DETAILED ACTION
This communication is in response to Amendments and Arguments filed on June 12th, 2026. Claims 1, 10 and 19 are amended, claims 1-20 are pending and have been examined.
All previous objections/rejections not mentioned in this Office Action have been withdrawn by the Examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments and Arguments
With respect to rejections made under 35 U.S.C. 101, Applicant argues, "Concerning step 1, Applicant's claimed invention in claim 1 is directed to a method. Therefore, step 1 is successfully fulfilled because the claimed invention is a process. Concerning Step 2A, the Office Action alleges that the claims are directed to an abstract idea. Applicant does not agree, and submits that the claimed invention does not fall within one of the enumerated categories, e.g., the claimed invention is not fundamental economic practices, or methods of organizing human activity, or an idea 'of itself', or mathematical relationships/formulas.
…the Federal Circuit in Enfish has made it clear that whether a claim involves an abstract idea is irrelevant, since the correct legal test is whether the claim as a whole is directed to an abstract idea.
Additionally, assuming in arguendo continuation to Step 2B, Applicant contends that the claim(s) consider as a whole, amounts to significantly more than the exception (i.e., more than an abstract idea, and thus not an exception), and accordingly, the claims are patent eligible… The claims are directed to a specific technological improvement in training logical neural networks, not an abstract idea," (starting on page 17 of Remarks, emphasis original).
Applicant further argues, "that amended independent claim 1 recites features beyond merely tying the invention to a particular operating environment including generic implementation of a computer (or computer based implementation) without imposing meaningful limitation on the scope of the claim. Thus, Applicant contends that amended claim 1 recites specific and meaningful limitation using a computer. Additionally, claim 1 at least recites additional elements that integrate the judicial exception into a practical application of the exception, which can include one or more improvements to a computer or computer technology. Thus, Applicant submits that the amended independent claims, considered as a whole, recite subject matter beyond an abstract idea.
…The claimed method improves this by mapping token-level attentions to predicates and pruning predicates before training, resulting in a more efficient and structured LNN.
Additionally, the claims integrate concepts into a practical application, namely training a logical neural network with a specific architecture including class-based AND gates and exclusive OR gates. The claimed steps cannot be performed mentally and require implementation in a machine-learning system...
Thus, claim 1 is directed to a specific technological solution, does not recite a mental process and, integrates concepts into a practical application and recites significantly more. Therefore, Applicant contends that amended claim 1 recites eligible subject matter and consequently the rejection should be withdrawn."
Applicant's argument is persuasive. The amended claims include both an explicit recitation of a technological improvement, as well as implementation detail that precludes a mere mental process or organization of human activity. While the human mind is capable of identifying relevant passages and topics in text, subdividing the text and removing irrelevant portions of that text, the claims integrated that process into a particular and practical application that, as a whole, are not operable in the human mind. Accordingly, the rejections under 35 U.S.C. 101 are withdrawn.
With respect to rejections made under 35 U.S.C. 103, Applicant argues "The cited references fail to teach the feature of amended claim 1. None of the references teach mapping token-level attentions to predicates and using that mapping to control predicate selection…
Additionally, the art does not teach pruning predicates based on absence of attention-corresponding tokens…
Therefore, Applicant contends that at least the underlined portions of amended claims 1 (as shown above) are not disclosed in the cited references, alone or in combination. Thereby, the claimed invention as recited in amended claim 1 is nonobvious over the cited references and consequently is patentable," (starting on page 25 of Remarks, emphasis original).
Applicant's arguments have been considered, but are not persuasive. Peng may be referenced to teach a system that identifies and prunes portions of a text, including subdivisions of that text into "emotional analysis objects" which would be functionally similar to tokens, even if Peng does not use the particular term of art "token". The obviousness of combination with Daguang and Riegel comes from their recitations of common uses of neural networks and the intrinsic structure of those networks, respectively. Further details are provided below.
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, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over China Invention Application CN 114662469 to Peng et al. (hereinafter, "Peng") in view of UK Patent Application GB 2609718 to Daguang et al. (hereinafter, "Daguang"), further in view of “Logical Neural Networks” by Riegle et al. (hereinafter, “Riegel”).
