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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. PT117281, filed on 06/04/2021.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered.
Response to Arguments
Applicant’s argument filed 01/20/2026 have been fully considered but they are not persuasive. The amendments have overcome the claim interpretation under 35 U.S.C. § 112(f) and thus, the claim interpretation has been withdrawn.
Applicant’s Argument: On page 13-17 of Applicant’s response to rejections under 35 U.S.C. 101, applicant states the claims provide a specific technical solution that addresses the problem of degradation in quality of prediction over time for machine learning models. The claim elements recite the specific improvement of using a historical dataset along with a set of predicted answer categories to generate a set of fitted knowledge elements. The use of dataset associated with a time interval prevents the degradation in quality of prediction by a model over time without retraining.
Examiner’s Response: Applicant’s argument is not persuasive. During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement (see MPEP §2106.05(a)). The MPEP (§2106.05(a)(II)) also warns, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Here, the alleged improvement in the form of “using a historical dataset along with a set of predicted answer categories to generate a set of fitted knowledge elements” is an improvement to the abstract idea of a mental process that can be performed in the human mind.
An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome (see MPEP 2106.05(a)). The amended claims do not provide sufficient details to describe any technological improvement. If the specifications explicitly set forth an improvement but in a conclusory manner (see MPEP 2106.04(d)(1): a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. The claims recite “the at least one additional historical dataset being associated with a time interval” and under the broadest reasonable interpretation, does not limit the dataset to only recent answers that can be used for generating answers to information requests. Even if the historical dataset is being interpreted as data from a recent time interval, it is not clear how using recent answers always improves the performance of the machine learning model.
Applicant’s Argument: On page 17-19 of Applicant’s response to rejections under 35 U.S.C. 103, applicant states that none of the recited references teach a historical dataset being associated with a time interval.
Examiner’s Response: Applicant’s argument is not persuasive. Applicant’s arguments with respect to claims 1, 14, and 23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 23 recites the limitation “A computer-implemented method for predicting a set of fitted knowledge elements for preparation of answers ...“ and "generating, by using the knowledge element prediction fit machine learning model, a set of fitted knowledge elements for preparing the answer ...". It is unclear whether the claim limitation “a set of fitted knowledge elements” refers to the same or different set. Examiner interprets the claim limitation to refer to the same set of fitted knowledge elements. Thus, the second instance should be corrected to “the set of fitted knowledge elements for preparing the answer”.
Claims 2-13 are dependent claims of independent claim 1. Thus, the dependent claims are rejected on the same basis as the parent claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites “A computer-implemented method for predicting a set of fitted knowledge elements for preparation of answers to customer information requests, comprising:” and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
“generating, (i) the information request and (ii) a set of associated scores related to a set of respective relevance probabilities of the set of answer categories” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“estimating, based on the set of answer categories, a probability of use of each of a plurality of knowledge elements in preparing an answer to the information request” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“generating, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“obtaining the answer based on the set of fitted knowledge elements” (a mental process that can be performed in the human mind, i.e. judgement)
“wherein estimating the probability of use comprises processing,
and for generating the set of fitted knowledge elements: the set of answer categories, a first historical dataset, set of answer categories, and at least one additional historical dataset comprising one or more of a second historical dataset of answers, a third historical dataset of knowledge elements used in preparation of the answers or a fourth historical dataset of external feedback, the at least one additional historical dataset being associated with a time interval” (a mental process that can be performed in the human mind, i.e. judgement)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
“receiving, from a computing device of a customer, an information request” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“generating, by using an intermediate predictive machine-learning model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“storing the set of answer categories in a computational memory” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“estimating, by using a knowledge element prediction fit machine-learning model ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“generating, by using the knowledge element prediction fit machine learning model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“transmitting, to the computing device of the customer, the answer” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“
knowledge element prediction fit machine-learning model machine-learning model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
“receiving, from a computing device of a customer, an information request” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“generating, by using an intermediate predictive machine-learning model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“storing the set of answer categories in a computational memory” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of storing and retrieving information in memory as identified by the court - see MPEP 2106.05(d))
“estimating, by using a knowledge element prediction fit machine-learning model ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“generating, by using the knowledge element prediction fit machine learning model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“transmitting, to the computing device of the customer, the answer” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“
knowledge element prediction fit machine-learning model machine-learning model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“further comprising pairing each answer category of the set of answer categories with a respective element of a feedback set relating to the answer” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the pairing is based on an identification code of the information request” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein estimating the probability of use comprises processing, the first historical dataset, the second historical dataset of answers, the third historical dataset of knowledge elements used in the preparation of the answers, and the fourth historical dataset of external feedback, wherein the fourth historical dataset of external feedback is associated with the time interval” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“ processing, by the knowledge element prediction fit machine-learning model:
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein estimating the probability of use comprises processing, the first historical dataset, the second historical dataset of answers, the third historical dataset of knowledge elements used in the preparation of the answers, the fourth historical dataset of external feedback, wherein the fourth historical dataset of external feedback is related to a series of previously defined time intervals of information requests” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“ processing, by the knowledge element prediction fit machine-learning model:
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“estimate at least one conditional probability of use of a knowledge base element” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“classifya first class” (a mental process that can be performed in the human mind, i.e. judgement)
