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 .
Remarks
This Office Action is responsive to Applicants' Amendment filed on April 10, 2026, in which claims 1, 11, and 20 are currently amended. Claims 1-20 are currently pending.
Claim Objections
Claims 1-20 are objected to because of the following informalities: The specification (including the abstract and claims), and any amendments for applications, except as provided for in 37 CFR 1.821 through 1.825, must have text written plainly and legibly either by a typewriter or machine printer in a nonscript type font (e.g., Arial, Times Roman, or Courier, preferably a font size of 12) lettering style having capital letters which should be at least 0.3175 cm. (0.125 inch) high, but may be no smaller than 0.21 cm. (0.08 inch) high (e.g., a font size of 6) in portrait orientation and presented in a form having sufficient clarity and contrast between the paper and the writing thereon to permit the direct reproduction of readily legible copies in any number by use of photographic, electrostatic, photo-offset, and microfilming processes and electronic capture by use of digital imaging and optical character recognition; and only a single column of text. See 37 CFR 1.52(a) and (b). The claim set does not comply with 37 CFR 1.52(a)(1)(iv) as being legibly written or 37 CFR 1.52(a)(1)(v) as having sufficient clarity and contrast between the paper and the writing thereon to permit the direct reproduction of readily legible copies. Appropriate correction is required.
Response to Arguments
Applicant’s arguments with respect to rejection of claims 1-6, 8, 9, 11-16, 18, 19, and 20 under 35 U.S.C. 101 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on p. 10 of the Remarks submitted 4/10/2026 that "There is no fair reading of the claims that demonstrate that the claims are directed towards the abstract idea of mental performance", Examiner respectfully disagrees. Applicant admits on the same page that "the claims are directed towards [...] the ability to ascertain intent based upon conversational input" which is something that the human mind is exceptionally well suited for. Applying this mental process "between a user" (a human) and a "digital assistant system" (which is not given explicit structure in the method claims such that it would be reasonable in view of the instant specification to limit the interpretation of a digital assistant to a computer system). The instant specification admits ([¶0030] "the applied system could be any system evaluates user input for semantic information or converts user input into a semantic representation") which appears to encompass the human mind. Even if the digital assistant were assumed to be a computer device, this amounts to mere instructions to apply the judicial exception to a generic computer device. This naturally transitions into Step 2A Prong Two and Applicant's arguments on p. 10 of the Remarks submitted 4/10/2026 that "The Independent claims recite elements that integrate any alleged abstract idea recited in such claims into a practical application of the abstract idea", which Examiner respectfully disagrees. As identified by Applicant's own admission, the claims are directed towards the judicial exception of "the ability to ascertain intent based upon conversational input" and the mere recitation of generic computer components to apply said abstract idea does not integrate the judicial exception into a practical application (MPEP 2106.07(a)(II) "employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application").
With respect to Applicant's arguments on pp. 11-12 that the claimed invention is "providing an improvement over conventional technologies", Examiner respectfully disagrees. For the sake of argument, assuming that the "digital assistant system" in claim 1 were a generic computer component (which is not required by claim 1 or the instant specification), the bulk of the claim is directed towards the mental process with no details on an improved architecture for the digital assistant system itself, such that the claim does not appear to be directed towards improving the "digital assistance system" but rather towards merely applying the mental process to the "digital assistant system." Therefore, the technological improvement implied by Applicant's arguments appears to be entirely derived from the mental process (2106.05(a) "It is important to note, the judicial exception alone cannot provide the improvement."). Similarly, Applicant argues that the mental process recited in the claims improves the machine learning model recited in the claim, however, assuming that the machine learning model is not a mental process, it is also a generic computer component recited at a high level of generality used as a black box means to apply the judicial exception by taking an input and providing an output consistent with what would commonly be performed entirely in the mind, such that the machine learning model itself does not appear to be improved by what is recited in the instant claims.
For at least these reasons and those detailed below, Examiner asserts that it is reasonable and appropriate to maintain the rejection under 35 U.S.C. 101.