Regarding claims 1, 10 and 19, Peng teaches a method, computer program product and system comprising: extracting predicates from a predetermined plurality of sentences (page 6, "S203, extracting the main language from the specified sentence, the predicate corresponding to the main language, the modification language of the predicate, the object corresponding to the object and the modification language of the object, as the pruning result information executing step S206;");
causing an explainer component to analyze the sentences to determine attentions from the predicates of the sentences, wherein the attentions are based on different predetermined classes of words, the attentions corresponding to tokens in the predetermined plurality of sentences (page 3, "The emotion analysis in this embodiment is to used for the emotion of the emotion analysis object in the sentence. For example, for any specified sentence, it can request to analyze the emotion of any one emotion analysis object in the specified sentence.It should be noted that the emotional analysis object in the specified sentence in the embodiment can be specified by the user, or can be other device with emotion requirement. For example, when the user is doing research, it can select an emotion analysis object for each specified sentence, analyzing the emotion analysis object emotion in the specified sentence. or it also can adopt the pre-trained emotion analysis object selection model, from the appointed sentence in one filter analysis object, to analyze the emotion analysis object emotion in the specified sentence.In this embodiment, the role of the emotion analysis recognition in the specified sentence, refers to the emotion analysis object in the specified sentence in the role," and page 7, "The emotion analysis model of the embodiment can be a two-classification or multi-classification model. specifically according to the classification type number set by the emotion classification model is determined according to the training. For example, if it is two classification, the corresponding emotion classification can be set as positive and negative, respectively positive optimistic emotion of the positive and negative emotion of negative pessimistic.");
mapping the attentions to the predicates based on the tokens included in each of the predicates, respectively (page 4, "S201, analyzing the role of the emotional analysis object tool recognition the specified sentence by using the dependency syntax;As the emotions of the emotional analysis object in the specified sentence are analyzed in this embodiment, the emotional analysis object plays the role of the main language or the sentence, and occasionally plays the role of predicate. For example, 'use' in 'very good' is a predicate in the sentence. and for the other role in the sentence, there is no emotion, so the emotion analysis object usually does not have other roles… For example, FIG. 3 is an example of a specified provides of the present disclosure. As shown in FIG. 3, the specified statement is the 'The food is the service is bad' as an example, using the dependency syntax analysis tool analyze the role of each word in the specified statement, role marking result as shown in FIG. 3. if the appointed emotion analysis object is 'the service', then it can recognition as the main language."); and
causing a logical neural network to be trained using the pruned list of predicates before deployment of the logical neural network as a service, wherein the subset of the extracted predicates are not used in the training of the logical neural network to thereby reduce a training time and processing workload associated with the training of the logical neural network […] (page 7, "under the TrimOrigin set, training and measuring the training data set and the data set of the measuring data set model is pruned by the pruning scheme of the present disclosure. under the Attack setting, training the model on the original training data set, and performing measuring measuring [sic] the data set after the attack. under the TrimAttack setting, training and measuring the model on the pruned attack training data set and measuring data set.");
causing the extracted predicates to be input into a predetermined pruner model, wherein the pruner model is trained to use the attentions to generate a pruned list of predicates from the predicates of the sentences (page 8, "trimming module 802 used for based on the role of the emotional analysis object to prune the specified sentence, acquire the pruned pruning result after information,");
generating the pruned list of predicates based on the attentions wherein a subset of the extracted predicates are not included in the pruned list of predicates, and wherein the generating of the pruned list of predicates includes removing predicates from the predicates of the sentences that do not include tokens corresponding to the attentions (page 3, "The emotion analyzing method of the embodiment, pruning the specified sentence based on the role of the emotion analyzing object, acquire the pruned pruning information, and based on the pruning result information analyzing the emotion analyzing object in the specified sentence, Because the pruning result information remove to the role of the emotion analyzing object of the noise, information the emotion analyzing object in the appointed sentence emotion, can reduce the influence of the noise analysis result, can effectively increasing the emotion analyzing object of the emotion analyzing accuracy of the analysis object, and it can effectively improve the robustness of emotion analysis.").
Peng does not explicitly teach “deploying the trained logical neural network as the service in a computing environment,” and thus, Daguang is introduced. Daguang teaches deploying the trained logical neural network as the service in a computing environment (paragraph [0110], "In at least one embodiment, training framework 904 trains untrained neural network 906 until untrained neural network 906 achieves a desired accuracy. In at least one embodiment, trained neural network 908 can then be deployed to implement any number of machine learning operations.").