“the at least one conditional probability is estimated based on a temporal sample of historical data relating to a set of information requests ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“the knowledge element prediction fit machine-learning model is configured to estimate ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“the intermediate predictive machine-learning model is configured to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“estimate at least one conditional probability of use of a knowledge base element” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“classifya first class” (a mental process that can be performed in the human mind, i.e. judgement)
“the at least one conditional probability is updated based on samples of historical data collected at regular time intervals” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“the knowledge element prediction fit machine-learning model is configured to estimate ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“the intermediate predictive machine-learning model is configured to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein estimating the probability further comprises estimatingat least one conditional probability by calculating a simple moving average, a weighted moving average or an exponential moving average” (a mathematical calculation)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“by using the knowledge element prediction fit machine-learning model” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“further comprising obtaining, from a conversational robot agent acting as an answering agent, a feedback relating to the answer, the feedback comprising: the answer, one or more knowledge elements used in generating the answer, and an external feedback related to relevance of the answer to the information request” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“further comprising obtaining, from a human agent, a feedback relating to the answer, the feedback comprising: the answer, one or more knowledge elements used in generating the answer, and an external feedback related to relevance of the answer to the information request” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“a plurality of available knowledge elements to generate one or more of: the set of answer categories or the set of fitted knowledge elements and the set of respective probabilities of use” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein at least one of the intermediate predictive machine-learning model or the knowledge element prediction fit machine-learning model is configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“the answer and a plurality of available knowledge elements, to extrapolate one or more knowledge elements used in generating the answer” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“further comprising processing by using a knowledge element extrapolation module,” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“a first historical pair of information request and answer higher than a second historical pair of information request and answer” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“the knowledge element prediction fit machine-learning model is configured to weight ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“the first historical pair is at least one of: associated with a more recent information request than the second historical pair, associated with a higher level of relevance between information request and answer, or obtained from a human agent” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 1:
Claim 14 recites “A predictive system for preparation of answers to information requests, comprising” and is thus a system, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
“analyze an information request” (a mental process that can be performed in the human mind, i.e. judgement)
“generate a set of answer categories related to (i) the information request and (ii) a set of associated scores related to a set of respective relevance probabilities of the set of answer categories” (a mental process that can be performed in the human mind, i.e. judgement)
“a probability of use of a knowledge element in preparation of an answer to the information request and generate a set of fitted knowledge elements for the preparation of the answer by processing: the set of answer categories; a first historical dataset predicted by the intermediate predictive machine-learning model relating to the set of answer categories; and at least one additional historical dataset comprising one or more of a second historical dataset of answers, a third historical dataset of knowledge elements used in preparation of the answers or a fourth historical dataset of external feedback, the at least one additional historical dataset associated with a time interval” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Claim 14 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
“an intermediate predictive machine-learning model configured to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a knowledge element prediction fit machine-learning model configured to: ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"a processor configured to send the answer to a computing device of a customer” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 14 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
“an intermediate predictive machine-learning model configured to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a knowledge element prediction fit machine-learning model configured to: ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
" a processor configured to send the answer to a computing device of a customer” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 14 is subject-matter ineligible.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“further comprising at least one memory, and at least one communication interface with a communication channel” (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the communication channel comprises at least one communication network comprising one or more of a public network, an interconnected set of at least one of a public or a private network, or a private network” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the intermediate predictive machine-learning model and the knowledge element prediction fit machine-learning model are installed in a unit comprising one or more of one or more servers or one or more computing devices” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the intermediate predictive machine-learning model and the knowledge element prediction fit machine-learning model are installed in a storage unit comprising one or more of one or more servers, one or more computing devices, or one or more programmable integrated circuits” (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“a plurality of available knowledge elements to generate the set of answer categories and the set of fitted knowledge elements” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein at least one of the intermediate predictive machine-learning model or the knowledge element prediction fit machine-learning model is configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“each answer category of the set of answer categories with a respective element of a feedback set relating to the answer” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the processor is further configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 21:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“the answer and a plurality of available knowledge elements, in order to extrapolate one or more knowledge elements used in generating the answer” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“a knowledge element extrapolation module configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 22:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the knowledge element extrapolation module is integrated into the knowledge element prediction fit machine-learning model” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 23:
The claim recites an article of manufacture that performs the method as described in claim 1. Therefore, claim 23 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 23 are analyzed below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7 and 9-23 are rejected under 35 U.S.C. 103 as being unpatentable over Rajagopal (US20200227026A1) in view of Xue (US11429834B1).
