DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The present application is being examined under the claims filed 11/13/2025.
Claims 1-7 are pending.
Response to Amendment
This Office Action is in response to Applicant’s communication filed 11/13/2025 in response to office action mailed 09/04/2025 . The Applicant’s remarks and any amendments to the claims or specification have been considered with the results that follow.
Response to Arguments
Regarding Objections and Informalities
In Remarks page 6, Argument 1
(Examiner summarizes Applicant’s arguments) Applicant argues that claim amendments obviate the objections
Examiner’s response to Argument 1
Examiner agrees that the amendments overcome the objections and the objections are withdrawn accordingly.
Regarding 35 U.S.C. 112
In Remarks page 6-7, Argument 2
(Examiner summarizes Applicant’s arguments) Applicant argues that claim amendments obviate the 35 U.S.C. 112 rejections.
Examiner’s response to Argument 2
Examiner agrees that the amendments overcome the 112 rejections and the rejections are withdrawn accordingly. However, amendments raise new issues under 35 U.S.C. 112(b) that must be addressed.
In Remarks pages 7-8, Argument 3
(Examiner summarizes Applicant’s arguments) Applicant argues that no new matter has been added to the claims in view of paragraph 49 of the specification.
Examiner’s response to Argument 3
Based on paragraph 49 and others, Examiner agrees that no new matter has been added and amended claim 1 is supported by the originally-filed application.
Regarding 35 U.S.C. 101
In Remarks pages 8-9, Argument 4
(Examiner summarizes Applicant’s arguments) Applicant argues based on a comparison between claim 3 of example 47 and amended claim 1 of the instant application. Applicant argues that, similar to example 47, the claims of the instant application reflect a technical improvement by performing question template matching and substituting an answer which improves performance. Applicant further argues that the claimed invention reduces dependence on experienced maintenance personnel and that the limitation of performing question template matching is not well-understood, routine, and conventional activity and that accordingly claim 1 should be eligible under steps 2A prong 2 and 2B in view of the 2024 updated guidance.
Examiner’s response to Argument 4
Examiner disagrees. Example 47 includes several additional elements which integrate the judicial exception into a practical application. 2024 SME examples page 12-13 recites:
Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement described in the background.
That is, the steps found to reflect the improvement in example 47 were additional elements that provide a substantial application to the abstract ideas. MPEP 2106.05(a) recites
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. […] In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception.
The claims at issue are composed almost entirely of abstract idea steps. The alleged technical improvements are provided by the abstract ideas alone, while the additional elements are merely tangential additions to the claims (see MPEP 2106.05(f)-(h) and (d)). Therefore, since the additional elements alone or in combination do not provide the improvement, the claim is not directed to an improvement to technology or technical field and the rejections are maintained. See rejections under 35 U.S.C. 101 for a complete analysis.
In Remarks pages 10-11, Argument 5
(Examiner summarizes Applicant’s arguments) Applicant argues that claims 2-7 are eligible by virtue of independent claim 1.
Examiner’s response to Argument 5
Claim 1 was not deemed eligible by Examiner and neither are claims 2-7 for similar reasons.
Regarding 35 U.S.C. 103
In Remarks pages 12-13, Argument 6
(Examiner summarizes Applicant’s arguments) Applicant argues that Skaljin’s decision tree leaf nodes do not classify vehicle malfunctions but instead represent a proportion of liability attribution, and that the purpose of the claim is to determine the cause of the malfunction and corresponding handling method.
Examiner’s response to Argument 6
Examiner disagrees. The claim never recites any limitations related to the cause nor handling method of a vehicle malfunction. The claim specifically recites the term “vehicle fault class” and “vehicle fault diagnosis”. The broadest reasonable interpretation of these terms includes a classification of liability attribution (“liability” is a synonym of the word “fault”) which is taught by Skaljin
In Remarks page 13, Argument 7
(Examiner summarizes Applicant’s argument) Applicant argues that a person skilled in the art would recognize that Li and Skaljin are architecturally incompatible because knowledge graph search query should be simple and that Skaljin’s “clarifying liability in a car accident” increases the search burden and does not simplify the search process.
Examiner’s response to Argument 7,
Examiner disagrees. Skaljin discloses classifying questions using a knowledge graph by preprocessing the questions in advance:
Graph mapping is to map the entities identified from the question to the knowledge graph. In the knowledge graph, with the subject entity of the question as the central point, all triples related to it are queried. Using the CBOW model in word2vec [15], these triples and the question input by the user are trained into the form of word vectors. The word vector of the triple is expressed as Ti_emb, and the word vector of the question is expressed as Q_emb.