Peng and Daguang are considered analogous because they are each concerned with training neural networks. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have deployed the logical neural network of Peng as taught by Daguang for the purpose of improving neural network performance through user interactions or as a means of implementing a service. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
The combination of Peng and Danguang does not teach particular structural details of neural networks, in regards to the architecture of a network’s neurons, and thus, Riegel is introduced.
Riegle teaches wherein neurons of the logical neural network correspond to the predicates and logical operators (section 2, "The nature of the modeled system of logic depends on the family of activation functions chosen for the network’s neurons, which implement the logic’s various atoms and operations… Inputs are initial truth value bounds for each of the neurons in the network; in particular, neurons pertaining to predicate atoms may be populated with truth values taken from KB data. Additional inputs may take the form of injected formulae representing a query or specific inference problem."); and
wherein an architecture of the logical neural network includes different logical AND gates for the different predetermined classes of words, wherein the architecture of the logical neural network includes an exclusive logical OR gate for mutually exclusive class(es) of the predetermined classes of words (section 1, "The central idea is to create a 1-to-1 correspondence between neurons and the elements of logical formulae, using the observation that the weights of neurons can be constrained to act as, e.g. AND or OR gates.").
Peng, Danguang and Riegel are considered analogous because they are each concerned with using neural networks. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have arranged a logical neural network as claimed according to the teachings of Riegel, in combination with Peng and Danguang with a reasonable expectation of success. A person of ordinary skill has good reason to pursue the claimed arrangement of neural networks within the field of the invention, given that a logical neural network may be configured to suit the input data. If this leads to the anticipated success, it is likely that product is not of innovation but of ordinary skill and common sense.
Claims 2-3, 11-12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Daguang and Riegel as applied to claims 1, 10 and 19 above, and further in view of "AMR Parsing with Action-Pointer Transformer" by Zhou et al. (hereinafter, "Zhou").
Regarding claims 2, 11 and 20, the combination of Peng, Daguang and Riegel does not teach a method, computer program product or system “wherein extracting the predicates from the sentences includes: applying an abstract meaning representation (AMR) parser to the sentences to extract semantics from the sentences, and converting the semantics into a graph, wherein the predicates are determined from the graph,” and thus, Zhou is introduced.
Zhou teaches applying an abstract meaning representation (AMR) parser to the sentences to extract semantics from the sentences, and converting the semantics into a graph, wherein the predicates are determined from the graph (section 1, "Abstract Meaning Representation (AMR) (Banarescu et al., 2013) is a sentence level semantic formalism encoding who does what to whom in the form of a rooted directed acyclic graph. Nodes represent concepts such as entities or predicates which are not explicitly aligned to words, and edges represent relations such as subject/object (see Figure 1). AMR parsing, the task of generating the graph from a sentence, is nowadays tackled with sequence to sequence models parameterized with neural networks.").
Peng, Daguang, Riegel and Zhou are considered analogous because they are each concerned with using neural networks. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Peng, Daguang and Riegel with the teachings of Zhou for the purpose of improving neural network performance. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 3 and 12, Zhou further teaches nodes in the graph represent concepts of the sentences, wherein edges in the graph represent relations to the concepts (section 1, "Abstract Meaning Representation (AMR) (Banarescu et al., 2013) is a sentence level semantic formalism encoding who does what to whom in the form of a rooted directed acyclic graph. Nodes represent concepts such as entities or predicates which are not explicitly aligned to words, and edges represent relations such as subject/object (see Figure 1).").
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Daguang and Riegel as applied to claims 1 and 10 above, and further in view of "'Why Should I Trust You?' Explaining the Predictions of Any Classifier" by Ribeiro et al. (hereinafter, "Ribeiro").
Regarding claims 4 and 13, the combination of Peng, Daguang and Riegel does not teach a method or computer program product “wherein local interpretable model-agnostic explanations (LIMEs) are used by the explainer component for analyzing the sentences,” and thus, Ribeiro is introduced.
Riberio teaches local interpretable model-agnostic explanations (LIMEs) are used by the explainer component for analyzing the sentences (section 1, "LIME, an algorithm that can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model," and section 2, "By 'explaining a prediction', we mean presenting textual or visual artifacts that provide qualitative understanding of the relationship between the instance's components (e.g. words in text, patches in an image) and the model's prediction.").