Regarding claim 1, Rajagopal teaches:
“A computer-implemented method for predicting a set of fitted knowledge elements for preparation of answers to customer information requests, comprising:” (abstract, A topic build uses cluster predictions to generate and identify a list of topics and subtopics for providing an answer to a user query.)
“receiving, from a computing device of a customer, an information request” ([0038, 0046, 0058-0059], The system receives the user utterances. The question and answer engine may be implemented on a server and the user utterances are submitted via an external chatbot program from a user device via the network.)
“generating, by using an intermediate predictive machine-learning model, a set of answer categories that are related to (i) the information request and (ii) a set of associated scores related to a set of respective relevance probabilities of the set of answer categories” ([0035-0038, 0040-0041], A predictive clustering model can be used to determine the topic and subtopic that is associated with the user’s utterance. The user’s utterance may further be processed by a similarity scoring module. A similarity scorer module uses a multi-step analysis to associate the sorted topics and subtopics (answer categories) with an utterance list (information request). A similar utterance list contains keywords that are weighted based on term frequency-inverse document frequency vectorization associated with different topics and subtopics. For example, a customer may submit the question “How do I switch to the easy-online rate?” and the similarity scorer determines that the related categories of topic is “rate” and “change”. A cosine vector scorer may be used to calculate a score for the predicted category to find a list of similar questions.)
“storing the set of answer categories in a computational memory” ([0035, 0061-0062], The similar utterance list may be used to update the historical utterance list and the updates may further be provided to the master question and answer database for storage. The system contains a memory component to store information by the different modules.)
“estimating, by using a knowledge element prediction fit machine-learning model and based on the set of answer categories, a probability of use of each of a plurality of knowledge elements in preparing an answer to the information request” ([0042-0043], The recommender model uses a latent dirichlet allocation model to determine topics from the document and each category of the document may determine the term frequency - inverse document frequency, which measures the importance of the topic in the document. The model computes the probability distribution of topics within documents and the probability distribution of words within topics. The recommender provides a list of documents that is similar to the user’s questions. The process may be performed for each document in the collection of documents.)
“generating, by using the knowledge element prediction fit machine learning model, a set of fitted knowledge elements for preparing the answer and a set of respective probabilities of use associated with the set of fitted knowledge elements” ([0042-0043], A genism prediction model is used to determine which resource is best suited to answer the question of a particular category and the resource is recommended for use in responding to the user’s question. One document from the list of documents may be selected to be provided as the answer.)
“obtaining the answer based on the set of fitted knowledge elements” ([0041, 0051], The recommender may identify the closest match for the category or subcategory of the user’s question and determine the most popular questions related to the category. The corresponding answers are provided to the user.)
“transmitting, to the computing device of the customer, the answer” ([0054-0055, 0059-0061], A chatbot may be a virtual assistant system on a server that communicates with the user device through application client interface. The chatbot may provide the answer to the user. The answer may be an electronic resource or a most frequently asked question with its corresponding answer.)