There is no reason at all that the information about liability predictions of Skaljin could not be used with the knowledge graph embeddings of Li. Furthermore, Skaljin discloses clear benefits of using the decision tree to classify vehicle fault. That is, Skaljin’s method allows for a faster “short-circuit” analysis of fault liability. A PHOSITA would find it obvious that the faster classification disclosed by Skaljin would lead to benefits of faster processing and/or better queries when combined with Li. Therefore the rejection is maintained.
In Remarks page 13, Argument 8
(Examiner summarizes Applicant’s arguments) Applicant argues that while Li teaches a BiLSTM-CRF and Aho Corasick teaches an aho-corasick algorithm, no single reference teaches using these algorithms together nor using both algorithms for vehicle fault detection.
Examiner’s response to Argument 8
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In Remarks page 13-14, Argument 9
(Examiner summarizes Applicant’s arguments) Applicant argues that claim 1 should be considered novel and non-obvious and thus claim 1 and those which depend from it should overcome the obviousness rejections.
In response to Argument 9,
For the reasons given in responses to arguments above, the rejection of claim 1 under 35 U.S.C. 103 is maintained and rejections of claims 2-7 are maintained for similar reasons.
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 1-7 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 1
Claim language may not be "ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention. Claim 1 recites the limitation “wherein a complete fault diagnosis of vehicle is implemented based on the answer statement”. The scope of the term “complete fault diagnosis” is ambiguous and unclear, rendering the claim indefinite under 35 U.S.C. 112(b).
Claims 2-7 are dependent upon claim 1, and are therefore similarly rejected for including the deficiencies of claim 1.
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 1-7 are rejected under 35 U.S.C. 101 for containing an abstract idea without significantly more.
Regarding Claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
A vehicle fault reasoning method based on a knowledge graph, comprises: constructing a knowledge graph of a vehicle fault — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to performing observations of the relationships between data.
performing question classification on the question statement by means of TextCNN to obtain a classification result — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data using known parameters which may be performed by, for example, performing a series of matrix operations and activation functions.
performing sequence marking on a training question statement by using a method of NER marking sequence and training an entity extraction model based on a sequence marking result, wherein the entity extraction model is a BiLSTM-CRF model — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data using known parameters which may be performed by, for example, performing a series of matrix operations and activation functions (i.e. the steps of the BiLSTM-CRF model can be performed in the human mind).
generating a vehicle fault class by using a decision tree model to make a decision on a result of the entity extraction model extracting the question statement and searching for an answer in the knowledge graph based on the vehicle fault class — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data using a set of known rules and conditionals.
and performing question template matching based on the question classification result and the answer, wherein the question template matching comprises performing multi-pattern string matching using an aho-corasick (AC) algorithm, and substituting the answer into the question template to obtain an answer statement — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating the similarity between data using a set of known algorithm steps that can be performed in the human mind or by a human using pen and paper.
wherein a complete fault diagnosis of vehicle is implemented based on the answer statement —This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating a vehicle condition using known data and predictions (e.g. a mechanic evaluating vehicle defects based on known diagnostic data).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
obtaining a question statement of a user — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
obtaining a question statement of a user — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Regarding Claim 2
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 2 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the constructing a knowledge graph of a vehicle fault further comprises: crawling Internet data related to the vehicle fault by means of a crawler and sorting out the Internet data combined with vehicle fault data into structured data; and constructing the knowledge graph by using the structured data — This limitation is directed to mere instructions to apply a judicial exception. Constructing a knowledge graph using generic web scraping to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the web scraping is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the constructing a knowledge graph of a vehicle fault further comprises: crawling Internet data related to the vehicle fault by means of a crawler and sorting out the Internet data combined with vehicle fault data into structured data; and constructing the knowledge graph by using the structured data — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 3
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 2 which included an abstract idea (see rejection for claim 2). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the knowledge graph is a knowledge base using pictures to store and comprises an entity and a relationship; the entity is represented in a node form, and the relationship is used to represent a directed edge between nodes — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the data of the knowledge graph.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the knowledge graph is a knowledge base using pictures to store and comprises an entity and a relationship; the entity is represented in a node form, and the relationship is used to represent a directed edge between nodes — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 4
