ages start with the fall of the Roman Empire in 476 AD, an adult might prefer an answer highlighting how it makes little sense to think of a unique starting date to the medieval era." Here, Quarteroni shows that depending on the question, the system creates answers that evaluate and identify a user's reading level depending on the literacy level on a subject for that user.
Further, see Quarteroni in page 113 in section 7.1 Reading level evaluation, mention “an experiment was designed with 20 participants aged between 16 and 52, from various backgrounds … and with a self-assessed good or medium English reading level. The answers to such questions included factoids (such as Who painted the Sistine Chapel?), lists (Types of rhyme), and definitions (What is chickenpox?). Each participant examined the results returned by YourQA to 8 of the 24 questions; for each question, results were returned in three different answer groups, corresponding to the basic, medium and advanced reading levels. Participants then evaluated the three sets of answers and specified for each answer passage whether or not they agreed that the given passage was assigned to the correct reading level.” Here, Quarteroni explicitly illustrates that depending on the user's question, this can determine the reading ability (i.e. reading level) of the user.
Also, see Quarteroni in page 1, abstract mention "as queries can be ambiguous and even answers extracted from documents with relevant content may be ill-received by users if they are too difficult (or simple) for them. We address the issue by integrating a User Modelling component to personalize the results of a Web-based open domain Question Answering system based on the user’s reading level and interests." Here, Quarteroni mentions using a model to evaluate reading level according to the questions the user provides.
Further, Quarteroni teaches “mapping, … for a reading level according to the user’s reading level;”
See Quarteroni in page 114, section 7.2.1. First phase: profile design describe “In the first phase, participants were invited to explore the Yahoo! Directory8, an online guide to the Web where sites are categorized by topic, and provide 2-3 categories of their interest: these ranged from dog care and music to role-playing games and computers. Participants were also invited to brainstorm as many key-phrases as they wanted relating to each of their chosen categories: examples of these were “frank capa”, “muzzle”, “little mermaid”; individual profile arrays were created from the key phrases using the format in formula [3]. Then, for each category of interest, related queries were elaborated in such a way that the system’s answers would be different when the profile filtering component was active”. Quarteroni here mentioned identifying a category by topic, which relates to mapping the topic to a web system.
Further, see Quarteroni in page 103, section 4, paragraph 3 mention "the User Model represents students searching for information on the Web for their assignments and consists of three components: 1) age range, a ∈ {7−10, 11−16, adult}; the first two ranges correspond to the primary and secondary school age in Britain, respectively; 2) reading level, r ∈ {basic, medium, advanced}; ..." Here, Quarteroni explicitly defines the reading level from using data analysis of each user in this third paragraph from getting information for a representation of a current user from paragraph 1.
Also, see Quarteroni in page 100, last paragraph note “The approach to personalized QA discussed in the following sections has the distinguishing feature of (semi-)automatically creating a User Model by taking advantage of IR approaches to personalized relevance computation and NLP approaches such as automatic concept extraction. In particular, the following techniques are borrowed and adapted from different applications towards the construction of a User Model: the collection of personal data and explicit feedback from users, the automatic acquisition of implicit information such as textual readability, as well as the automatic extraction of topics and tags.” Here, Quarteroni shows that collecting each user’s information, to extract topics and tags to indicate mapping by the user topic from the question answering QA system.
Also, see Quarteroni in page 1, abstract mention "as queries can be ambiguous and even answers extracted from documents with relevant content may be ill-received by users if they are too difficult (or simple) for them. We address the issue by integrating a User Modelling component to personalize the results of a Web-based open domain Question Answering system based on the user’s reading level and interests." Here, Quarteroni mentions using a model to evaluate reading level according to the questions the user provides.
Further, Quarteroni teaches “updating ... for a defined readability level,”
See Quarteroni mention in page 103, section 4. A User Model for open-domain Question Answering, paragraph 3 mention “the User Model represents students searching for information on the Web for their assignments and consists of three components: 1) age range, a ∈ {7−10, 11−16, adult}; the first two ranges correspond to the primary and secondary school age in Britain, respectively; 2) reading level, r ∈ {basic, medium, advanced}; ...” Here, Quarteroni explicitly defines the readability level from using data analysis of each user in this third paragraph from getting information for a representation of a current user from paragraph 1. See paragraph 5 in page 103 from Quarteroni for details.
Also, see Quarteroni in page 103, section 4, paragraphs 1-2, describe “in this work, the salient feature of a personalized QA system with respect to a traditional one (see section 3) is the presence of a User Modelling component, con structing, maintaining and updating a representation of the current user…to meet this aim, the proposed User Model (UM) is centered on two types of information: on the one hand, an estimation of the user’s age and reading level; on the other, a representation of his/her topics of interest.” Here, the phrase ‘updating a representation of the current user’ shows updating information of the user by the user’s level, such as reading level mentioned from page 103, section 4, paragraph 3.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of WU and incorporate into the teachings of Quarteroni because both references teach a system that takes user inputs and generates responses for a user corresponding to a user’s reading level.
One of ordinary skill in the art would be motivated to do so because such a method provides an “efficient and light-weight method to personalize the results of a Web-based Question Answering system based on a User Model representing the reading level and interests of individual users. We show how the User Model components can be estimated automatically and fairly unobtrusively” (see Quarteroni in page 120, second paragraph, in section 8. Conclusions).