Applicant’s arguments with respect to rejection of claims 1-20 under 35 U.S.C. 103 based on amendment have been considered, however, are not persuasive.
With respect to Applicant’s arguments on p. 14 of the Remarks submitted 4/10/2026 that “neither Yu nor Li suggest the features of "providing the request and candidate predicate as input into a trained machine learning model wherein the trained machine learning model is configured to generate an output comprising a matching score indicative of a likelihood that the candidate predicate represents an action that the user intends the digital assistant to take, wherein the output is generated based on the set of request inputs and the set of predicate inputs" and "performing an action to effectuate the user intent based upon the output comprising the matching score.",” Examiner respectfully disagrees. Yu teaches ([Abstract] "Our method uses deep residual bidirectional LSTMs to compare questions and relation names" [p. 575] "During training we adopt a ranking loss" [¶5.1] "We use the HR-BiLSTM as described in Sec. 4" [p. 575] "we compute the matching score of r given q as srel(r; q)=cos(hr,hq)" [p. 576] "For each question q, after generating a score srel(r;q) for each relation using HR-BiLSTM") where Yu provides both sides to the HR-BiLSTM; the request/question side is represented by gamma, and the predicate/relation side represented by B and then max-pooled into the relation vector. The model is explicitly trained using a ranking loss that separates a gold relation from negative candidate relations based on calculated matching score. For at least these reasons and those further detailed below Examiner asserts that the rejection in view of the combination of Yu and Li is reasonable and should be maintained.
Claim Rejections - 35 USC § 101
101 Rejection
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 1-6, 8, 9, 11-16, 18, 19, and 20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
detecting and actioning user intent in natural language requests (observation, evaluation, and judgement)
identifying a candidate predicate based on the conversational input, wherein the conversational input comprises at least one entity (observation, evaluation, and judgement),
concatenating features derived from the subgraph with pretrained word embeddings to yield a set of request inputs and a set of predicate inputs; (observation, evaluation, and judgement)
To generate an output comprising a matching score indicative of a likelihood that the candidate predicate represents an action that the user intends the digital assistant to take, wherein the output is generated based on the set of request inputs and the set of predicate inputs (observation, evaluation, and judgement)
performing an action to effectuate the user intent based upon the output comprising the matching score (observation, evaluation, and judgement)
Therefore, claim 1 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “a digital assistant system” and “providing the request and candidate predicate as input into a trained machine learning model wherein the trained machine learning model is configured to generate an output”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Claim 1 also recites additional elements “receiving a request from a user, wherein the request is identified from conversational input set forth by the user into the digital assistant system, wherein the conversational input comprises at least one entity”, “retrieving a subgraph from a knowledge base based on the request”, and “outputting a response to the user” which amounts to insignificant extra-solution activity of gathering and outputting data which does not integrate the judicial exception into a practical application (See MPEP 2106.05(g)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting of data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)).
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 11 and 20, which recite a system and a computer program product, respectively, as well as to dependent claims 2-6, 8, 9, 12-16, 18, and 19.
Independent claim 11 recites additional instructions to apply the judicial exception using generic computer components “A digital assistant system comprising a processor and computer executable instructions, that when executed by the processor, cause the system to perform operations comprising”.
Independent claim 20 recites additional instructions to apply the judicial exception using generic computer components “A computer storage medium comprising executable instructions that, when executed by a processor of a machine, cause the machine to perform operations of”.
The additional limitations of the dependent claims are addressed briefly below:
Dependent claims 2 and 12 recite additional instructions to apply the judicial exception using generic computer components “wherein the trained machine learning model comprises a first trained bi-directional LSTM neural network and a second trained bi-directional LSTM network”
Dependent claims 3 and 13 recite additional instructions to apply the judicial exception using generic computer components “the trained machine learning model comprises a trained bi-directional matching LSTM neural network
Dependent claims 4 and 14 recite additional instructions to apply the judicial exception using generic computer components “wherein the trained machine learning model further comprises a first trained bi-directional LSTM network utilizing the set of request inputs and a second trained bi-directional LSTM network utilizing the set of predicate inputs”
Dependent claims 5 and 15 recite additional observation, evaluation, and judgement “wherein the set of request inputs comprises word embedding based on the request concatenated with a subset of the features derived from the subgraph”.