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 2 which included an abstract idea (see rejection for claim 2). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the entity of the knowledge graph comprises: repair time, a license plate number, operating mileage, a user name, a defect class, market bad description, a troubleshooting solution, a preliminary judgment conclusion, a final measure of the whole vehicle, and responsibility judgment —This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the knowledge graph entity.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the entity of the knowledge graph comprises: repair time, a license plate number, operating mileage, a user name, a defect class, market bad description, a troubleshooting solution, a preliminary judgment conclusion, a final measure of the whole vehicle, and responsibility judgment — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 5
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the entity extraction BiLSTM-CRF model comprising an Embedding layer, a two-way LSTM layer, and a CRF layer — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the entity extraction model.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the BiLSTM-CRF model comprising an Embedding layer, a two-way LSTM layer, and a CRF layer — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 6
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein types of a voice text comprise fault recognition, factual questions, method questions, list questions, and other questions — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits to a field of a type of voice text.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein types of a voice text comprise fault recognition, factual questions, method questions, list questions, and other questions — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 7
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim merely recites the additional judicial exception:
Step 2A Prong 1:
wherein the AC algorithm comprising a Trie tree and a fail pointer, and the Trie tree comprising an AC tree of each entity type — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data to find matches based on known rules and steps.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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, 5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over NPL reference Li “A Question Answering System of Ethnic Minorities Based on Knowledge Graph” herein referred to as Li in view of Skaljin et al. (PGPUB no. US20210103772A1) herein referred to as Skaljin, and NPL reference Aho et al. “Efficient String Matching: An Aid to Bibliographic Search” herein referred to as Aho Corasick.
Regarding Claim 1
Li teaches:
A vehicle fault reasoning method based on a knowledge graph, comprises: constructing a knowledge graph of a vehicle fault
(page 244 column 1 paragraph 2) “The knowledge graph construction process is shown in Fig. 1.”; Figure 1
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obtaining a question statement
(page 244 column 2 paragraph 1) “Implementation flow of traditional retrieval-type Question Answering System is shown in Fig. 3. Firstly, it preprocessed and analyzed questions put forward by users.”
performing question classification on the question statement by a TextCNN to obtain a classification result
(page 245 column 2 section C) “The purpose of performing question classification is to classify the user’s questions and obtain the corresponding question categories, so as to understand the user’s intention. In general, questions entered by users can be regarded as short text, thereby question classification can be regarded as short text classification. […] this paper proposed an improved method based on TextCNN, concatenating the triple with the question as the input of the classifier and adding an attention layer between the input layer and the convolutional layer.”
performing sequence marking on a training question statement by using a method of NER marking sequence and training an entity extraction model based on a sequence marking result;
and searching for an answer in the knowledge graph based on the vehicle fault class
(page 245 column 1 section A) “The model consists of Bi-LSTM and CRF. In the modeling process, a pre-trained word vector is used as the input of the embedding layer, and then encoded by the bidirectional LSTM layer. After encoding, it is added to the sense layer, and finally sent to the CRF layer for sequence labeling to obtain the predicted sequence.”; (page 247 column 1 section B paragraph 3) “The evaluation results of Bi-LSTM + CRF named entity recognition model in Ethnic are shown in Table III.”
and performing question template matching based on the question classification result and the answer and substituting the answer into the question template to obtain an answer statement
(page 246 column 1 section D) “Using cypher language to find the corresponding entity or attribute value in the Neo4j graph database, and then build the answer to return to the user.”; (page 246 column 1 just above section D) “After user-entered question is processed by the question preprocessing and classifier, a question template is obtained. And the question template is equivalent to the relationship or attribute in the triple. It provides conditions for graph mapping and answer generation later.”
Li does not explicitly teach:
generating a vehicle fault class by using a decision tree model to make a decision on a result of the entity extraction model extracting the question statement
wherein the question template matching comprises performing multi-pattern string matching by using an aho-corasick (AC) algorithm,
wherein a complete fault diagnosis of vehicle is implemented based on the answer statement.
However, Skaljin teaches:
generating a vehicle fault class by using a decision tree model to make a decision on a result of the entity extraction model extracting the question statement
[*Examiner notes: The broadest reasonable interpretation of “vehicle fault class” includes a determination of who caused an automobile accident (an accident can be considered a type of vehicle fault)] (paragraph [0047]) “The leaf nodes 130 correspond to fault determinations. For example, a given one of the leaf nodes 130 may correspond to a first driver involved in a collision having some share of fault (e.g., 0%, 50%, 100%, etc.) and a second driver involved in the collision having some commensurate share of fault (e.g., 100%, 50%, 0%, etc.).”; (paragraph [0044]) “An example decision tree 100 based on a set of fault-determination rules is shown in FIG. 1.”
wherein a complete fault diagnosis of vehicle is implemented based on the answer statement.