However, WU in view of Quarteroni, did not teach “updating a knowledge graph for a defined … level;” or “updating a personalized knowledge graph of the user according to the knowledge graph;”
• or “mapping, by the one or more processors, the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a reading level according to the user's reading level;”
• or “generating, … a response to the question according to the … user's personalized knowledge graph, … ;”
In an analogous field, SINGARAJU teaches “updating a knowledge graph for a defined … level”
See SINGARAJU in paragraph [0029] mention “According to some embodiments, entities can be extracted from the small user dataset (including, e.g., user utterances, intents, entities, and QnAs) for a custom application (e.g., a chatbot) based on certain rules. A seed graph can be generated based on the extracted entities and connections or relations between the entities. Large-scale reference knowledge graphs, such as WikiData, can then be traversed using a finite state machine to identify candidate entities and/or relations to be added to the seed graph to expand the seed graph into a customized knowledge graph for the custom application. The traversal may also help to identify possible additional relations between entities in the user dataset and relevant facts from the reference knowledge graph. A scoring function may be used to select entities and/or relations from the identified entities and relations, and the selected entities and/or relations may then be added to the seed graph to generate the customized knowledge graph. The generated customized knowledge graph can be used for the customer application, such as a custom chatbot, using certain knowledge graph embedding techniques.” Here, SINGARAJU notes that in the process of creating a knowledge graph, the method starts with extracting entities and establishing relations from a user dataset to build a seed graph (which is viewed as a knowledge graph), then expanding this by incorporating information from large scale reference graphs from WikiData, then selects and filters new information by using a scoring function, and finally adds the selections back into the seed graph to create an updated knowledge graph.
Also, see SINGARAJU note in paragraph [0078] "In some embodiments, dialog engine 212 may use a state machine that includes user defined states (e.g., end user intents) and actions to take in the states or from state to state to handle the conversations with end users." Here, SINGARAJU shows identifying a user's information according to user intents with states (construed to mean level).
Further, see SINGARAJU in [0093] mention "For example, the bot may use the natural language processor to analyze the inputs from the users, and use the analysis results to navigate through the knowledge graph and find the user intents at a semantic level. The user intents can then be used to determine the most appropriate next response to the user." Here, SINGARAJU shows updating a knowledge graph for a defined semantic level.
Further, see SINGARAJU in paragraph [0008] mention "The method may also include computing priority scores for the entries in the buffer using the priority function and the updated control parameter, selecting a second set of entries having the highest priority scores from the entries in the buffer, and adding entities and links identified by the second set of entries to the seed graph to generate an updated customized knowledge graph." Here, SINGARAJU mentions updating a knowledge graph for a level by priority score.
Further, SINGARAJU teaches “mapping, by the one or more processors, the topic to an answer generator, the user's personalized knowledge graph, and a generalized knowledge graph for a ...level according to the user's ... level;”
See SINGARAJU in [0011] describe a method that "cause the one or more processors to perform processing including receiving a user dataset for the application, extracting entities from the user dataset, identifying links between the entities based on the user dataset, and creating a seed graph that represents the entities and the links between the entities. The processing may also include identifying weakly connected components in the seed graph, and, for each weakly connected component in the seed graph, mapping entities in the weakly connected component to vertices in a reference knowledge graph," Here, SINGARAJU shows mapping entities gathered from a user's dataset referencing a knowledge graph, where entities are construed as identifying and extracting specific, structured information (like a topic, product, or date) from user input to guide the conversation. SINGARAJU also mentions using processors within the system. See SINGARAJU in paragraphs [0003, 0037] for details.
Further, see SINGARAJU in [0005] describe “In some embodiments, the user dataset may include a plurality of user utterances. Extracting the entities and identifying the links between the entities may include, for example, performing part-of-speech tagging, named-entity recognition, and/or constituency parsing on the user dataset. Mapping the entities in the weakly connected component to the vertices in the reference knowledge graph may include mapping the entities to the vertices in the reference knowledge graph.” By performing part-of-speech tagging, named-entity recognition, SINGARAJU illustrates that these methods identify subject matter that correspond to a topic within the entities.
Further, see SINGARAJU in [0040] describe “An intelligent bot, generally powered by artificial intelligence (AI), can communicate more intelligently and contextually in live conversations, and … may be able to understand the end user's intention based upon user utterances in natural language and respond accordingly.” Here, SINGARAJU mentions an intelligent bot represents an answer generator, and this bot understands a user’s intention and respond accordingly, which relates to an answer generator. See SINGARAJU for details about a topic in [0054] and [0071].
See SINGARAJU also describe in [0093] “Instead of using intent matching as described above, some bot systems may combine lower level analysis results (syntax analysis, entities, key phrases) with a knowledge graph for a specific domain to identify user intents. The knowledge graph (or ontology) may connect intents, phrases, nouns, and adjectives to concepts in the domain. For example, the bot may use the natural language processor to analyze the inputs from the users, and use the analysis results to navigate through the knowledge graph and find the user intents at a semantic level. The user intents can then be used to determine the most appropriate next response to the user.” Here, SINGARAJU shows that the bot analyzes a user’s inputs to search via a knowledge graph to find user intents, which can then be used to allow the bot to respond back to the user (where respond back relates to the bot being an answer generator). See SINGARAJU for details in [0094-0101].
Further, see SINGARAJU in paragraph [0029] mention “entities can be extracted from the small user dataset (including, e.g., user utterances, intents, entities, and QnAs) for a custom application (e.g., a chatbot) based on certain rules. A seed graph can be generated based on the extracted entities and connections or relations between the entities. Large-scale reference knowledge graphs, such as WikiData, can then be traversed using a finite state machine to identify candidate entities and/or relations to be added to the seed graph to expand the seed graph into a customized knowledge graph for the custom application. The traversal may also help to identify possible additional relations between entities in the user dataset and relevant facts from the reference knowledge graph. A scoring function may be used to select entities and/or relations from the identified entities and relations, and the selected entities and/or relations may then be added to the seed graph to generate the customized knowledge graph. The generated customized knowledge graph can be used for the customer application, such as a custom chatbot, using certain knowledge graph embedding techniques.” Here, SINGARAJU notes that in the process of creating a knowledge graph, the method starts with extracting entities and establishing relations from a user dataset to build a seed graph (which is viewed as a knowledge graph), then expanding this by incorporating information from large scale reference graphs from WikiData, then selects and filters new information by using a scoring function, and finally adds the selections back into the seed graph to create an updated knowledge graph.