Dependent claims 6 and 16 recite additional observation, evaluation, and judgement “the set of predicate inputs comprises word embedding based on the candidate predicate concatenated with a subset of the features derived from the subgraph”
Dependent claims 8 and 18 recite additional instructions to apply the judicial exception using generic computer components “the trained machine learning model comprises a sigmoid layer” (a Bi-LSTM has sigmoid activation by definition)
Dependent claims 9 and 19 also recite additional observation, evaluation, and judgement “wherein the pretrained word embeddings for a first intent domain also apply to a second intent domain without retraining”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-6, 8, 9, 11-16, 18, 19, and 20 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-9, 11-14, and 16-20 are rejected under U.S.C. §103 as being unpatentable over the combination of Yu (“Improved Neural Relation Detection for Knowledge Base Question Answering”, 2017) and Li (US20170109355A1).
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FIG. 2 of Yu
Regarding claim 1, Yu teaches A method of a digital assistant system detecting and actioning user intent in natural language requests, comprising: receiving a request [from a user];([Abstract] "we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question")
wherein the request is identified from conversational input set forth [by the user] into the digital assistant system, wherein the conversational input comprises at least one entity;([p. 571] "For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB […] The KBQA system in the figure performs two key tasks: (1) entity linking, which links n-grams in questions to KB entities, and (2) relation detection" [p. 572] "Question: what episode was mike kelley the writer of" Yu's system performs entity linking from conversational question input n-grams to KB entities. Figure 1 illustrates that the question includes the entity mention "mike kelley," which is linked to candidate KB entities)
identifying a candidate predicate based on the conversational input, ([p. 1142] "We first identify the topic entity with entity linking and then detect the relation asked by the question with relation detection (from all relations connecting the topic entity)." Relation interpreted as synonymous with candidate predicate)
wherein the candidate predicate comprises an intent associated with the request;([p. 571] "identifies the KB relation(s) a question refers to" [FIG. 1] "episodes_written" "starring_roles" The relation is a predicate that captures what the user's question is asking (the intent))
identifying a subgraph from a knowledge base based on the request;([p. 577] "Our method can be viewed as entity linking on a KB sub-graph. It contains two steps: (1) Sub-graph generation: given the top scored query generated by the previous 3 steps 5, for each node v (answer node or the CVT node like in Figure 1(b)), we collect all the nodes c connecting to v (with relation rc) with any relation, and generate a sub-graph associated to the original query")
wherein the subgraph is identified based upon the entity;([p. 7] "Our method can be viewed as entity linking on a KB sub-graph")
concatenating features derived from the subgraph ([p. 574] "We transform each token above to its word embedding then use two BiLSTMs (with shared parameters) to get their hidden representations [...] (each row vector i is the concatenation between forward/backward representations at i).")
with pretrained word embeddings ([p. 577] "All word vectors are initialized
with 300-d pretrained word embeddings")
to yield a set of request inputs ([p. 574] "The first-layer of BiLSTM works on the word embeddings of question words q ={q1,··· ,qN} and gets hidden representations Γ(1) 1:N =[...] The second-layer BiLSTM Γ(1) 1:N to get the second set of hidden representations Γ(2) 1:N. Since the second BiLSTM starts with the hidden vectors from the first layer, intuitively it could learn more general and abstract information compared to the first layer" set Γ interpreted as a set of request inputs)
and a set of predicate inputs;([p. 574] "their hidden representations [Bword 1:M1 : Brel 1:M2 ] (each row vector i is the concatenation between forward/backward representations at i)." Hidden representation vector B interpreted as set of predicate inputs)
providing the request and candidate predicate as input into a trained machine learning model ([Abstract] "Our method uses deep residual bidirectional LSTMs to compare questions and relation names" [p. 575] "During training we adopt a ranking loss" [¶5.1] "We use the HR-BiLSTM as described in Sec. 4" Yu provides both sides to the HR-BiLSTM; the request/question side is represented by gamma, and the predicate/relation side represented by B and then max-pooled into the relation vector. The model is explicitly trained using a ranking loss that separates a gold relation from negative candidate relations.)