(paragraph [0046]) “. As mentioned above, the internal nodes 120 may each correspond to a respective binary-response (e.g., yes/no) question related to the facts of a given motor vehicle collision. The edges extending from the internal nodes 120 may correspond to responses (e.g., “yes” or “no”)[*Examiner notes: corresponds to ].”; (paragraph [0047]) “The leaf nodes 130 correspond to fault determinations[*Examiner notes: complete fault diagnosis of vehicle based on answer statement]. For example, a given one of the leaf nodes 130 may correspond to a first driver involved in a collision having some share of fault (e.g., 0%, 50%, 100%, etc.) and a second driver involved in the collision having some commensurate share of fault (e.g., 100%, 50%, 0%, etc.).”
Li, Skaljin, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li with the decision tree of Skaljin because (Skaljin paragraph [0058]) “By employing the system 200, it is possible to avoid and/or reduce the burden of using a fault-determination decision tree to assess fault. For example, in some cases the system 200 may map a given free-form text 210 to a leaf node of fault-determination decision tree thereby providing a fault determination without requiring any further processing. In another example, the system 200 may serve to “short circuit” the required analysis and may greatly reduce the number of edges of a fault-determination decision tree that need to be traversed in order to arrive at a leaf node and a fault determination.”
Aho Corasick teaches:
wherein the question template matching comprises performing multi-pattern string matching by using an aho-corasick (AC) algorithm,
(page 333 column 2 section 2 paragraph 1) “This section describes a finite state string pattern matching machine that locates keywords in a text string.”
Li, Skaljin, Aho-Corasick, and the instant application are analogous because they are all directed to machine learning and graph technology.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li in view of Skaljin with the pattern matching of Aho Corasick because (Aho Corasick page 340) “The pattern matching scheme described in this paper is well suited for applications in which we are looking for occurrences of large numbers of keywords in text strings.”
Regarding claim 5
Li in view of Skaljiin and Aho Corasick teaches:
The vehicle fault reasoning method based on a knowledge graph according to claim 1
(see rejection of claim 1)
Li further teaches:
wherein the BiLSTM-CRF model comprising an Embedding layer, a two-way LSTM layer, and a CRF layer
(page 244 column 2 last paragraph) “First of all, the Bi- LSTM+CRF model is used to identify the named entity of questions.”
Regarding Claim 7
Li in view of Skaljin and Aho Corasick teaches:
The vehicle fault reasoning method based on a knowledge graph according to claim 1
(see rejection of claim 1)
And Aho Corasick further teaches:
and the AC algorithm comprising a Trie tree and a fail pointer, and the Trie tree comprising an AC tree of each entity type.
[*Examiner notes: The figures on page 335 show a trie tree comprising an AC tree of each entity type]; (page 335 column 1 section 3 paragraph 2) “We shall now show how to construct valid goto, failure[*Examiner notes: fail pointer] and output functions from a set of keywords.”; (page 335 column 2)
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Li and Skaljin with Aho Corasick for the same reasons given in claim 1 above.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Skaljin, Aho Corasick, and further in view of NPL reference Wang et al. “Richpedia: A Large-Scale, Comprehensive Multi-Modal Knowledge Graph”.
