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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
Claim 8 limitations that invoke 112(f):
“a question obtaining module configured to obtain a user question;”
“a conversion module configured to convert”
“a similarity vector determining module configured to determine the user question embedding vector and a first similarity vector”
“a candidate node determining module configured to determine a candidate node set”
“an optimally-matched node determining module configured to determine an optimally-matched node set”
“a knowledge extraction module configured to determine, based on the optimally-matched node set, file knowledge content corresponding to the user question” The specification in paragraph [0015] “An electronic device includes a memory and a processor, where the memory is configured to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above cross-file question and answer knowledge extraction method” provides the structure to implement the sections above
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) significantly more. The subject matter eligibility test for products and process is describe below for claim 1 in view of dependent claims.
Regarding claim 1:
Step 1: Is the claim to a process machine manufacture or composition of matter?
Yes – Claim 1 recites a method, which is a method that falls under the statutory categories.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“converting the user question into a user question embedding vector by using an embedding function;” - The limitations recites a mathematical function for converting a user question into a vector embedding (see MPEP 2106.04(a)(2)I).
“determining the user question embedding vector and a first similarity vector of a root node of a file embedding vector tree of each professional knowledge file,” - The limitations recites a mental process of selecting a determining the user question embedding vector and a first similarity vector of the root node (see MPEP 2106.04(a)(2)III).
“the first similarity vector is a product of a vector corresponding to a maximum inner product value of each root node and the user question embedding vector;” - The limitations recites a mathematical process of maximum inner product that is used to create the first similarity vector (see MPEP 2106.04(a)(2)I).
“the maximum inner product value is a maximum value among an inner product of the user question embedding vector and each of the main title embedding vector, the average embedding vector, and the abstract embedding vector;” - The limitations recites a mathematical process of maximum inner product that being the maximum value among an inner product (see MPEP 2106.04(a)(2)I).
“determining an optimally-matched node set based on the candidate node set, wherein the optimally-matched node set is a subset corresponding to a maximum element sum in the candidate node set;” - The limitations recites a mental process of determining an optimally-matched node set based on the candidate node set (see MPEP 2106.04(a)(2)III).
“the maximum element sum is a maximum value of a first element sum, a second element sum, a third element sum, and a fourth element sum;” - The limitations recites a mathematical process calculating the maximum value of a first element sum (see MPEP 2106.04(a)(2)I).
“the first element sum is an element sum of a first average similarity vector of the candidate node subset for the cross-file structural question and answer knowledge;“ - The limitations recites a mathematical addition for a first element sum (see MPEP 2106.04(a)(2)I).
“the first average similarity vector is an average value of similarity vectors of all nodes in the candidate node subset for the cross-file structural question and answer knowledge;” - The limitations recites a mathematical process of determining an average that is the first average similarity vector (see MPEP 2106.04(a)(2)I).
“the second element sum is an element sum of a second average similarity vector of the candidate node subset for the cross-file paragraph question and answer knowledge; “ - The limitations recites a mathematical addition for a second element sum (see MPEP 2106.04(a)(2)I).
“the second average similarity vector is an average value of similarity vectors of all nodes in the candidate node subset for the cross-file paragraph question and answer knowledge;” - The limitations recites a mathematical process of determining an average that is the second average similarity vector (see MPEP 2106.04(a)(2)I).
“the third element sum is an element sum of a third average similarity vector of the candidate node subset for the single-file structural question and answer knowledge; “ - The limitations recites a mathematical addition for a third element sum (see MPEP 2106.04(a)(2)I).
“the third average similarity vector is an average value of similarity vectors of all nodes in the candidate node subset for the single-file structural question and answer knowledge;” - The limitations recites a mathematical process of determining an average that is the third average similarity vector (see MPEP 2106.04(a)(2)I).
“the fourth element sum is an element sum of a fourth average similarity vector of the candidate node subset for the single-file paragraph question and answer knowledge;“ - The limitations recites a mathematical addition for a fourth element sum (see MPEP 2106.04(a)(2)I).
“and the fourth average similarity vector is an average value of similarity vectors of all nodes in the candidate node subset for the single-file paragraph question and answer knowledge;” - The limitations recites a mathematical process of determining an average that is the fourth average similarity vector (see MPEP 2106.04(a)(2)I).