wherein the trained machine learning model is configured to generate an output comprising a matching score([p. 575] "we compute the matching score of r given q as srel(r; q)=cos(hr,hq)" [p. 576] "For each question q, after generating a score srel(r;q) for each relation using HR-BiLSTM")
indicative of a likelihood that the candidate predicate represents an action that the user intends the digital assistant to take, ([p. 571] "identifies the KB relation(s) a question refers to" [p. 575] "the two layers of representations are more likely to be complementary to each other [...] During training we adopt a ranking loss to maximizing the margin between the gold relation r+ and other relations r- in the candidate pool R" Yu's action is a KB query/answer action)
wherein the output is generated based on the set of request inputs and the set of predicate inputs;([p. 575] "Then the final question representation hq becomes a max pooling overall 0 is,1<i<N. (2)Applying max pooling on (1) 1:N and (2) 1:N to get h(1) max and h(2) max, respectively, then setting hq=h(1) max+h(2) max. Finally we compute the matching score of r given q as srel(r;q)=cos(hr,hq)." In Yu the request inputs are gamma -> hq and predicate inputs are B -> hr (See also FIG. 2)).
However, Yu does not explicitly teach receiving a request from a user;
performing an action to effectuate the user intent based upon the output comprising the matching score; and
outputting a response to the user.
Li, in the same field of endeavor, teaches receiving a request from a user;([¶0002] "The present disclosure relates generally to computing technologies, and more specifically to systems and methods for automating the answering of questions raised in natural language and improving human computer interfacing." [¶0046] "an input query having one or more words is received" [¶0096] "the input query may include a human inspired question")
performing an action to effectuate the user intent based upon the output comprising the matching score; and([¶0066] "a structured query is generated and sent to a KG server. Then, the KG server executes the structure query to obtain the object, i.e., answer to the question")
outputting a response to the user.([¶0092] " answer rendering module 412 that outputs and presents the results").
Yu as well as Li are directed towards using LSTM for natural language queries. Therefore, Yu as well as Li are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Yu with the teachings of Li by using the system and user interface of Li to implement the BiLSTM system in Yu. Li provides as additional motivation for combination ([p. 2] “The present disclosure relates generally to computing technologies, and more specifically to systems and methods for automating the answering of questions raised in natural language and improving human computer interfacing.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 2, the combination of Yu and Li teaches The method of claim 1 wherein the trained machine learning model comprises a first trained bi-directional LSTM neural network and a second trained bi-directional LSTM network.(Yu [p.575] "during training we adopt a ranking loss to maximizing the margin between the gold relation r+ and other relations r- in the candidate pool R" See also FIG. 2 which explicitly shows Bi-LSTM 1 and Bi-LSTM 2).
Regarding claim 3, the combination of Yu and Li teaches The method of claim 1 wherein the trained machine learning model comprises a trained bi-directional matching LSTM neural network.(Yu [p.575] "during training we adopt a ranking loss to maximizing the margin between the gold relation r+ and other relations r- in the candidate pool R" [p. 575 §4.3] "Unlike the standard usage of deep BiLSTMs that employs the representations in the final layer for prediction, here we expect that two layers of question representations can be complementary to each other and both should be compared to the relation representation space (Hierarchical matching)" See also FIG. 2).