Further, see SINGARAJU in paragraphs [0029-0030] describe “The techniques disclosed herein can also be used in other applications, such as question answering or relation extraction. As used herein, a “chatbot,” “bot,” or “skill” refers to a computer program designed to simulate conversation with human users, especially over the Internet.” Here, SINGARAJU also mentions using a computer program like a chatbot which simulate conversations, including generating answers, and relates to being an answer generator.
By identifying relations between entities in a user data with facts from a reference knowledge graph (i.e. generalized knowledge graph), SINGARAJU teaches mapping a topic to an answer generator (which is gathering information for the chatbot described in [0029-0030]). SINGARAJU mentions customized knowledge graph to correspond to a user's personalized knowledge graph.
Also, see SINGARAJU note in paragraph [0078] "In some embodiments, dialog engine 212 may use a state machine that includes user defined states (e.g., end user intents) and actions to take in the states or from state to state to handle the conversations with end users." Here, SINGARAJU shows identifying a user's information according to user intents with states (construed to mean level).
Further, SINGARAJU teaches “generating, … a response to the question according to the … user's personalized knowledge graph, … ;”
See SINGARAJU in paragraph [0093] describe “Instead of using intent matching as described above, some bot systems may combine lower level analysis results (syntax analysis, entities, key phrases) with a knowledge graph for a specific domain to identify user intents. The knowledge graph (or ontology) may connect intents, phrases, nouns, and adjectives to concepts in the domain. For example, the bot may use the natural language processor to analyze the inputs from the users, and use the analysis results to navigate through the knowledge graph and find the user intents at a semantic level. The user intents can then be used to determine the most appropriate next response to the user.” Here, SINGARAJU explicitly shows using a knowledge graph that identifies a user’s intents (i.e. a user's personalized knowledge graph) to create the next appropriate response for the user (i.e. generating, … a response to the question according to that user’s personalized knowledge graph).
Further, see SINGARAJU in paragraph [0058] mention “In one specific embodiment, an end user may send a message to bot system 120 using mobile device 110 through messaging application system 115…Using the one or more characteristics, the bot system may respond to the end user on the messaging application. The response may include a message to the end user that responds to the message received from the end user. For example, the response may include a greeting with the name of the end user, such as “Hi Tom, What can I do for you?”. Depending on the enterprise associated with the bot system, the bot system may progress to accomplish a goal of the enterprise. For example, if the bot system is associated with a pizza delivery enterprise, the bot system may send a message to the end user asking if the end user would like to order pizza. The conversation between the bot system and the end user may continue from there, going back and forth, until the bot system has completed the conversation or the end user stops responding to the bot system.” Here, SINGARAJU describes how the chat bot have a conversation with a user, by asking the user a question, then responding to the user.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU and Quarteroni and incorporate into the teachings of SINGARAJU because all references teach a system that takes a user’s inputs and generates responses for a user corresponding to a user’s reading level by mapping a topic from knowledge graphs.
One of ordinary skill in the art would be motivated to do so because such a method provides a method that “improve intent classification in a chatbot based on knowledge graph embedding techniques” (SINGARAJU, [0003]), and “may allow for a more natural conversation between the bot and the end users for improved conversational experience. Instead of the end user learning a fixed set of keywords or commands that the bot knows how to respond to, an intelligent bot may be able to understand the end user's intention based upon user utterances in natural language and respond accordingly,” (SINGARAJU, [0040]).
However, WU in view of Quarteroni, further in view of SINGARAJU did not teach “updating a personalized knowledge graph of the user according to the knowledge graph;”
In an analogous art, Weng teaches “updating a personalized knowledge graph of the user according to the knowledge graph;”
See Weng in page 9, section 4.2 Collaborative Creation describe "Knowledge graph with a good extensible graph structure and human-readable visualization, we develop a knowledge graph tool to allow users add nodes and links to the graph together. Therefore, teachers can carry out group inquiry learning according to the teaching needs, and learners can improve the collected materials according to the topics carried out by teachers." By adding nodes and links to the knowledge graph, Weng shows updating an individual's knowledge graph (i.e. personalized knowledge graph for the user).
See Weng in figures 7 on page 7, and figures 8 and 9 on page 8, and figure 11 on page 10... where Weng describes updating a personalized knowledge graph, where personalized is construed to mean a unique individual graph for each user. Figures 7-9 are subgraphs part of a general overall knowledge graph.
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In pages 9-10, Weng shows various users, where each have their own knowledge graphs in figure 11.
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Here, the figure shows each user's knowledge graph also gets added nodes and links to improve the materials according to topics (where adding nodes and links relate to updating the personalized knowledge graph since knowledge graphs contain nodes and links).
Also, see Weng in page 1, abstract mention "Based on the three subgraphs of knowledge graph, prominent teacher, student learning situation and suitable learning route could be visualized. [Application] Personalized exercises recommendation model is used to formulate the personalized exercise by algorithm based on the knowledge graph." Here, Weng shows using the general subgraphs (i.e. the knowledge graph), this later updates a personalized knowledge graph for each user.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, and SINGARAJU, and incorporate into the teachings of Weng because all references teach a system that takes a user’s inputs and generates responses for a user corresponding to a user’s reading level by mapping a topic from knowledge graphs.