“determining, based on the optimally-matched node set, file knowledge content corresponding to the user question,” The limitations recites a mental process of determining file knowledge content corresponding to the user question based on the optimally-matched node set (see MPEP 2106.04(a)(2)III).
Step 2 Prong 2: Does the claim recite additional elements that integrate the judicial exception into a particular application? No –
The claim includes the additional element(s):
“A cross-file question and answer knowledge extraction method, comprising: obtaining a user question;”
The additional elements fall under Insignificant Extra-Solution Activity as mere data gathering by user question. See MPEP 2106.5(g).
“wherein the file embedding vector tree comprises the root node, a leaf node, and a non-root and non-leaf node;”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
“the vector corresponding to the maximum inner product value is the main title embedding vector, the average embedding vector, or the abstract embedding vector;”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
“the leaf node comprises paragraph text and a paragraph text embedding vector;”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
“the root node comprises a main title of the file, a main title embedding vector, an average embedding vector of chapter title embedding vectors, a file abstract, and an abstract embedding vector;”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
“the non-root and non-leaf node comprises a chapter title, a chapter title embedding vector, an average embedding vector of subtitle or paragraph embedding vectors, a chapter abstract, and a chapter abstract embedding vector;”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
“determining a plurality of similar vector trees by using a K-nearest neighbor algorithm based on first similarity vectors of all root nodes;”
The additional elements fall under “apply it” as using a generic computer to determine a plurality of similar vectors trees by using k-nearest neighbor information between systems (see MPEP 2106.05(f)).
“determining a plurality of similar vector trees by using a K-nearest neighbor algorithm based on first similarity vectors of all root nodes;”
The additional elements fall under “apply it” as using a generic computer to determine a plurality of similar vectors trees by using k-nearest neighbor information between systems (see MPEP 2106.05(f)).
“determining a candidate node set by using the K-nearest neighbor algorithm based on all the similar vector trees,”
The additional elements fall under “apply it” as using a generic computer to determine a candidate node set using k-nearest neighbor information between systems (see MPEP 2106.05(f)).
“wherein the candidate node set comprises a candidate node subset for cross-file structural knowledge, a candidate node subset for cross-file paragraph knowledge, a candidate node subset for single-file structural knowledge, and a candidate node subset for single-file paragraph knowledge;”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
“wherein the file knowledge content comprises the main title, the chapter title, the chapter abstract, a paragraph body of the file or combinations thereof.”
The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No - The claim does not include additional elements that are sufficient to amount to a significantly more than the judicial exemption. As an order whole, the claim is directed to the mathematical process to determining a optimally matched node. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of receiving, determining and what the nodes comprise fall under using generic computer to apply an exception, insignificant Extra-Solution Activity, and mere data gathering. The method does not improve on the function of a computer, transforms an article into another article, nor is it applied by a particular machine, making the claim not patent eligible.
Regarding claim 2:
Step 2A Prong 2, Step 2B: The additional element(s):
“The cross-file question and answer knowledge extraction method according to claim 1, further comprising: constructing the file embedding vector tree.”
The additional elements fall under “apply it” as using a generic computer to construct the file embedding vector tree (see MPEP 2106.05(f)).
Regarding claim 3:
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“preprocessing the professional knowledge files to determine file information, wherein the file information comprises the main title, the chapter title, a paragraph body under each chapter, the chapter abstract, and the file abstract;” - The limitations recites a mental process of processing professional knowledge files to determining files information (see MPEP 2106.04(a)(2)III).
“constructing the file embedding vector tree based on the file information.” - The limitations recites a mental process of constructing the file embedding vector tree based on the file information (see MPEP 2106.04(a)(2)III).
Step 2A Prong 2, Step 2B: The additional element(s):
“The cross-file question and answer knowledge extraction method according to claim 2, wherein the constructing the file embedding vector tree specifically comprises: obtaining a plurality of professional knowledge files;”
The additional elements fall under Insignificant Extra-Solution Activity as mere data gathering. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 4:
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“The cross-file question and answer knowledge extraction method according to claim 3, wherein the preprocessing the professional knowledge files to determine file information specifically comprises: extracting key information of each of the professional knowledge files,” - The limitations recites a mental process of extracting key information of each of the professional knowledge file (see MPEP 2106.04(a)(2)III).