“wherein estimating the probability of use comprises processing, by the knowledge element prediction fit machine-learning model and for generating the set of fitted knowledge elements: the set of answer categories, a first historical dataset, predicted by the intermediate predictive machine-learning model, relating to the set of answer categories, and at least one additional historical dataset comprising one or more of a second historical dataset of answers, a third historical dataset of knowledge elements used in preparation of the answers or a fourth historical dataset of external feedback, ” ([0040-0041, 0042, 0050, 0055], The recommender model may use the similarity scoring module to identify the closest match for the category and the subcategory (answer categories) of the user’s question. Once the category and the subcategory are identified by the recommender, the most popular questions and answers are retrieved from the master question and answer database (first historical dataset) to provide to the user as a response. In some embodiment, the user utterance may be determined to be novel relative to the information in the master question and answer database and a document may be retrieved from an electronic resource library (one additional dataset) consisting of resources labeled with category and subcategory labels. The proposed system is trained on historical chat logs and an analytic data collector can process the chat logs to generate aliases for each question. It is implied that these historical chat logs may contain time stamps but it is not explicitly disclosed. Xue reference is included to disclose historical dataset associated with a time to generate answers for user’s questions.)
Rajagopal does not explicitly disclose an implementation of “the at least one additional historical dataset being associated with a time interval”. However, Xue discloses in the same field of endeavor:
“wherein estimating the probability of use comprises processing, by the knowledge element prediction fit machine-learning model and for generating the set of fitted knowledge elements: ... a fourth historical dataset of external feedback, the at least one additional historical dataset being associated with a time interval” ([col. 4, lines 51-54; col. 7, lines 1-38; col. 10, lines 35-41], Historical support texts with known answers may be provided to customer support agent to answer a new user question. User feedback for the provided best answer may be obtained to further refine the answer prediction model. The user feedback may be in the form of labeled support texts, where the label indicates that the support text was a useful answer. Support texts are indexed with timestamps so that questions and answers can be related to one another.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “the at least one additional historical dataset being associated with a time interval” from Xue into the teaching of Rajagopal. Doing so can provide continuous improvement of answer prediction models by re-training the models based on user feedback (Xue, col. 7, lines 27-38).
Regarding claim 2, Rajagopal teaches:
“further comprising pairing each answer category of the set of answer categories with a respective element of a feedback set relating to the answer” ([0037, 0044], An analytic collector may collect data related to the results from both the predictor and the recommender. The collected data may identify the most popular clusters for each category and a question asked count (feedback) based on the frequency of a particular type of question that has been asked. The determination may be computed based on number of times the predictor and sentiment classifier module generate hits. The question asked count is associated with the type of question and stored in memory. Categories and list of similar questions are associated by a score generated using a cosine vector scorer.)
Regarding claim 3, Rajagopal teaches:
“wherein the pairing is based on an identification code of the information request” ([0044, 0057], A user utterance may be associated with a question alias (identification code) and the alias may be used to retrieve a list of answers from topic and subtopic associated with the question.)
Regarding claim 4, Rajagopal teaches:
“wherein estimating the probability of use comprises processing, by the knowledge element prediction fit machine-learning model: the first historical dataset, the second historical dataset of answers, the third historical dataset of knowledge elements used in the preparation of the answers, and the fourth historical dataset of external feedback, The recommender model may use the similarity scoring module to identify the closest match for the category and the subcategory of the user’s question using a historical utterance list (first historical dataset). Once the category and the subcategory are identified by the recommender, the most popular questions and answers are retrieved from the master question and answer database (second historical dataset) to provide to the user as a response. In some embodiment, the user utterance may be determined to be novel relative to the information in the master question and answer database and a document may be retrieved from an electronic resource library (third historical dataset) consisting of resources labeled with category and subcategory labels. An analytic data collector that is separate from the recommender model can analyze historical chat logs and generate a question alias list (fourth historical dataset).)
Rajagopal does not explicitly disclose an implementation of “wherein the fourth historical dataset of external feedback is associated with the time interval”. However, Xue discloses in the same field of endeavor:
“... wherein the fourth historical dataset of external feedback is associated with the time interval” ([col. 4, lines 51-54; col. 7, lines 1-38; col. 10, lines 35-41], Historical support texts with known answers may be provided to customer support agent to answer a new user question. User feedback for the provided best answer may be obtained to further refine the answer prediction model. The user feedback may be in the form of labeled support texts, where the label indicates that the support text was a useful answer. Support texts are indexed with timestamps so that questions and answers can be related to one another.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “wherein the fourth historical dataset of external feedback is associated with the time interval” from Xue into the teaching of Rajagopal. Doing so can provide continuous improvement of answer prediction models by re-training the models based on user feedback (Xue, col. 7, lines 27-38).