One of ordinary skill in the art would be motivated to do so because such a method provides “with the help of knowledge graph, the association and visualization of knowledge will be friendly presented”, (see Weng in page 1, Introduction, first paragraph), and “Knowledge graph with a good extensible graph structure and human-readable visualization, … Therefore, teachers can carry out group inquiry learning according to the teaching needs, and learners can improve the collected materials according to the topics carried out by teachers,” (see Weng in page 9, section 4.2 Collaborative Creation).
Claim 8:
Regarding claim 8, the claim recites similar limitations as corresponding independent claim 1, and is rejected for similar reasons as claim 1 using similar teachings and rationale.
Further, SINGARAJU teaches “A computer program product for providing individualized answers, the computer program product comprising one or more computer readable storage media and collectively stored program instructions on the one or more computer readable storage media, the stored program instructions which, when executed, cause one or more computer systems…,”
See SINGARAJU in [0036] describe "a computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, …" Here, SINGARAJU describes a computer-program product with instructions to run a program.
Claim 15:
Regarding claim 15, WU teaches “A computer system for providing, the computer system comprising: one or more computer processors; one or more computer readable storage media; and stored program instructions on the one or more computer readable storage media for execution by the one or more computer processors, the stored program instructions which, when executed, cause the one or more computer processors,”
See WU in [0005] describe “One aspect of the disclosure is directed to a system for a multiple topic chat bot. The system includes at least one processor and a memory. The memory encodes computer executable instruction that, when executed by the at least one processor.” Here, WU mentions a system that has a processor and a memory.
Further, see WU in [0162] describe “The term computer readable media or storage media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions”. Here, WU mentions a computer readable storage media.
Regarding claim 15, the claim recites similar limitations as corresponding independent claim 1, and is rejected for similar reasons as claim 1 using similar teachings and rationale.
Claims 2, 4, 5, 7, 9, 11, 12, 14, 16, 18, and 19 are rejected under 35 U.S.C. 103 over WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, and further in view of Wang, J., (US PG Pub. No. US20230245651A1), published on August 3, 2023, (hereafter, WANG).
Claim 2:
Regarding claim 2, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, teach the limitations of claim 1.
Further, SINGARAJU teaches "...user's personalized knowledge graph"
See SINGARAJU in [0003] describe "The present disclosure relates generally to building customized knowledge graphs, and more particularly, to techniques for constructing a customized knowledge graph for a specific knowledge domain (e.g., for a specific client or a specific bot) based on a small user dataset and large-scale reference (e.g., external) knowledge graphs. The customized knowledge graph may be used to, for example, improve intent classification in a chatbot based on knowledge graph embedding techniques. " Here, SINGARAJU mentions a client (which relates to a user) in building customized knowledge graph based on a generic external knowledge graph.
However, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, did not teach: “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, feedback regarding the response, from the user;” or “and altering, by the one or more processors, the ... knowledge graph according to the feedback.”
In an analogous field, WANG teaches “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, feedback regarding the response, from the user;”
See WANG in [0030] describe "FIG. 23 depicts a flow chart illustrating a process through which a conversational AI agent learns and adapts based on user feedback and advanced reasoning models." Further, see WANG in [0212] describe "Throughout the dialogue flow, the AI system maintains context and remembers the user’s previous inputs and responses. This allows the conversational AI agent to provide more contextually relevant and personalized responses to the user. The flow chart also includes a loop that enables the user to provide feedback on the conversational AI agent’s response. If the user indicates that the response was not helpful or accurate, the conversational AI agent may ask for clarification or provide an alternative response." Here, WANG shows the conversational AI agent is similar to a QA system that adapts based on user feedback, where the feedback is mentioned by the user who responds back to the AI agent regarding the quality of the AI agent's response, according to WANG in [0212].
Further, see WANG in [0506] describe "These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks." Here, WANG shows the processor that is part of the AI system that runs the conversational AI agent with a user. For more info, see WANG in [0057] and in [0096], [0113] for details.
Further, WANG teaches “and altering, by the one or more processors, the knowledge graph according to the feedback,”
See [0195], where WANG mentions in step "(5) Maintenance and Evolution: The knowledge graph engine is responsible for updating and maintaining the knowledge graph, incorporating new information, and detecting and resolving inconsistencies or errors. The knowledge graph engine may also use ML algorithms to learn new patterns, relationships, or insights from the data and incorporate them into the knowledge graph. " Further, see WANG in [0064] describe "One way that an AI system can identify new information from continuously monitoring user interactions is through ML algorithms. These algorithms can analyze large amounts of data from user interactions and identify patterns and trends that can be used to update the OKB." Here, WANG shows the knowledge graph is updated or altered to incorporate new information, where this new information is based on information the user provides to the AI agent through user interactions as WANG mentions in [0064]. Further, see WANG in [0506] regarding the processor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, and Weng, incorporate with the teachings of WANG by using the teachings of WU, Quarteroni, SINGARAJU, and Weng, of a system that takes user input to generate responses, and incorporate with WANG’s teaching of receiving user feedback regarding the system’s response, and updating a knowledge graph according to the feedback.
One of ordinary skill in the art would be motivated to do so because by integrating WANG’s framework into the methods of WU, Quarteroni, SINGARAJU, and Weng, one with ordinary skill in the art would achieve the goal of providing “improved accuracy and efficiency in understanding contextual information in the environment and predicting the most relevant user intent and objective. The present invention aims to deliver an improved user experience by considering individual preferences, conversational history, and communication styles. The AI system adapts its responses according to a user’s past interaction or preferred tone, resulting in more effective and satisfying conversations.” (WANG, [0033-0034]).
Claim 4:
Regarding claim 4, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, teach the limitations of claim 1.
However, WU in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, did not teach “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, input regarding a service profile: and updating, by the one or more processors, the service profile according to the input.”