“generating the chapter abstract for each chapter of each of the professional knowledge files by using an abstract generation function,” - The limitations recites a mental process of generating the chapter abstract for each chapter of each of the professional knowledge files (see MPEP 2106.04(a)(2)III).
“generating the file abstract for each of the professional knowledge files based on a plurality of chapter abstracts of the professional knowledge file.” - The limitations recites a mental process of generating the file abstract for each of the professional knowledge files (see MPEP 2106.04(a)(2)III).”
Step 2A Prong 2, Step 2B: The additional element(s):
“wherein the key information comprises the main title, the chapter title, and the paragraph body under each chapter;”
“wherein the abstract generation function is a deep learning–based abstract generation model or a rule-based abstract generation algorithm;”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 5:
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“The cross-file question and answer knowledge extraction method according to claim 1, wherein the determining the user question embedding vector and a first similarity vector of a root node of a file embedding vector tree of each professional knowledge file specifically comprises: calculating the inner product of the user question embedding vector and each of the main title embedding vector, the average embedding vector, and the abstract embedding vector of the file embedding vector tree of each professional knowledge file to obtain a first inner product, a second inner product, and a third inner product;” - The limitations recites a mathematical process of calculating the inner product of the user question embedding vector and each of the main title embedding vector, the average embedding vector, and the abstract embedding vector of the file embedding vector tree of each professional knowledge file to obtain a first inner product, a second inner product, and a third inner product; (see MPEP 2106.04(a)(2)I).
“comparing the first inner product, the second inner product, and the third inner product to determine the maximum inner product value and the vector corresponding to the maximum inner product value;” - The limitations recites a mental process of comparing the first, second, and third inner product to t determine the maximum inner value (see MPEP 2106.04(a)(2)III).
“generating the file abstract for each of the professional knowledge files based on a plurality of chapter abstracts of the professional knowledge file.” - The limitations recites a mental process of generating the file abstract for each of the professional knowledge files (see MPEP 2106.04(a)(2)III).”
“determining a first similarity vector for each root node based on the vector corresponding to the maximum inner product value and the user question embedding vector.” - The limitations recites a mental process of determining a first similarity vector for each root node (see MPEP 2106.04(a)(2)III).”
Step 2A Prong 2, Step 2B: The additional element(s):
No additional elements. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 6:
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“calculating a first similarity element sum of each root node, wherein the first similarity element sum is an element sum of the first similarity vector of the root node;” The limitations recites a mathematical process of calculating a first similarity element sum of each root node (see MPEP 2106.04(a)(2)I).”
“determining an initial candidate node set by using root nodes corresponding to the first preset quantity of top first similarity element sums as candidate nodes;” The limitations recites a mental process of determining an initial candidate node set by using root nodes (see MPEP 2106.04(a)(2)I).”
Step 2A Prong 2, Step 2B: The additional element(s):
“The cross-file question and answer knowledge extraction method according to claim 1, wherein the determining a plurality of similar vector trees by using a K-nearest neighbor algorithm based on first similarity vectors of all root nodes specifically comprises:”
“sorting all first similarity element sums in descending order, and selecting a first preset quantity of top first similarity element sums;”
The additional elements fall under “apply it” as using a generic computer to sort all first similarity element sums in descending order and select a first preset quantity (see MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“selecting a benchmark similarity vector from the initial candidate node set, and determining a second preset quantity of similar vector trees by using the K-nearest neighbor algorithm, wherein the benchmark similarity vector is a first similarity vector corresponding to a maximum first similarity element sum, and the second preset quantity is less than the first preset quantity.”
The additional elements fall under “apply it” as using a generic computer to select a benchmark similarity vector from the initial candidate node set and use the K-nearest neighbor algorithm (see MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 7:
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“calculating a second similarity vector between the user question embedding vector and a non-root and non-leaf node of each file embedding vector tree; - The limitations recites a mathematical process of calculating a second similarity vector between the user question embedding vector and a non-root and non-leaf node of each file embedding vector tree (see MPEP 2106.04(a)(2)I).
calculating a second similarity element sum of each non-root and non-leaf node, wherein the second similarity element sum is an element sum of a second similarity vector of the non-root and non-leaf node” - The limitations recites a mathematical process of calculating a second similarity element sum of each non-root and non-leaf node (see MPEP 2106.04(a)(2)I).”