Regarding claim 4, the combination of Yu and Li teaches The method of claim 3 wherein the trained machine learning model further comprises a first trained bi-directional LSTM network utilizing the set of request inputs (Yu [p. 574] "The first-layer of BiLSTM works on the word embeddings of question words q ={q1,··· ,qN} and gets hidden representations Γ(1) 1:N =[...] The second-layer BiLSTM Γ(1) 1:N to get the second set of hidden representations Γ(2) 1:N. Since the second BiLSTM starts with the hidden vectors from the first layer, intuitively it could learn more general and abstract information compared to the first layer" set Γ interpreted as a set of request inputs utilized by first bi-LSTM (bi-LSTM 2) network (see FIG. 2))
and a second trained bi-directional LSTM network utilizing the set of predicate inputs.(Yu [p. 574] "their hidden representations [Bword 1:M1 : Brel 1:M2 ] (each row vector i is the concatenation between forward/backward representations at i)." Hidden representation vector B interpreted as set of predicate inputs utilized by second Bi-LSTM network (Bi-LSTM 1 in FIG. 2). (Note that the second bi-LSTM utilizes both sets of inputs)).
Regarding claim 6, the combination of Yu and Li teaches The method of claim 1 wherein the set of predicate inputs comprises word embedding based on the candidate predicate concatenated with a subset of the features derived from the subgraph.(Yu [p. 577] "Our method can be viewed as entity linking on a KB sub-graph. It contains two steps: (1) Sub-graph generation: given the top scored query generated by the previous 3 steps 5, for each node v (answer node or the CVT node like in Figure 1(b)), we collect all the nodes c connecting to v (with relation rc) with any relation, and generate a sub-graph associated to the original query" [p. 574] "We transform each token above to its word embedding then use two BiLSTMs (with shared parameters) to get their hidden representations [...] (each row vector i is the concatenation between forward/backward representations at i)." Yu explicitly constructs predicate candidates as relations/ relation chains connected to the topic entity (i.e. an entity-neighborhood slice of the KB graph) and feed those into the Bi-LSTM for scoring/matching).
Regarding claim 7, the combination of Yu and Li teaches The method of claim 1 wherein the trained machine learning model comprises a self-attention layer.(Yu [p. 576] "Another way of hierarchical matching consists in relying on attention mechanism, e.g. (Parikh et al., 2016), to find the correspondence between different levels of representations" [p. 578] "replacing residual with attention […] For the attention-based baseline, we tried the model from (Parikh et al., 2016) and its one-way variations").
Regarding claim 8, the combination of Yu and Li teaches The method of claim 1 wherein the trained machine learning model comprises a sigmoid layer.(Yu [Abstract] "Our method uses deep residual bidirectional
LSTMs" LSTMs use sigmoid activations in gates by definition.).
Regarding claim 9, the combination of Yu and Li teaches The method of claim 1 wherein the pretrained word embeddings for a first intent domain also apply to a second intent domain without retraining.(Yu [p. 571] "relation detection for KBQA often becomes a zero-shot learning task" [p. 574] "We initialize the relation sequence LSTMs with the final state representations of the word sequence, as a back-off for unseen relations" Unseen relations interpreted as second intent domain. Yu explicitly uses pretrained word embeddings for targeting zero shot/unseen labels.).
Regarding claims 11-14 and 16-19, claims 11-14 and 16-19 are directed towards a system for performing the methods of claims 1-4 and 6-9, respectively. Therefore, the rejections applied to claims 1-4 and 5-9 also apply to claims 11-14 and 16-19. Claim 11 also recites additional elements a processor and computer executable instructions, that when executed by the processor, cause the system to perform operations comprising: (Li [¶0102] “As illustrated in FIG. 12, system 600 includes one or more central processing units (CPU) 601 that provides computing resources and controls the computer. CPU 601 may be implemented with a microprocessor or the like, and may also include one or more graphics processing units (GPU) 617 and/or a floating point coprocessor for mathematical computations. System 600 may also include a system memory 602, which may be in the form of random-access memory (RAM), read-only memory (ROM), or both”).