Regarding claim 5, Rajagopal teaches:
“wherein estimating the probability of use comprises processing, by the knowledge element prediction fit machine-learning model: the first historical dataset, the second historical dataset of answers, the third historical dataset of knowledge elements used in the preparation of the answers, the fourth historical dataset of external feedback, The recommender model may use the similarity scoring module to identify the closest match for the category and the subcategory of the user’s question using a historical utterance list (first historical dataset). Once the category and the subcategory are identified by the recommender, the most popular questions and answers are retrieved from the master question and answer database (second historical dataset) to provide to the user as a response. In some embodiment, the user utterance may be determined to be novel relative to the information in the master question and answer database and a document may be retrieved from an electronic resource library (third historical dataset) consisting of resources labeled with category and subcategory labels. An analytic data collector that is separate from the recommender model can analyze historical chat logs and generate a question alias list (fourth historical dataset).)
Rajagopal does not explicitly disclose an implementation of “wherein the fourth historical dataset of external feedback is related to a series of previously defined time intervals of information requests”. However, Xue discloses in the same field of endeavor:
“... wherein the fourth historical dataset of external feedback is related to a series of previously defined time intervals of information requests” ([col. 4, lines 51-54; col. 7, lines 1-38; col. 10, lines 35-41], Historical support texts with known answers may be provided to customer support agent to answer a new user question. Support texts consist of questions previously asked by customer support agents. The user feedback may be in the form of labeled support texts, where the label indicates that the support text was a useful answer. Support texts are indexed with timestamps so that questions and answers can be related to one another.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “wherein the fourth historical dataset of external feedback is related to a series of previously defined time intervals of information requests” from Xue into the teaching of Rajagopal. Doing so can provide continuous improvement of answer prediction models by re-training the models based on user feedback (Xue, col. 7, lines 27-38).
Regarding claim 6, Rajagopal teaches:
“the knowledge element prediction fit machine-learning model is configured to estimate at least one conditional probability of use of a knowledge base element” ([0042], The recommender provides specific documents and may use a latent dirichlet allocation model to generate the list of recommended documents. A latent dirichlet allocation model is a conditional probability model. The model computes the probability distribution of words within topics.)
“the intermediate predictive machine-learning model is configured to classifya first class” ([0039], A sentiment classifier determines if the user utterance is an issue, a request, or a question (class).)
“the at least one conditional probability is estimated based on a temporal sample of historical data relating to a set of information requests indexed in the computational memory” ([0028, 0042, 0058, 0061], The list of recommended documents is based on categories identified by the topic builder module, which takes a historical utterance list to generate the categories. The categories may be stored in memory. The historical user utterance are questions that have been previously asked by users. Xue (col. 4, lines 51-54) further teaches historical data can be associated with timestamps.)
Regarding claim 7, Rajagopal teaches:
“the knowledge element prediction fit machine-learning model is configured to estimate at least one conditional probability of use of a knowledge base element” ([0042], The recommender provides specific documents and may use a latent dirichlet allocation model to generate the list of recommended documents. A latent dirichlet allocation model is a conditional probability model. The model computes the probability distribution of words within topics.)
“the intermediate predictive machine-learning model is configured to classifya first class” ([0039], A sentiment classifier determines if the user utterance is an issue, a request, or a question (class).)
“the at least one conditional probability is updated based on samples of historical data collected at regular time intervals” ([0028, 0042], The list of recommended documents is based on categories identified by the topic builder module, which takes a historical utterance list to generate the categories. The categories may be stored in memory. The model is dynamic and can be retrained over time as new documents or other electronic resources with category/subcategory labels are added to the resource library or folder.)