In an analogous field, WANG teaches “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, input regarding a service profile:”
Note the specification in paragraph [0046] states “ server application 210 includes a manager 240, which provides a user interface allowing administrators and users to configure …system services, create/edit personalized user settings and user profiles 248, … adjust system algorithms, including NLP/NLU algorithms, adjust the defined data structures 244 described above, and store these elements in system service profiles 242.” From paragraph [0046] in the specification, the examiner construes a service profile to be a computer system storage element that contains information regarding users and includes data structures and algorithms, and is part of the server application.
See WANG in [0049-0051] describe "Referring to FIG. 1 , the OKB 104 is a structured system, but not limited to a database, a set of databases, a repository, a set of repositories, and the like, that stores different types of data and various types of information that the AI system can access and use to provide contextually relevant and personalized responses.
…The OKB 104 also manages information about specific objects or entities, including their properties, location, and relationships to other objects. The information can be manually entered or automatically extracted from text, images, or other sources." Here, WANG describes the OKB system, (which relates to the service profile), can store context information about different types of data, and also includes extracted information (i.e. input) from text or other sources. See WANG in [0041], [0062-63], [0360-0361], and [0345] for more details. Further, see WANG in [0414] for additional information.
Further, see WANG in [0506] describe "These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram." Here, WANG shows the processor that is part of the AI system that runs the conversational AI agent with a user. For more info, see WANG in [0057] and in [0096], [0113] for details.
Further, WANG teaches “and updating, by the one or more processors, the service profile according to the input,”
See WANG in [0063] describe " automatic updating of the OKB occurs in real-time through the use of ML algorithms or other automated techniques. For example, an AI system may continuously monitor user interactions and automatically update the OKB with new information learned from those interactions. The AI system may use natural language processing algorithms to extract relevant information from user inputs and use this information to update the OKB in real-time." From paragraph [0046] in the specification, the examiner construes a service profile to be a computer system storage element that contains context information regarding users and define data structures and algorithms, and is part of the server application. Here in reference [0063], WANG describe a system that monitors user interactions and then extracts relevant information from user inputs to automatically update the OKB (i.e. update the service profile) in real-time, which relates to updating the service profile according to the input. Note, the word ‘input’ is construed under broadest reasonable interpretation by the examiner to mean any type of information that is entered, extracted, or sent to a computer system. Further, see WANG in [0506] regarding the processor as described in the above limitation. See WANG in [0023], [0351] for additional details.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, and Weng, incorporate with the teachings of WANG by using the teachings of WU, Quarteroni, SINGARAJU, and Weng, of a system that takes user input to generate responses, and incorporate with WANG’s teaching of receiving an input of a service profile, and updating a service profile from the input.
One of ordinary skill in the art would be motivated to do so because by integrating WANG’s framework into the methods of WU, Quarteroni, SINGARAJU, and Weng, one with ordinary skill in the art would achieve the goal of providing “improved accuracy and efficiency in understanding contextual information in the environment and predicting the most relevant user intent and objective. The present invention aims to deliver an improved user experience by considering individual preferences, conversational history, and communication styles. The AI system adapts its responses according to a user’s past interaction or preferred tone, resulting in more effective and satisfying conversations.” (WANG, [0033-0034]).
Claim 5:
Regarding claim 5, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, teach the limitations of claim 1.
However, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, did not teach “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, input regarding a user profile: and updating, by the one or more processors, the user profile according to the input.”
In an analogous art, WANG teaches “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, input regarding a user profile: …,”
See WANG in [0353] describe "(1) Default assumptions: The AI system can rely on default assumptions or general user profiles to provide contextual information that is likely to be relevant to a broad range of users....(3) Real-time user input: The AI system can use real-time user input and interaction to understand the user’s immediate needs and intents, allowing it to adapt and provide relevant contextual information accordingly." Here, WANG mentions that the AI system (which includes a processor), receives user input in real time, including user profiles, to understand a user's immediate needs and intents (i.e. input regarding a user profile).
Further, see WANG in [0506] describe "These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks." Here, WANG shows the processor that is part of the AI system that runs the conversational AI agent with a user. For more info, see WANG in [0057] and in [0096], [0113] for details.
Further, WANG teaches “and updating, by the one or more processors, the user profile according to the input,”
See WANG in [0071] describe "In one embodiment, a user interacts with a conversational AI agent through a shared ride driver’s business card to request a ride from their current location to a destination. The conversational AI agent can use this interaction to update the OKB, adjusting the user’s preferences and attributes, such as their preferred car type, driver rating, and price range." Here, WANG shows that the AI agent updates the OKB, which is the object knowledge base, which contains information (such as a user's preferences and attributes that relates to a user profile) regarding each user the AI system interacts with. Note, the examiner construes user profile to include any information that is related to a user, such as preferences, activity, patterns that a user expresses. See WANG in [0428, 0478] for details. Further, see WANG in [0506] regarding the processor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, and Weng, with the teachings of WANG by using the teachings of WU, Quarteroni, SINGARAJU, and Weng,, of a system that takes user input to generate responses, and incorporate with WANG’s teaching of receiving an input regarding a user profile, and updating a user profile from the input.
One of ordinary skill in the art would be motivated to do so because by integrating WANG’s framework into the methods of WU, Quarteroni, SINGARAJU, and Weng, one with ordinary skill in the art would achieve the goal of providing “improved accuracy and efficiency in understanding contextual information in the environment and predicting the most relevant user intent and objective. The present invention aims to deliver an improved user experience by considering individual preferences, conversational history, and communication styles. The AI system adapts its responses according to a user’s past interaction or preferred tone, resulting in more effective and satisfying conversations.” (WANG, [0033-0034]).
Claim 7:
Regarding claim 7, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, teach the limitations of claim 1.
However, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, did not teach “The computer implemented method according to claim 1, further comprising defining, by the one or more processors, a data structure including relevant user data.”