“calculating a third similarity vector between the user question embedding vector and a leaf node of each file embedding vector tree; “ - The limitations recites a mathematical process of calculating a third similarity vector between the user question embedding vector and a leaf node of each file embedding vector tree; (see MPEP 2106.04(a)(2)I).”
“calculating a third similarity element sum of each leaf node, wherein the third similarity element sum is an element sum of a third similarity vector of the leaf node;” - The limitations recites a mathematical process of calculating a third similarity element sum of each non-root and non-leaf node (see MPEP 2106.04(a)(2)I).”
“determining all second similarity element sums in a first target file embedding vector tree, wherein the first target file embedding vector tree is a file embedding vector tree of a non-root and non-leaf node corresponding to a maximum second similarity element sum;” – The limitations recites a mental process of determining all second similarity element sums in a first target file embedding vector tree (see MPEP 2106.04(a)(2)III).
“determining the candidate node subset for the single-file structural question and answer knowledge based on the fifth preset quantity of top second similarity element sums;
determining all third similarity element sums in a second target file embedding vector tree, wherein the second target file embedding vector tree is a file embedding vector tree of a leaf node corresponding to a maximum third similarity element sum;” - The limitations recites a mental process of determining the candidate node based on the fifth preset quantity and determining all third similarity elements sums (see MPEP 2106.04(a)(2)III).
“determining the candidate node subset for the single-file paragraph question and answer knowledge based on the sixth preset quantity of top third similarity element sums.” - The limitations recites a mental process of determining the candidate node subset for the single-file paragraph question and answer knowledge based on the sixth preset quantity of top third similarity element sums (see MPEP 2106.04(a)(2)III).
Step 2A Prong 2, Step 2B: The additional element(s):
“The cross-file question and answer knowledge extraction method according to claim 1, wherein the determining a candidate node set by using the K-nearest neighbor algorithm based on all the similar vector trees specifically comprises: constructing a virtual root node based on a second preset quantity of similar vector trees;”
The additional element falls under the “apply it” by using computers to constructing a virtual root node based on a second preset quantity of similar vector trees; (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“sorting all second similarity element sums in descending order, and selecting a third preset quantity of top second similarity element sums; determining the candidate node subset for the cross-file structural question and answer knowledge by using the K-nearest neighbor algorithm based on the third preset quantity of top second similarity element sums;”
The additional element falls under the “apply it” by using computers to sort all second similarity elements and determine a candidate node subset using K-nearest neighbor algorithm (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“sorting all third similarity element sums in descending order, and selecting a fourth preset quantity of top third similarity element sums;
determining the candidate node subset for the cross-file paragraph question and answer knowledge by using the K-nearest neighbor algorithm based on the fourth preset quantity of top third similarity element sums;” - The additional element falls under the “apply it” by using computers to sort all third similarity elements and determine a candidate node subset using K-nearest neighbor algorithm (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“sorting all the second similarity element sums in the first target file embedding vector tree in descending order, and selecting a fifth preset quantity of top second similarity element sums in the first target file embedding vector tree;”
The additional element falls under the “apply it” by using computers to storing all second similarity element sums and selecting a fifth preset quantity (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“sorting all the third similarity element sums in the second target file embedding vector tree in descending order, and selecting a sixth preset quantity of top third similarity element sums in the second target file embedding vector tree; and” - The additional element falls under the “apply it” by using computers to storing all third similarity element sums and selecting a sixth preset quantity (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Claim 8 recite system and is analogous to the method of claims 1. Therefore, the rejections of claim 1 above applies to claim 8.
Regarding claim 9:
Step 2A Prong 2, Step 2B: The additional element(s):
“An electronic device, comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor runs the computer program to enable the electronic device to execute the cross-file question and answer knowledge extraction method according to claim 1.”
The additional element falls under the “apply it” by using computers to run the computer program to enable cross-file question and answer knowledge extraction method (MPEP 2106.05(f)). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claims 10, 11, 12, 13, 14, and 15 are analogous to claim 9 and are rejected for the same reason as claim 9.
Regarding claim 16,
Step 2A Prong 2, Step 2B: The additional element(s):
“The electronic device according to claim 9, wherein the memory is a readable storage medium.” - The additional elements fall under Insignificant Extra-Solution Activity (See MPEP 2106.5(g)).