Regarding claim 9, Rajagopal teaches:
“further comprising obtaining, from a conversational robot agent acting as an answering agent, a feedback relating to the answer, the feedback comprising: the answer, one or more knowledge elements used in generating the answer, and an external feedback related to relevance of the answer to the information request” ([0043-0044, 0050, 0058-0059], In some embodiment, the framework may be implemented into a FAQ-chatbot system where the chatbot is an automated agent that provides the best answers based on a user’s question. The proposed system includes an analytic data collector and trainer module to analyze chat logs. It is implied that the chat logs are collected from the FAQ-chatbot. The analytic data collector may continuously collect real-time data and determine the frequency that a particular question has been asked based on the collected chat logs. Each question is associated with an answer in the master question and answer database. Additionally, a prediction model can determine a document that can be used in responding to the user’s question and provided to the user through the chatbot interface. Xue (col. 7, lines 27-38) further discloses collecting user feedback from the support interface based on the best answer.)
Regarding claim 10, Rajagopal teaches:
“further comprising obtaining, from a human agent, a feedback relating to the answer, the feedback comprising: the answer, one or more knowledge elements used in generating the answer, and an external feedback related to relevance of the answer to the information request” ([0032, 0043-0044, 0050, 0058-0059], The proposed system can integrate the services of virtual agents and human agents and allow virtual chatbots to work seamlessly with human agents, such that the chatbot can ‘hand-off’ the conversation with a customer when appropriate. For example, when the chatbot is unable to answer the customer's question or address the issue or request, it can trigger an automatic and immediate redirection of the customer to a human agent. The human agent provides the user with additional support in answering their concern. Xue (col. 7, lines 27-38) further discloses collecting user feedback from the support interface based on the best answer.)
Regarding claim 11, Rajagopal teaches:
“wherein at least one of the intermediate predictive machine-learning model or the knowledge element prediction fit machine-learning model is configured to access a plurality of available knowledge elements to generate one or more of: the set of answer categories or the set of fitted knowledge elements and the set of respective probabilities of use” ([0030-0033, 0038, 0043], A topic builder may cluster words from historical data to form different topics and subtopic. A priority matrix may be further created to help sort between topics and subtopics by assigning priorities to the words. The system receives the user utterance and implements a predictive clustering model to associate the user utterance with a topic or subtopic that closely aligns with the information. A genism prediction model is used to determine which resource is best suited to answer the question of a particular category and the resource is recommended for use in responding to the user’s question. One document from the list of documents may be selected to be provided as the answer.)
Regarding claim 12, Rajagopal teaches:
“further comprising processing by using a knowledge element extrapolation module, the answer and a plurality of available knowledge elements, to extrapolate one or more knowledge elements used in generating the answer” ([0030, 0033-0034], A topic builder is implemented to cluster historical user utterance into topics and subtopics. The results of the topic builder are provided to the question and answer builder. The data from the master question and answer database is sorted into appropriate topic and subtopic. Each topic is associated with a plurality of questions and its corresponding answer in generating an answer for a user’s utterance.)
Regarding claim 13, Rajagopal teaches:
“the knowledge element prediction fit machine-learning model is configured to weight a first historical pair of information request and answer higher than a second historical pair of information request and answer” ([0040-0041, 0044], Each question and answer in the master database may be associated with a question asked count that indicates the frequency that the particular question has been asked. When the user’s question is not found, the recommender model uses the similarity scoring module to identify the closest match for the category and the subcategory. The most popular questions and corresponding answers are provided to the user as a user’s answer. A question-and-answer pair with a higher question asked count is weighed higher than a question-and-answer pair with a lower count.)
“the first historical pair is at least one of: associated with a more recent information request than the second historical pair, associated with a higher level of relevance between information request and answer, or obtained from a human agent” ([0040-0041, 0044], A count is assigned to the frequency asked questions and topics. The question asked count updates the master database and the predictor. The most frequently asked questions and corresponding answers with a higher count is more relevant than other pairs with a lower count. The claim limitation only requires at least one of the 3 recited conditions to be satisfied.)
Regarding claim 14, Rajagopal teaches:
“A predictive system for the preparation of answers to information requests, comprising” (abstract, A topic build uses cluster predictions to generate and identify a list of topics and subtopics for providing an answer to a user query.)
“an intermediate predictive machine-learning model configured to, analyze an information request” ([0035-0037, 0040-0041], A similarity scorer module uses a multi-step analysis to associate the sorted topics and subtopics with an utterance list. A similar utterance list contains keywords that are weighted based on term frequency-inverse document frequency vectorization associated with different topics and subtopics. A sentiment classifier determines a user’s utterance includes a question.)