Peng, Daguang and Ribeiro are considered analogous because they are each concerned with using neural networks. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Peng, Daguang and Riegel with the teachings of Ribeiro for the purpose of improving neural network usability. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Claims 5-7 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Daguang and Riegel as applied to claims 1 and 10 above, and further in view of U.S. Patent Application Publication 2014/0108005 to Kassis et al. (hereinafter, "Kassis").
Regarding claims 5 and 14, the combination of Peng, Daguang and Riegel does not teach a method or system “wherein analyzing the sentences to determine the attentions includes: inputting text of the sentences into the explainer component, tokenizing the text to determine a plurality of tokens, feeding the plurality of tokens separately into a predetermined neural network, wherein the neural network generates a probabilistic ranking of words of the sentences in terms of classification of the sentences to predetermined classes, and wherein an output of the neural network includes the attentions determined from the sentences, wherein the attentions are determined, from a plurality of attentions consumed by the neural network, as having relatively highest probabilities for being associated with the predetermined classes,” and thus, Kassis is introduced.
Kassis teaches inputting text of the sentences into the explainer component (paragraph [0018], "With reference to FIG. 3, the Sentence Parser 302 identifies sentences in a provided block of input text. The ULC Noun Parser 304 identifies keyword/tokens/phrases in a provided block of input text."),tokenizing the text to determine a plurality of tokens, feeding the plurality of tokens separately into a predetermined neural network (paragraph [0018], "With reference to FIG. 3, the Sentence Parser 302 identifies sentences in a provided block of input text. The ULC Noun Parser 304 identifies keyword/tokens/phrases in a provided block of input text."),wherein the neural network generates a probabilistic ranking of words of the sentences in terms of classification of the sentences to predetermined classes (paragraph [0012], "As shown in FIG. 1, in a ULC system 100, a universal language classifier 102 takes as input a document 104 and produces one or more outputs 106 including one or more of: sentence(s) 108, keyword(s) 110, abstract(s) 112, and ranked category (categories) 114."), andwherein an output of the neural network includes the attentions determined from the sentences (paragraph [0018], "The ULC Term Scorer 310 processes input text and identifies unique keywords keeping track of the sentences they occur in, the number of times the keyword occurs, and determines a Term Scorer Keyword Score for each keyword in the input document."),wherein the attentions are determined, from a plurality of attentions consumed by the neural network, as having relatively highest probabilities for being associated with the predetermined classes (paragraph [0025], "The Known Words object 306 must be created and a Known Words List 326 must be loaded into memory before any categorization can occur… The category with the highest score determines the most accurate category assignment for each categorization session.").
Peng, Daguang, Riegel and Kassis are considered analogous because they are each concerned with language processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Peng, Daguang and Riegel with the teachings of Kassis for the purpose of improving language processing accuracy. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 6 and 15, Kassis further teaches a method and system wherein the attentions are at least some of the words of the sentences, wherein the attentions are determined to have at least a predetermined probability for increasing an accuracy of the logical neural network during the training of the logical neural network based on the attentions being determined as having the relatively highest probabilities for being associated with the predetermined classes (paragraph [0207], "Known Words Base Scores are calculated for each Noun Parser Keyword that represent how rare that Keyword is within each specific Known Words category. Rare terms are given higher scores using the current base score algorithm.").
Claims 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Daguang, Riegel and Kassis as applied to claims 6 and 15 above, and further in view of U.S. Patent 11,699,177 to Alexandrov et al. (hereinafter, "Alexandrov").
Regarding claims 7 and 16, the combination of Peng, Daguang, Riegel and Kassis does not teach a method and system “wherein the different predetermined classes of words are associated with a predetermined product, wherein at least some of the predetermined plurality of sentences are review of the product, wherein a first of the predetermined classes of words includes a durability of the product,” however, Alexandrov teaches the different predetermined classes of words are associated with a predetermined product, wherein at least some of the predetermined plurality of sentences are review of the product, wherein a first of the predetermined classes of words includes a durability of the product (column 9, line 16, "Attention-based encoder 211 may receive (at 402) one or more filtered reviews that are determined to be of relevance to the quality assessment of a product or service. Attention-based encoder 211 may evaluate the review structure to isolate (at 404) different topics within the review. The different topics may correspond to references to the product or service name as well as features, qualities, attributes, and/or other descriptive characteristics of the product or service that are included as part of the contextual relevance model.").