Regarding claim 20, claim 20 is directed towards a computer readable media for performing the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 20. Claim 20 also recites additional elements A computer storage medium comprising executable instructions that, when executed by a processor of a machine, cause the machine to perform operations of a digital assistant system, the operations comprising: (Li [¶0102] “As illustrated in FIG. 12, system 600 includes one or more central processing units (CPU) 601 that provides computing resources and controls the computer. CPU 601 may be implemented with a microprocessor or the like, and may also include one or more graphics processing units (GPU) 617 and/or a floating point coprocessor for mathematical computations. System 600 may also include a system memory 602, which may be in the form of random-access memory (RAM), read-only memory (ROM), or both”).
Claims 5, 10, and 15 are rejected under U.S.C. §103 as being unpatentable over the combination of Yu and Li and in further view of Sun (“Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text”, 2018).
Regarding claim 5, the combination of Yu and Li teaches The method of claim 1.
However, the combination of Yu and Li doesn't explicitly teach wherein the set of request inputs comprises word embedding based on the request
concatenated with a subset of the features derived from the subgraph..
Sun, in the same field of endeavor, teaches the set of request inputs comprises word embedding based on the request ([p. 4] "To represent q, let wq1,…wq|q| be the words in the question. The initial representation is computed as [See Eqn. 4]" That is a request derived embedding produced from the question's words)
concatenated with a subset of the features derived from the subgraph.([p. 4] "The update for entity nodes involves a single-layer feed-forward network (FFN) over the concatenation of four states [See Eqn. 1] The first two terms correspond to the entity representation and question representation (details below), respectively, from the previous layer" Sun explicitly teaches concatenating a request-derived representation with features derived from the question subgraph).
The combination of Yu and Li as well as Sun are directed towards using LSTMs for natural language questions over knowledge bases/graphs. Therefore, the combination of Yu and Li as well as Sun are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Yu and Li with the teachings of Sun by using Yu’s hierarchical matching alongside or as a substitution for Sun’s GRAFT-Net scoring method. Sun provides as additional motivation for combination ([p. 7 §5.3] “GRAFT-Nets (GN) shows consistent improvement over KV-MemNNs on both datasets in all settings, including KB only (-KB), text only (-EF, Text Only column), and early fusion (-EF).”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 10, the combination of Yu and Li teaches The method of claim 1 wherein retrieving a subgraph from a knowledge base based on the request comprises: detecting an entity in the request;(Yu [p. 2] "We first identify the topic entity with entity linking")
retrieving the subgraph from the knowledge base based on the entity;(Yu [p. 2] "from all relations connecting the topic
entity" where Yu explicitly discloses that all the relations connecting the topic entity are a subgraph [p. 7]. See also FIG. 1)
deriving the features from the subgraph [using a convolutional neural network.](Yu [p. 5] "We compute the matching score of r given q as srel(r;q)=cos(hr,hq)" Yu explicitly derives subgraph-based matching features in the next step ("Entity-linking on sub-graph nodes"), computing match scores between question n-grams and names of entities in the generated subgraph).
However, the combination of Yu and Li doesn't explicitly teach deriving the features from the subgraph using a convolutional neural network.
Sun, in the same field of endeavor, teaches deriving the features from the subgraph using a convolutional neural network. ([p. 2] "To enable early fusion, in this paper we propose a novel graph convolution based neural network, called GRAFT-Net (Graphs of Relations Among Facts and Text Networks), specifically designed to operate over heterogeneous graphs of KB facts and text sentences").
The combination of Yu and Li as well as Sun are directed towards using LSTMs for natural language questions over knowledge bases/graphs. Therefore, the combination of Yu and Li as well as Sun are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Yu and Li with the teachings of Sun by using Yu’s hierarchical matching alongside or as a substitution for Sun’s GRAFT-Net scoring method. Sun provides as additional motivation for combination ([p. 7 §5.3] “GRAFT-Nets (GN) shows consistent improvement over KV-MemNNs on both datasets in all settings, including KB only (-KB), text only (-EF, Text Only column), and early fusion (-EF).”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 15, claim 5 is directed towards a system for performing the method of claim 5. Therefore, the rejection applied to claim 5 also applies to claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang (US 20200242444 A1). Is directed towards a conversational entity relationship system using neural network and matching scores to determine likelihood of candidate predicate relationships.
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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