“generate a set of answer categories related to (i) the information request and (ii) a set of associated scores related to a set of respective relevance probabilities of the set of answer categories” ([0035-0038, 0040-0041], A predictive clustering model can be used to determine the topic and subtopic that is associated with the user’s utterance. The user’s utterance may further be processed by a similarity scoring module. A similarity scorer module uses a multi-step analysis to associate the sorted topics and subtopics (answer categories) with an utterance list (information request). A similar utterance list contains keywords that are weighted based on term frequency-inverse document frequency vectorization associated with different topics and subtopics. For example, a customer may submit the question “How do I switch to the easy-online rate?” and the similarity scorer determines that the related categories of topic is “rate” and “change”. A cosine vector scorer may be used to calculate a score for the predicted category to find a list of similar questions.)
“a knowledge element prediction fit machine-learning model configured to: estimate a probability of use of a knowledge element in preparation of an answer to the information request” ([0042-0043], The recommender model uses a latent dirichlet allocation model to determine topics from the document and each category of the document may determine the term frequency - inverse document frequency, which measures the importance of the topic in the document. The model computes the probability distribution of topics within documents and the probability distribution of words within topics. The recommender provides a list of documents that is similar to the user’s questions. The process may be performed for each document in the collection of documents.)
“generate a set of fitted knowledge elements for the preparation of the answer by processing: the set of answer categories; a first historical dataset predicted by the intermediate predictive machine-learning model relating to the set of answer categories; and at least one additional historical dataset comprising one or more of a second historical dataset of answers, a third historical dataset of knowledge elements used in preparation of the answers or a fourth historical dataset of external feedback, ” ([0040-0041, 0042, 0050, 0055], The recommender model may use the similarity scoring module to identify the closest match for the category and the subcategory (answer categories) of the user’s question. Once the category and the subcategory are identified by the recommender, the most popular questions and answers are retrieved from the master question and answer database (first historical dataset) to provide to the user as a response. In some embodiment, the user utterance may be determined to be novel relative to the information in the master question and answer database and a document may be retrieved from an electronic resource library (one additional dataset) consisting of resources labeled with category and subcategory labels. The proposed system is trained on historical chat logs and an analytic data collector can process the chat logs to generate aliases for each question. It is implied that these historical chat logs may contain time stamps but it is not explicitly disclosed. Xue reference is included to disclose historical dataset associated with a time to generate answers for user’s questions.)
“a processor configured to send the answer to a computing device of a customer” ([0054-0055, 0059-0061], A chatbot may be a virtual assistant system on a server that communicates with the user device through application client interface. The chatbot may provide the answer to the user. The answer may be an electronic resource or a most frequently asked question with its corresponding answer.)
Rajagopal does not explicitly disclose an implementation of “the at least one additional historical dataset being associated with a time interval”. However, Xue discloses in the same field of endeavor:
“generate a set of fitted knowledge elements for the preparation of the answer by processing: ... at least one additional historical dataset comprising one or more of a second historical dataset of answers, a third historical dataset of knowledge elements used in preparation of the answers or a fourth historical dataset of external feedback, the at least one additional historical dataset associated with a time interval” ([col. 4, lines 51-54; col. 7, lines 1-38; col. 10, lines 35-41], Historical support texts with known answers may be provided to customer support agent to answer a new user question. User feedback for the provided best answer may be obtained to further refine the answer prediction model. The user feedback may be in the form of labeled support texts, where the label indicates that the support text was a useful answer. Support texts are indexed with timestamps so that questions and answers can be related to one another.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “the at least one additional historical dataset being associated with a time interval” from Xue into the teaching of Rajagopal. Doing so can provide continuous improvement of answer prediction models by re-training the models based on user feedback (Xue, col. 7, lines 27-38).
Regarding claim 15, Rajagopal teaches:
“further comprising at least one memory, and at least one communication interface with a communication channel” ([0046], The system consist of a processor, a memory, and a network component for communication.)
Regarding claim 16, Rajagopal teaches:
“wherein the communication channel comprises at least one communication network comprising one or more of a public network, an interconnected set of at least one of a public or a private network, or a private network” ([0046], The system consists of a processor, a memory, and a network component for communication. The network may be a local area network.)
Regarding claim 17, Rajagopal teaches:
“wherein the intermediate predictive machine-learning model and the knowledge element prediction fit machine-learning model are installed in a unit comprising one or more of one or more servers or one or more computing devices” ([0046, 0061], The system may be installed on a server on an internal network.)