Peng, Danguang, Riegel, Kassis and Alexandrov are considered analogous because they are each concerned with language processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Peng, Danguang, Riegel and Kassis with the teachings of Alexandrov for the purpose of applying the system to a commercial field of endeavor. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 8 and 17, Riegel teaches a method and system wherein the deployment of the trained logical neural network includes using the trained logical neural network to determine states for an additional plurality of sentences, wherein the determined states comprise true or false (section 3 Model structure, "In general, LNNs are described in terms of FOL, but it is useful to discuss LNNs restricted to the scope of propositional logic.1 Structurally, an LNN is a graph made up of the syntax trees of all represented formulae connected to each other via neurons added for each proposition… To aid interpretability of bounds, we define a threshold of truth
1
2
<
α
≤
1
such that a continuous truth value is considered True if it is greater than
α
and False if it is less than
1
-
α
.").
Peng, Danguang, Kassis and Alexandrov and Riegel are considered analogous because they are each concerned with natural language processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have used a logical neural network as taught by Riegel in the combination of Peng, Danguang, Kassis and Alexandrov with a reasonable expectation of success. A person of ordinary skill has good reason to pursue the known options of neural networks within the field of the invention. If this leads to the anticipated success, it is likely that product is not of innovation but of ordinary skill and common sense.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Daguang and Riegel as applied to claims 1 and 10 above, and further in view of U.S. Patent Application Publication 2023/0082485 to Sengupta et al. (hereinafter, "Sengupta").
Regarding claims 9 and 18, the combination of Peng, Daguang and Riegel does not teach a method or system “wherein using the attentions to generate the pruned list of predicates from the extracted predicates includes comparing mapped abstract meaning representations (AMRs) to the attentions, wherein the subset of the extracted predicates are the extracted predicates of AMRs that are determined to not contain at least one of the attentions,” and thus, Sengupta is introduced.
Sengupta teaches comparing mapped abstract meaning representations (AMRs) to the attentions, wherein the subset of the extracted predicates are the extracted predicates of AMRs that are determined to not contain at least one of the attentions (paragraph [0021], "Various embodiments of the present invention disclose two different variant solutions for data denoising. In both the solutions, transformers are used as the base architecture. Transformers may use multi-headed self-attention to capture both local and global contexts from texts. Various embodiments of the present invention propose using two primary building blocks: an encoder to identify the noises in the data; and a decoder to correct the identified noises. The encoder may read the incorrect text data as input, extract an abstract representation from the text data, and identify the probability that each token of the text data is contextually incorrect. In some embodiments, a proposed system calculates three probabilities for each word token: a copy probability, a removal probability, and a generation probability. If the copy probability of token is greater than 0.5, the proposed system may copy the exact token from input to the output. For example, proper nouns in the texts can be copied directly to the output without making any changes. Using the removal probability of the token, the encoder decides whether the system should remove the entire token in the output or not.").
Peng, Daguang, Riegel and Sengupta are considered analogous because they are each concerned with language processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Peng, Daguang and Riegel with the teachings of Sengupta for the purpose of improving language processing accuracy. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Application Publication 2004/0019601 to Gates.
U.S. Patent Application Publication 2020/0212297 to Chatterjee et al.
U.S. Patent Application Publication 2021/0124739 to Karanasos et al.
U.S. Patent Application Publication 2021/0365817 to Riegel et al.
U.S. Patent Application Publication 2022/0100962 to Akhalwaya et al.
U.S. Patent Application Publication 2023/0100508 to Abobakr et al.
U.S. Patent Application Publication 2023/0108135 to Kimura et al.
U.S. Patent Application Publication 2023/0367322 to Hou et al.
U.S. Patent 7,027,974 to Busch et al.
U.S. Patent 11,941,531 to Arik et al.
China Invention Application CN 111581365 to Wu et al.
China Invention Application CN 115081427 to Shi et al.
WIPO Publication WO 2019/045758 to Chen et al.
“QuickFOIL: Scalable Inductive Logic Programming” by Zeng et al.
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 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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/SEAN THOMAS SMITH/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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