Regarding claims 17, 18, 19, and 20, are analogous to claim 9 and are rejected for the same reason as claim 16.
Allowable Subject Matter
Claim 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Regarding claim 1, Salama et al. (WO2023098971A1) (“Salama”) teaches a method A cross-file question and answer knowledge extraction method, comprising: obtaining a user question; converting the user question into a user question embedding vector by using an embedding function (Salama Page 13 line 6-11, Mathematically, sentence embeddings of question q and context-sentence
cs are concatenated with the element-wise difference |q – cs| to train a softmax classifier for learning the weight matrix Wt
∈
R 3n*k , as given by the following (1)
equation:
PNG
media_image1.png
49
535
media_image1.png
Greyscale
where n is the dimension of the SBERT sentence embeddings and k is the number of labels.
page 16 line 17-19, According to an embodiment, the context retriever 114 is based on a SBERT architecture. The SBERT architecture as it produces enhanced fixed-length sentence embeddings demonstrating state-of-the-art results on various unsupervised learning tasks.
Page 18 line 11-16, At step 202D, a context retriever 114 is pre-trained for identifying within given paragraph describing a context, candidate sub-paragraphs with the highest probability of containing an answer to a user question, using semantic understanding between the user question and the context. According to an embodiment, the context retriever 114 is based on a sentence bidirectional encoder representation from transformers (SBERT), architecture.
page 18 line 27-30 and page 19 line 1-2,
With reference to FIG. 3C, during the inference phase, at step 206A, a user question is received. At step 206B, a candidate paragraph, containing a candidate answer to the user question, is identified based on semantic meaning of the user question by means of the pre- trained context retriever 114. At step 206B, an answer to the user question based on a pair of the user question and the candidate paragraph is extracted by means of the tuned extractive question answering module 112); Further it provides a method for identifying based the highest probability of containing answer to a user question.
Regarding the limitation determining the user question embedding vector and a first similarity vector of a root node of a file embedding vector tree of each professional knowledge file, wherein the file embedding vector tree comprises the root node, a leaf node, and a non-root and non-leaf node; Galitsky et al. (US11741316B2) (“Galitsky”) in the same field of endeavor of question answering using machine learning teaches creating trees based on knowledge regarding form finance, law, business, science, etc. and finding similarities in the date to the user question by identifying common syntactic nodes (Galitsky col 1 line 43-52, In an aspect, a method involves creating a first semantic tree from a question and second semantic tree from a candidate answer. Each semantic tree includes nodes and edges. The nodes represent entities. Each edge represents a relationship between two of the entities. The method involves identifying, between the first semantic tree and the second semantic tree, a plurality of common subtrees. A common subtree includes nodes and edges. Each node represents a common entity that is common between the first semantic tree and the second semantic tree.
In the example depicted, computing device 110 interacts with user device 160 in a dialogue session. Dialogue 170, depicted on user device 160, includes utterances 171 and 172. Computing device 110 accesses utterance 171, analyzes text of the utterance, and determines an answer from a candidate answer. If the candidate answer is determined to be accurate, then autonomous agent 112 provides the candidate answer to user device 160. The conversation can continue.
Col 4 line 12-22, Computing device 110 can include one or more of autonomous agent 112, semantic parser 114, syntactic parser 116, deep learning model 118, and knowledge database 120.
Semantic parser 114 can use techniques such as abstract meaning representation (AMR) to generate one or more semantic trees that represent knowledge or information present in a question ( e.g., utterance 171) and/or a candidate answer. Syntactic parser 116 generates syntactic parse trees for the question and/or a candidate answer. Semantic and syntactic information is used to validate the candidate answer.
Col 4 line 52-58, In some cases, the entities are matched using knowledge database 120. Knowledge database 120 can be a domain specific ontology (e.g., finance, law, business, science, etc.). The knowledge database 120, among other features, can provide synonym matching. Autonomous agent 112 can build knowledge database 120 or knowledge database 120 from an external source.