Regarding Claim 2
Li in view of Skaljin and Aho Corasick teaches:
The vehicle fault reasoning method based on a knowledge graph according to claim 1
(see rejection of claim 1)
Li in view of Skaljin, and Aho Corasick does not explicitly teach:
wherein the constructing a knowledge graph of a vehicle fault further comprises: crawling Internet data related to the vehicle fault by means of a crawler and sorting out the Internet data combined with vehicle fault data into structured data; and constructing the knowledge graph by using the structured data
However, Wang teaches:
wherein the constructing a knowledge graph of a vehicle fault further comprises: crawling Internet data related to the vehicle fault by means of a crawler and sorting out the Internet data combined with vehicle fault data into structured data; and constructing the knowledge graph by using the structured data
[*Examiner notes: Wang teaches combining existing data with image data crawled from the web into structured knowledge graph data. Skaljin teaches using vehicle fault data.]; (page 2 column 2 section 2.1 bullet 3) “Google,5 Yahoo,6 and Bing7 image sources: We implemented a web crawler to gather sufficient image entities related to each KG entity[*Examiner notes: constructing knowledge graph with structured data]. The web crawler will operate with the image search engine to capture input image entities which will be parsed query results. We employed Google Images, Bing Images, and Yahoo Image Search as image engines.”; (page 4 column 2 section 2.3 paragraph 1) “These collected images from open sources are naturally linked to the input crawling seeds, i.e, KG entities, and image entities from Wikipedia and Wikidata. Therefore, we can exploit relevant hyperlinks and text in Wikipedia to discover the semantic relations (rp:relation) between image entities”
Li, Skaljin, Aho Corasick, Wang, and the instant application are analogous because they are all directed to machine learning. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li in view of Skaljin and Aho Corasick with the image web crawling taught by Wang because (Wang page 11 column 1 section 8) “Our work is to collect images from the Internet for entities in textual knowledge graphs. Then, images are filtered by a diversity retrieval model and RDF links are set between image entities based on the hyperlinks and descriptions in Wikipedia. The result is a big and high-quality multi-modal knowledge graph dataset, which provides a wider data scope to the researchers from The Semantic Web and Computer Vision.”
Regarding Claim 3
Li in view of Skaljin, Aho Corasick and Wang teaches:
The vehicle fault reasoning method based on a knowledge graph according to claim 2
(see rejection of claim 2)
Wang further teaches:
wherein the knowledge graph is a knowledge base using pictures to store and comprises an entity and a relationship; the entity is represented in a node form, and the relationship is used to represent a directed edge between nodes.
(page 3 column 1 third paragraph) “Triple generation: In Richpedia, we zoom in creating these three types of triples as follows: <ei, rp:imageof, ek> indicates that an image entity ei is an image of a KG entity ek.. An example is where rp:001564 is an image entity, i.e., a picture of Sydney, and wd:Q3130 is the KG entity in Wikidata.”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Li, Skaljin, and Aho Corasick with Wang for the same reasons given in claim 2 above.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Min in view of Skaljin, Aho Corasick, Wang, and further in view of NPL reference Zhang et al. “Analysis of Traffic Accident Based on Knowledge Graph” herein referred to as Zhang and NPL reference Sun (PGPUB no. US20190004831A1) herein referred to as Sun.
Regarding Claim 4
Li in view of Skaljin, Aho Corasick, and Wang teaches:
The vehicle fault reasoning method based on a knowledge graph according to claim 3
(see rejection of claim 3)
Li in view of Skaljin, Aho Corasick, and Wang does not explicitly teach:
wherein the entity of the knowledge graph comprises: repair time, a license plate number, operating mileage, a user name, a defect class, market bad description, a troubleshooting solution, a preliminary judgment conclusion, a final measure of the whole vehicle,
and responsibility judgment.
Zhang teaches:
a license plate number
(page 3 column 1 bullet point 1) “For example, in a traffic accident, the entity can be the name of the person involved in the accident, illegal behavior, license plate, a certain road, rainy day, and so on.”
a user name
(page 3 column 1 bullet point 1) “For example, in a traffic accident, the entity can be the name of the person involved in the accident, illegal behavior, license plate, a certain road, rainy day, and so on.”
a defect class,
(page 5 table 1)
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592
741
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market bad description,
(page 5 table 1)
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588
740
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a preliminary judgment conclusion,
(page 5 table 1)
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587
752
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a final measure of the whole vehicle,
(page 5 table 1)
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587
731
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and responsibility judgment.
(page 5 table 1)
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587
752
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Li, Skaljin, Aho Corasick, Wang, Zhang, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li in view of Skaljin, Aho Corasick, and Wang with the accident data taught by Zhang because (Zhang page 1 abstract) “The visualized graphic network displayed by the traffic accident knowledge combines human cognition with machine cognition, which improves human’s ability to understand massive and complicated data. The theoretical system of constructing traffic accident knowledge graph has certain reference significance in the follow-up research on the analysis of massive traffic accident data.”