In an analogous field, WANG teaches “The computer implemented method according to claim 1, further comprising defining, by the one or more processors, a data structure including relevant user data,”
See WANG in [0359-0360] mention " the CM method is applied to find the most relevant objects and context for the identified user with conversational capability. This process involves filtering and selecting objects based on rules and ML algorithms, allowing the AI system to create associations between the environment data and the objects that are most relevant to the user’s context. In the CM method, context can include various factors like user preferences, location, time, user interaction history, or any other information that can help in understanding the user’s intent or the situation accurately. In an approach for the Contextual Matching (CM) method involves several steps. First, contextual information is extracted by collecting and processing relevant data or content from the input or query. Next, the extracted contextual information is represented in a structured format, typically as feature vectors, for easy comparison and matching. Then, the similarity or relevance between the input or query and available data or content is calculated based on the contextual information, using various similarity measures or algorithms such as cosine similarity or Jaccard similarity." Here, WANG mentions data structures include feature vectors that allows for extraction of user data such as user preferences, user interaction history, and implements comparison and matching from input to a context. These provide information for the AI system that are most relevant to the user’s context to respond to the user’s query. Further, see WANG in [0506] regarding the processor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, and Weng, and incorporate with the teachings of WANG by using the teachings of WU, Quarteroni, SINGARAJU, and Weng, of a system that takes user input to generate responses, and incorporate with WANG’s teaching of defining a data structure including relevant user data.
One of ordinary skill in the art would be motivated to do so because by integrating WANG’s framework into the methods of WU, Quarteroni, SINGARAJU, and Weng, one with ordinary skill in the art would achieve the goal of providing “improved accuracy and efficiency in understanding contextual information in the environment and predicting the most relevant user intent and objective. The present invention aims to deliver an improved user experience by considering individual preferences, conversational history, and communication styles. The AI system adapts its responses according to a user’s past interaction or preferred tone, resulting in more effective and satisfying conversations.” (WANG, [0033-0034]).
Claim 9:
Regarding claim 9, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 8. Regarding claim 9, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Claim 11:
Regarding claim 11, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 8. Regarding claim 11, the claim recites similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Claim 12:
Regarding claim 12, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 8. Regarding claim 12, the claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Claim 14:
Regarding claim 14, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 8. Regarding claim 14, the claim recites similar limitations as corresponding claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Claim 16:
Regarding claim 16, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 15. Regarding claim 16, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Claim 18:
Regarding claim 18, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 15. Regarding claim 18, the claim recites similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Claim 19:
Regarding claim 19, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 15. Regarding claim 19, the claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 over WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, further in view of WANG, and further in view of Kaiss, W. et al. in “Effectiveness of an Adaptive Learning Chatbot on Students’ Learning Outcomes Based on Learning Styles,” published on July 7, 2023, available at https://online-journals.org/index.php/i-jet/article/view/39329/13571 , (hereafter, KAISS).
Claim 3:
Regarding claim 3, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, teach the limitations of claim 1.
Further, SINGARAJU teaches “and updating, by the one or more processors, a knowledge graph according to the classification,”
See SINGARAJU in [0113] mention "Named-entity recognition (also known as entity identification, entity chunking, or entity extraction) includes locating and classifying named entities in unstructured text into pre-defined categories, such as names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, and the like.” Here, SINGARAJU describes entities that can be classified into pre-defined categories. Further, see SINGARAJU in [0008] describe "...adding entities and links identified by the second set of entries to the seed graph to generate an updated customized knowledge graph." Here in [0008], SINGARAJU shows entities are added to update a knowledge graph.
Further, see SINGARAJU in [0077] mention “Entity resolver 216 may identify entities (e.g., objects) associated with the end user intents. For example, in addition to the end user intent identified by intent modeler 214, such as “order pizza,” entity resolver 216 may resolve entities associated with the intent, such as the pizza type, toppings, and the like.” Here, SINGARAJU shows that an entity is associated with users and can be used to connect an entity with a user. In [0113], SINGARAJU shows that entities (connected to users) are classified into categories, and since entities involve end user intents in [0077], this relates to classification of users as well, and this information is used to update a knowledge graph from [0008]. See SINGARAJU for more info in [0002].
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU and Quarteroni and incorporate into the teachings of SINGARAJU because all references teach a system that takes user inputs and generates responses for a user and update a knowledge graph according to the classification.
One of ordinary skill in the art would be motivated to do so because such a method provides a method that “improve intent classification in a chatbot based on knowledge graph embedding techniques” (SINGARAJU, [0003]), and “may allow for a more natural conversation between the bot and the end users for improved conversational experience. Instead of the end user learning a fixed set of keywords or commands that the bot knows how to respond to, an intelligent bot may be able to understand the end user's intention based upon user utterances in natural language and respond accordingly,” (SINGARAJU, [0040]).
However, WU, in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, did not teach:
• “The computer implemented method according to claim 1, further comprising: monitoring, by the one or more processors, computer interactions of the user:”
• “collecting data, by the one or more processors, from the monitored computer interactions;”
• “analyzing, by the one or more processors, the collected data according to defined dimensions;”
• “classifying, by the one or more processors, the user according to the analysis of the collected data;”
• “updating, by the one or more processors, a user profile according to the classification;”
In an analogous art, WANG teaches “The computer implemented method according to claim 1, further comprising: monitoring, by the one or more processors, computer interactions of the user:”
See WANG in [0064] describe "One way that an AI system can identify new information from continuously monitoring user interactions is through ML algorithms. These algorithms can analyze large amounts of data from user interactions and identify patterns and trends that can be used to update the OKB." Here, WANG shows that the AI system, monitors user interactions continuously through ML algorithms, and these are part of computer system.