Regarding claim 18, Rajagopal teaches:
“wherein the intermediate predictive machine-learning model and the knowledge element prediction fit machine-learning model are installed in a storage unit comprising one or more of one or more servers, one or more computing devices, or one or more programmable integrated circuits” ([0046, 0061-0062], The system may be installed on a server on an internal network. Devices may contain an application-specific integrated circuit.)
Regarding claim 19, Rajagopal teaches:
“wherein at least one of the intermediate predictive machine-learning model or the knowledge element prediction fit machine-learning model is configured to access a plurality of available knowledge elements to generate the set of answer categories and the set of fitted knowledge elements” ([0030-0033, 0038, 0042, 0061], A topic builder may cluster words to form different topics and subtopic. A priority matrix may be further created to help sort between topics and subtopics by assigning priorities to the words (associated with scores). The system receives the user utterance and implements a predictive clustering model to associate the user utterance with a topic or subtopic that closely aligns with the information. User utterance may come from a user device and communicated to the server platform for processing. The recommender model uses a latent dirichlet allocation model to determine topics from the document and the topics are compared to the categories from the question to determine a match. The recommender provides a list of documents that is similar to the user’s questions.)
Regarding claim 20, Rajagopal teaches:
“wherein the processor is further configured to pair each answer category of the set of answer categories with a respective element of a feedback set relating to the answer” ([0044, 0061], An analytic collector may collect data related to the results from both the predictor and the recommender. The collected data may identify the most popular clusters for each category and a question asked count based on the frequency of a particular question that has been asked. The determination may be computed based on number of times the predictor and sentiment classifier module generate hits.)
Regarding claim 21, Rajagopal teaches:
“a knowledge element extrapolation module configured to process the answer and a plurality of available knowledge elements, in order to extrapolate one or more knowledge elements used in generating the answer” ([0030, 0033-0034], A topic builder is implemented to cluster historical user utterance into topics and subtopics. The results of the topic builder are provided to the question and answer builder. The data from the master question and answer database is sorted into appropriate topic and subtopic. Each topic is associated with a plurality of questions and its corresponding answer in generating an answer for a user’s utterance.)
Regarding claim 22, Rajagopal in view of Xue teaches:
“wherein the knowledge element extrapolation module is integrated into the knowledge element prediction fit machine-learning model” ([Xue, col. 9, lines 5-67, col. 10, lines 1-34, Figure 4], A two-stage answer prediction model is disclosed to generate one or more best answers to a customer support agent. Two separate internal modules are used to define the similarity between the support texts and query.)
Regarding claim 23:
Claim 23 recites an article of manufacture that performs the same process as described in Claim 1. Therefore claim 23 is rejected under the same reasons mention for claim 1. The additional elements of claim 23 is addressed below by Rajagopal:
“A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to” ([abstract, 0066], A topic build uses cluster predictions to generate and identify a list of topics and subtopics for providing an answer to a user query. The device may execute software instructions stored by a non-transitory computer-readable medium.)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Rajagopal (US20200227026A1) in view of Xue (US11429834B1) and Pliner (US20150065173A1).
Regarding claim 8, Rajagopal in view of Xue teaches:
“wherein estimating the probability further comprises estimating, by using the knowledge element prediction fit machine-learning model, the at least one conditional probability ” ([Rajagopal, 0042], The recommender provides specific documents and may use a latent dirichlet allocation model to generate the list of recommended documents. A latent dirichlet allocation model is a conditional probability model. The model computes the probability distribution of words within topics.)
Rajagopal in view of Xue does not explicitly disclose an implementation of “calculating a simple moving average, a weighted moving average or an exponential moving average”. However, Pliner discloses in the same field of endeavor:
“wherein estimating the probability further comprises estimating, by using the at least one conditional probability by calculating a simple moving average, a weighted moving average or an exponential moving average” ([0079], A moving average may be used to smooth out short-term fluctuations and emphasize longer term trends in the probability estimates over the predetermined time period.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “calculating a simple moving average, a weighted moving average or an exponential moving average” from Pliner into the teaching of Rajagopal in view of Xue. Doing so can provide better probability estimates by including time intervals (Pliner, par. 78-79).
Conclusion
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/GARY MAC/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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