Sun teaches:
wherein the entity of the knowledge graph comprises: repair time
(paragraph [0083]) “The intelligent customer service system queries the device knowledge graph, acquires the corresponding knowledge point (the last maintenance date is Jan. 1, 2017, the next maintenance mileage should be 37,000 kilometers)”
operating mileage
(paragraph [0083]) “The intelligent customer service system queries the device knowledge graph, acquires the corresponding knowledge point (the last maintenance date is Jan. 1, 2017, the next maintenance mileage should be 37,000 kilometers[*Examiner notes: operating mileage])”
a troubleshooting solution
(paragraph [0080]) “For example, the intelligent customer service system may inform the user “The system shows that your compressor is in an abnormal status, so it is recommended that you have it overhauled in a 4S shop[*Examiner notes: troubleshooting solution]. Whether to go to the nearby 4S shop?” After the user confirms, the intelligent customer service system may send a repair order to the 4S shop, and may also provide the user with navigation information to the 4S shop.”
Li, Skaljin, Aho Corasick, Wang, Zhang, Sun, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li in view of Skaljin, Aho Corasick, Wang, and Zhang with the repair time, mileage, and troubleshooting solution of Sun because (Sun paragraph [0020]) “Compared with the prior art, the present disclosure enables the user to acquire a better customer service experience. For example, the existing customer service systems often require users to describe problems by themselves, which can be unclear because the users are lack of experience/expertise, thus causing a slow locating or even misjudging of the problems. However, the present disclosure automatically investigates the problems by collecting the real-time status information of the IoT device and querying the device knowledge graph, to accurately locate the problem and provide the user with a suitable solution.”
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Skaljin, Aho Corasick, and further in view of Deluca et al. (PGPUB no. US20210150386A1) herein referred to as Deluca and NPL reference Sundblad “Question Classification in Question Answering Systems” herein referred to as Sundblad.
Regarding Claim 6
Li in view of Skaljin and Aho Corasick teaches:
The vehicle fault reasoning method based on a knowledge graph according to claim 1
(see rejection of claim 1)
Li in view of Skaljin and Aho Corasick does not explicitly teach:
wherein types of a voice text comprise fault recognition,
factual questions, method questions,
list questions, and other questions.
However, Deluca teaches:
wherein types of a voice text comprise fault recognition,
(paragraph [0047]) “In exemplary embodiments, receiving module 122 receives a query from the user in at least one of the following ways: via a voice interface within vehicle 130, via a mobile application on a device of the user, via a phone call with a remote assistant, and via a text input.”; (paragraph [0049]) “With reference to an illustrative example, Bob is driving in his car and the “check engine” light suddenly comes on within the dashboard. Bob asks IBM Watson® Personal Assistant, which is installed in his car, “Watson, what does that ‘check engine’ light that just came on mean?” Without the present invention, Bob would need to read through his owner's manual to determine possible reasons for the “check engine” light, or bring his car to a service station. The present invention, rather, is capable of understanding Bob's inquiry (via natural language processing techniques, as discussed above) and analyzing the sensor systems 134 within Bob's car in order to pointedly respond to Bob's question.”
Li, Skaljin, Aho Corasick, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li in view of Skaljin and Aho Corasick with the voice fault recognition of Deluca because (Deluca paragraph 31) “In an exemplary embodiment, user interface 132 may include a voice interface such as IBM Watson® Personal Assistant, thus allowing vehicle diagnostic query program 120 to communicate with vehicle 130 and obtain specific vehicle car manual data, service history data, user queries, authorizations, access to user information, personalized responses from an associated user, snapshots of vehicle sensor system data, and so forth.”
And Sundblad teaches:
factual questions, method questions,
(page 32 section 4.1) “The taxonomies used in question answering can be roughly divided into
three different sizes: small taxonomies that are flat and usually consists of 6-12 different categories, medium sized taxonomies that can be both flat and hierarchical and consist of 15-30 different categories, and large taxonomies that are hierarchical and consist of 50-150 categories. Examples of flat taxonomies are found in A and hierarchical are found in B.”; (page 56)
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281
551
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list questions, and other questions.
(page 54)
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106
497
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Li, Skaljin, Aho Corasick, Deluca, Sundblad, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the knowledge graph and NER sequence marking of Li in view of Skaljin, Aho Corasick, and Deluca with the question categories of Sundblad because (Sundblad page 22) “We saw that addressing the problem needs three things: a corpus of questions to be categorized, a taxonomy into which categorization is to be made, and a machinery for actually performing the categorization. We then looked at different kinds of taxonomies that have been used in previous work”. That is, different question categories are necessary to do classifications.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kline et al. US20190322245A1 for teaching vehicle diagnosis.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126