Later, see WANG in [0506] describe "These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart". Here, WANG shows the computer system includes a processor described in WANG in [0506]. See WANG in figure 1 for details, where OKB stands for object knowledge base.
Further, WANG teaches “collecting data, by the one or more processors, from the monitored computer interactions;”
See WANG in [0055] describe " The AI system collects and stores data about the user’s interactions with the system in the OKB. By leveraging the data stored in the OKB, the AI system can adapt to the user’s preferences and behavior, creating a more seamless and intuitive conversational interaction." Here, WANG describes the AI system collects and stores data from the user’s interactions with the AI system. See WANG in [0064] for the monitored user interactions with the system and the processor from [0506] for details.
Further, WANG teaches “analyzing, by the one or more processors, the collected data according to defined dimensions;”
See WANG in [0506] describe "These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart". Here, WANG shows the computer system includes a processor described in WANG in [0506].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, and Weng, incorporate with the teachings of WANG by using the teachings of WU, Quarteroni, SINGARAJU, and Weng, of a system that takes user input to generate responses, and incorporate with WANG’s teaching of monitoring computer interactions with a user, and analyzing collected data according to defined dimensions.
One of ordinary skill in the art would be motivated to do so because by integrating WANG’s framework into the methods of WU, Quarteroni, SINGARAJU, and Weng, one with ordinary skill in the art would achieve the goal of providing “improved accuracy and efficiency in understanding contextual information in the environment and predicting the most relevant user intent and objective. The present invention aims to deliver an improved user experience by considering individual preferences, conversational history, and communication styles. The AI system adapts its responses according to a user’s past interaction or preferred tone, resulting in more effective and satisfying conversations.” (WANG, [0033-0034]).
However, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, and further in view of WANG, did not teach:
• “analyzing, by the one or more processors, the collected data according to defined dimensions;”
• “classifying, by the one or more processors, the user according to the analysis of the collected data;”
• “updating, by the one or more processors, a user profile according to the classification;”
In an analogous art, KAISS teaches “analyzing, by the one or more processors, the collected data according to defined dimensions;”
See KAISS in page 253, section 3. Architecture of Proposed Approach, last two paragraphs describe “Using the DialogFlow Framework to build our chatbot which will be integrated into the Moodle platform as a widget in an HTML block. During a conversation with the chatbot, the adaptive learning based on learners’ learning styles engine is launched on demand, which has access to the Moodle database and the learners’ database to generate a personalized list of learning objects recommended to the target learner according to his/her preferred learning style... in the case of a new user of Moodle, an Index of Learning Styles Questionnaire will be given to learners to determine their preferred learning styles and classify them to the appropriate style (visual, verbal, active, reflective,...) and the result obtained will be stored in the learner’s database which will be used by the Adaptive Learning Engine based Learning Style module to recommend learning objects appropriate to the style he/she prefers.” Here, KAISS explains that the data gathered (i.e. collected data) from the learning styles questionnaire will be used to classify learners into visual, verbal, active, reflective learning styles. Since the defined dimensions for reading levels from the specification [0020] is construed as a user assigned to any category based on their input to the QA system. Here, KAISS shows an analogous method to classify learners (users) based on their learning styles (i.e. defined dimensions) according to criteria under an Index of Learning Styles from Felder and Silverman’s model. See section 2.2 on page 252 for details on the model.
Further, KAISS teaches “classifying, by the one or more processors, the user according to the analysis of the collected data;”
See KAISS in page 252, section 2.2. Description of the Felder-Silverman learning style model describe "Indexing of Learning Styles (ILS) questionnaire, which consists of 44 questions that have been shown to be effective in identifying the learning style of each learner. The ILS provides a method for calculating percentage values of learning style attributes from the learner’s responses on the questionnaire," Here, KAISS shows giving a questionnaire to learners to identify their learning styles, and this shows classifying user based on a questionnaire (i.e. collected data). Further, see KAISS also mentions in page 253, section 3, last paragraph for details.
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Further, see KAISS in section 4. Effectiveness of Adaptive Learning Chatbot Based Learning Style, page 255, and figure 2 describe "Figure 2 shows the learning styles of the participants obtained after they answered the Index of Learning Styles Questionnaire [33] to determine their learning styles. In our experimentation, the learning styles that most participants belong to is the “visual learning style” at 51%, followed by the “verbal learning style” at 27%." Here, KAISS illustrates the results of the data collected from the questionnaire in categorizing users by their learning style. In an analogous art, KAISS uses the method of using various learning styles, and is applicable to users of various reading levels.
Further, KAISS teaches “updating, by the one or more processors, a user profile according to the classification;”
See KAISS in page 256, section 4. Effectiveness of Adaptive Learning Chatbot Based Learning Style, describe "The final objective of our chatbot is to allow it to identify and generate adaptive learning objects (from Moodle) according to the learner’s learning style. Figure 3 shows an example of learning object recommendation for the case where the learner is in visual learning style, for example, if he clicks on one of the provided recommendations, it will redirect him/her to the content in video format. Whereas for a learner with a verbal learning style, our chatbot recommends, for example as shown in Figure 4, learning objects in text format, PDF document, Powerpoint presentation." Here, KAISS shows updating the chatbot to generate adaptive learning objects according to the learner's learning style (of a user profile according to the classification, where classification is a learner's learning style). KAISS shows the chatbot recognizes a particular style of learner (i.e. user profile) and uses the information from the classification about that learner to provide recommendations specific to the learner. By recommending, the chatbot system also updates information about the current user to find customized responses for the user. Note the term ‘updating’ is construed by examiner to mean any change or action the system brings to a user.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, Weng, and WANG, and incorporate with the teachings of KAISS by using the teachings of WU, Quarteroni, SINGARAJU, Weng, and WANG of a system that takes user input to generate responses, and incorporate with the teaching of KAISS of classifying a user based on collected data and updating a user profile based on that classification.