Col 10 line 14-13, Returning to FIG. 3, at operation 312, process 300 involves calculating a syntactic alignment score based on the number of common syntactic nodes. In general, a greater number of alignments means a greater similarity and therefore a higher score. An ideal answer is when an answer and question are similar (e.g. common words organized in the same manner). In this respect, aligning the structure of the trees is an improvement over simply identifying common keywords between question and answer
Col 8 line 38-46, At operation 310, process 300 involves identifying, between the first syntactic tree and the second syntactic tree, a number of common syntactic nodes. Different approaches can be used. For instance, a machine-learning based approach can identify common nodes. In this case, the syntactic trees are provided to a trained machine-learning model, which identifies and outputs the common syntactic nodes. The common syntactic nodes are connected between the first and second syntactic trees.).
Regarding the limitation determining a plurality of similar vector trees by using a K-nearest neighbor algorithm based on first similarity vectors of all root nodes;
Anderson et al. (US20200097598A1) (“Anderson”) teaches a method of clustering question using k-means clustering algorithm to determine answers for a group of users (Anderson Fig. 3,
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para 0049, As shown in FIG. 3, the QA system pipeline 300 comprises a plurality of stages 310-380 through which the QA system operates to analyze an input question and generate a final response. In an initial question input stage 310, the QA system receives an input question that is presented in a natural language format. That is, a user inputs, via a user interface, an input question for which the user wishes to obtain an answer, e.g., “Who are Washington's closest advisors?” In response to receiving the input question, the next stage of the QA system pipeline 300, i.e. the question and topic analysis stage 320, parses the input question using natural language processing (NLP) techniques to extract major features from the input question, and classify the major features according to types, e.g., names, dates, or any of a plethora of other defined topics. For example, in the example question above, the term “who” may be associated with a topic for “persons” indicating that the identity of a person is being sought, “Washington” may be identified as a proper name of a person with which the question is associated, “closest” may be identified as a word indicative of proximity or relationship, and “advisors” may be indicative of a noun or other language topic.
Para 0060, FIG. 4 is a block diagram of a question answering system for enhancing knowledge delivery and attainment for delivery of presentation content in accordance with an illustrative embodiment. A presenter or presentation system delivers presentation content 403 to a group of users. Question answering (QA) system 410 receives questions 401 from the group of users who are viewing or listening to a live or recorded presentation. QA system 410 provides answers to questions 401 in the form of supplemental information 416 based on information in corpus 404 and, in one embodiment, presentation content 403. QA system 410 may return supplemental information 416 to the users asking the question.
Para 0064, n one example embodiment, clustering component 414 uses k-means clustering. The k-means clustering algorithm is a technique of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. The k-means clustering algorithm aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.
Para 0065 line 1-3, Clustering component 414 groups questions into question clusters based on similarity of question features 412 and answer features 413.)
However Salama, Galitsky, and Anderson fail to teach or suggest the limitations “determining an optimally-matched node set based on the candidate node set, wherein the optimally-matched node set is a subset corresponding to a maximum element sum in the candidate node set;
the maximum element sum is a maximum value of a first element sum, a second element sum, a third element sum, and a fourth element sum;
determining, based on the optimally-matched node set, file knowledge content corresponding to the user question, wherein the file knowledge content comprises the main title, the chapter title, the chapter abstract, a paragraph body of the file or combinations thereof” as recited in claim1, taken alone or in combination with the remining features and elements of the claim invention.
Independent claim 8 would be allowable for the same reasons cited in claim 1. The remaining claims would be allowable because they depend on one of allowable independent claims 1 and 8.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Giliozzo (US20140229163A1) – teaches a method for obtaining similarity measure between concepts based on latent sematic analysis using graph structure derived from a knowledge based.
Yao, Jing, et al. "CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection." ACM Transactions on Intelligent Systems and Technology 14.2 (2023): 1-24 (“Yao”) – teaches a method for clustering textual information using multi-step ranking function utilizing the neighbor usefulness based on a query document.
Shevelev et al. (WO2022256262A1) – teaches a question answering bot capable of generating answer for user by analyzing questions and answers.
Dikshit, Pankaj, Bhanu Chandra, and M. P. Gupta. "Automating questions and answers of good and services tax system using clustering and embeddings of queries." 2021 20th IEEE international conference on machine learning and applications (ICMLA). IEEE, 2021. – teaches a method using similarity scores and clustering to answer question by finding the 3 nearest cluster.
Conclusion
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/ALFREDO CAMPOS/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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