One of ordinary skill in the art would be motivated to do so because by integrating the framework of KAISS into the methods of WU, Quarteroni, SINGARAJU, Weng, and WANG, one with ordinary skill in the art would achieve the goal of providing a method which “improved the learners’ learning outcomes compared to the pre-test results.... For the intermediate level, there was an improvement from 39% to 49%. For the advanced level, there is an increase from 0% to 6%,”(see KAISS page 258, second paragraph, part of section 4).
Claim 10:
Regarding claim 10, WU in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teach the limitations in claim 8. Regarding claim 10, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Claim 17:
Regarding claim 17, WU in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, teach the limitations in claim 15. Regarding claim 17, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 over WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, and further in view of Kim, Y. et al. (Pub. No. KR20230062268A), published on May 9, 2023, (hereafter, KIM), and further in view of KAISS.
Claim 6:
Regarding claim 6, WU in view of Quarteroni, further in view of SINGARAJU, and further in view of Weng, teach the limitations of claim 1.
Further, WU teaches “The computer implemented method according to claim 1, further comprising: … by the one or more processors,…”
See WU in [0027] describe " The system includes at least one processor and a memory. The memory encodes computer executable instruction that, when executed by the at least one processor, are operative to: collect user inputs from a group chat of a first user and a second user to form a collection." Here, WU teaches that this user input information is received by at least one processor.
However, WU in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, did not teach “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, input regarding a classification criteria: and updating, by the one or more processors, the classification criteria according to the input.”
In an analogous field, KIM teaches “The computer implemented method according to claim 1, further comprising: receiving, by the one or more processors, input regarding a classification criteria,”
See KIM in [0035] describe "the learner's reading comprehension level can be evaluated by the reading comprehension index … The creed index evaluation system includes a text input unit that receives a text source file related to Korean text; a text pre-processing unit generating text for analysis by performing pre-processing on Korean text according to the text source file; a vocabulary list database for storing a pre-constructed vocabulary list for use in Korean text analysis; It may include a text analysis unit that calculates a reading index of a pre-specified item representing a word level, a sentence level, a paragraph level, and a text level included in the text for analysis by applying the pre-established lexicon and a pre-specified text analysis criterion." Here, KIM teaches receiving an input, in this case a text input unit that receives text files, that calculates a reading index with a pre-specified text analysis criterion. See KIM in [0063] for details.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, and Weng, and incorporate with the teachings of KIM by using the teachings of WU, Quarteroni, SINGARAJU, and Weng, of a system that takes user input to generate responses, and incorporate with the teaching of KIM of receiving an input regarding a classification criteria.
One of ordinary skill in the art would be motivated to do so because by integrating the framework of KIM into the methods of WU, Quarteroni, SINGARAJU, and Weng, one with ordinary skill in the art would achieve the goal of providing a method that “ help to increase the level of reading comprehension, such as improving vocabulary,” (see KIM, [0024]), and which include “reading index books are recommended according to the measured reading index, such as the Creed index, and seek growth-type reading to improve vocabulary and reading skills to improve reading skills,” (KIM, [0054]).
However, WU, in view of Quarteroni, further in view of SINGARAJU, further in view of Weng, and further in view of KIM did not teach “and updating, by the one or more processors, the classification criteria according to the input,”
In an analogous field, KAISS teaches “and updating, by the one or more processors, the classification criteria according to the input,”
See KAISS in page 251, section 1. Introduction, describe “A chatbot is a new form of automated contextual communication between users and machines or systems, which exploits a conversational approach based on natural language. In [7] stated that the term “chatbot” refers to a software system, also called a conversational agent, as it interacts in turn with the user, through written messages.” Here, KAISS describes a chatbot is a conversational agent that speaks with a user through written messages.
Further, see KAISS in page 258, second paragraph, describe “This post-test evaluation shows that our proposed approach improved the learners’ learning outcomes compared to the pre-test results. Figure 7 shows the difference in learning outcomes between the pre-test and post-test. The beginner level decreased from 61% in the pre-test (10% medium beginner and 51% high beginner) to 45% in the post-test. For the intermediate level, there was an improvement from 39% to 49%. For the advanced level, there is an increase from 0% to 6%.” Here, KAISS describes the learners who took a test to see if their classification criteria, categorized according to their progress level as beginner, intermediate or advanced, changed after interaction with a chatbot (this interaction includes user inputs through written messages to the chatbot). KAISS mentions that the progress level of learners changed from before to after the learners used the chatbot, indicating an update in the classification criteria according to input. See KAISS in figures 5, 6, and 7 for details.
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the references of WU, Quarteroni, SINGARAJU, Weng, and KIM, and incorporate with the teachings of KAISS by using the teachings of WU, Quarteroni, SINGARAJU, Weng, and KIM of a system that takes user input to generate responses, and incorporate with the teaching of KAISS of updating classification criteria according to input.
One of ordinary skill in the art would be motivated to do so because by integrating the framework of KAISS into the methods of WU, Quarteroni, SINGARAJU, Weng, and KIM, one with ordinary skill in the art would achieve the goal of providing a method which “improved the learners’ learning outcomes compared to the pre-test results.... For the intermediate level, there was an improvement from 39% to 49%. For the advanced level, there is an increase from 0% to 6%,” (see KAISS page 258, second paragraph, part of section 4).
Claim 13:
Regarding claim 13, WU, in view of Quarteroni,, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 8. Regarding claim 13, the claim recites similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Claim 20:
Regarding claim 20, WU, in view of Quarteroni,, further in view of SINGARAJU, further in view of Weng, teaches the limitations in claim 15. Regarding claim 20, the claim recites similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/WenWei Zeng/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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