DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 4/21/2026.
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 Arguments
Applicant’s argument, pages 8-10, filed 4/21/2026, with respect to the rejection of claims 2-5, 8-9, 12-15, and 18-20 under 35 USC 112 (b) have been fully considered and are persuasive. The rejection is now withdrawn.
Applicant’s argument, pages 10-12, filed 4/21/2026, with respect to the rejection of claims 1-3, 8-13, and 18-20 under 35 USC 103 have been fully considered and are moot upon a further consideration and a new ground(s) of rejection made under AIA 35 U.S.C. 103 as being unpatentable over ZHAO (US 2025/0053736 A1), and in further view of KUMAR (US 2025/0217769 A1). Please see the rejections below for more details.
Allowable Subject Matter
Claims 4-7 are allowed. 14-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 of this title, 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 1-3, 8-13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO (US 2025/0053736 A1), and in further view of KUMAR (US 2025/0217769 A1).
REGARDING CLAIM 1, ZHAO discloses a method of providing an interactive voice response interface to a storage system management application, comprising:
creating a textual representation of a machine-readable model (Par 61 – “In step S220, several sentences are generated based on several pre-constructed sentence templates M, the graph data D1, and the ontology information B1. The several sentences are classified into a generated sentence set A.”) describing a configuration of a storage system managed by the storage system management application (Par 41 – “According to the method and the apparatus provided in the embodiments of this specification, the graph data and the ontology information of the subgraph correspond to ontology information in a sentence template, to construct a sentence based on the graph data and the ontology information of the subgraph.”), the textual representation including a plurality of textual statements in which each textual statement (Par 47 – “The computing device can convert graphic data and ontology information into a text based on a correspondence between graphic data and ontology information and sentence generation logic defined in a sentence template. There can be a plurality of sentence templates. For the graph data of the subgraph, different texts can be generated based on different sentence templates, to extract different levels of texts in the subgraph.”) describes a respective relationship between a pair of objects of the machine-readable model or describes a relationship between a given object and a respective value of the given object (Par 49 – “The knowledge graph includes a plurality of nodes and connecting edges between the nodes. The node represents an entity. Therefore, the node may also be referred to as an entity node. The connecting edge between nodes is used to represent a relationship between entity nodes. The entity is a thing in the real world, for example, a person, a place name, a concept, medicine, a company, an organization, an institution, a device, a number, a date, a currency, or an address, which are countless. The entity can be represented by an entity word, and the entity word has a noun property. For example, cola and a beverage are entities. A relationship is used to express a certain connection between different entities. For example, in a connection relationship “Cola-is-a beverage”, a relationship is “is”, and represents relationship data such as “Cola is a beverage”.”);
providing the textual representation of the configuration of the storage system to a large language model as training data for the large language model to train the large language model to learn the textual representation of the configuration of the storage system (Pa 91 – “In step S230, a text corpus corresponding to the subgraph K1 is determined based on the generated sentence set A. The text corpus is used to train a language model.”; Par 53 – “The language model can be a natural language processing model, or can be a large language model. The language model is a natural language processing model trained based on a deep learning technology and a large-scale corpus. A main function of the language model is to predict a next word, a next character, etc. in a text. By learning of a large quantity of language samples, the language model can learn of a structure and a law of a language, and can generate a proper natural language text. Currently, the language model is widely applied to fields such as machine translation, text generation, emotion analysis, and speech recognition, and is one of the important technologies in natural language processing.”); and
[using the large language model, by an interactive voice response system of the storage system management application, to respond to natural language queries about the configuration of the storage system from the learned textual representation of the configuration of the storage system.]
ZHAO does not explicitly teach the [square-bracketed] limitations.
KUMAR discloses the [square-bracketed] limitations. KUMAR discloses a method/system for large language machine learning models for service provider systems comprising: a machine-readable model describing a configuration of a storage system managed by the storage system management application (KUMAR Par 59 – “In one example, a user-generated document can include a string that contextually references another software platform. For example, a documentation platform document may include the string “this document corresponds to project ID 123456, status of which is pending.” In this example, a suitable LLM prompt may be provided that causes the LLM to determine an association between the documentation platform and a project management platform based on the reference to “project ID 123456.””; Par 49 – “An LLM may include a neural network specifically trained to determine probabilistic relationships between members of a sequence of lexical elements, characters, strings or tags (e.g., words, parts of speech, or other subparts of a string), the sequence presumed to conform to rules and structure of one or more natural languages and/or the syntax, convention, and structure of a particular programming language and/or the rules or convention of a data structuring format (e.g., JSON, XML, HTML, Markdown, and the like).”); and
[using the large language model, by an interactive voice response system of the storage system management application, (KUMAR Par 237 – “FIG. 6 depicts an example graphical user interface 600 of a collaboration platform that utilizes a generative service to generate and modify content. Specifically, the graphical user interface 600 is generated by a frontend of a documentation platform that managed a set of electronic documents or pages. As shown in FIG. 6 , the graphical user interface 600 includes a navigation region or panel 602 including a hierarchical element tree of selectable elements, each element selectable to cause display of a respective content item (e.g., page or document) in a content region or panel 604.”) to respond to natural language queries about the configuration of the storage system (KUMAR Fig. 6 – “What are the remaining ….” “The APEX page includes several issues ….” “The following issues are pending”; Par 238 – “In this particular implementation, user input is treated as a discrete message 634, which is displayed in a stream of messages (634, 640, 650) within the generative interface panel 620. The messages may be arranged chronologically and include generative responses 640, 650 produced by a respective automated assistant service of the generative service. While the current example de[cots the generative interface panel 620 as part of a content collaboration graphical user interface 600, other implementations may include a separate chat interface that is not incorporated into a content collaboration graphical user interface and may be operated as a separate chat-based application or platform.”) from the learned textual representation of the configuration of the storage system] (KUMAR Par 51 – “To determine probabilistic relationships between different lexical elements (as used herein, “lexical elements” may be a collective noun phase referencing words, characters, numbers, whitespace, formatting, and the like), an LLM is trained against as large of a body of text as possible, comparing the frequency with which particular words appear within N distance of one another. The distance N may be referred to in some examples as the token depth or contextual depth of the LLM.”; Par 52 – “In many cases, word and phrase lexical elements may be lemmatized, part of speech tagged, or tokenized in another manner as a pretraining normalization step, but this is not required of all embodiments. An LLM is typically trained on natural language text in respect of multiple domains, subjects, contexts, and so on; typical commercial LLMs are trained against substantially all available internet text or written content available (e.g., printed publications, source repositories, and the like). Training data may occupy petabytes of storage space in some examples.”; Par 88 – “For example, a prompt may call for a summary of all documents related to a particular project. In this case, a prompt engineering service may coordinate and/or orchestrate several requests to a generative output engine to summarize a first document, a second document, and a third document, and then generate an aggregate response of each of the three summarized documents.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of ZHAO to include responding to a user query using the LLM, as taught by KUMAR.
One of ordinary skill would have been motivated to include responding to a user query using the LLM, in order to enhance the interaction between a user and a machine for a specific domain interaction.
REGARDING CLAIM 2, ZHAO in view of KUMAR discloses the method of claim 1, wherein the machine-readable model is a hierarchical Java model containing the objects describing the configuration of the storage system (ZHAO Par 59 – “The graph data D1 obtained in this step includes several triplets including graph elements in the subgraph. To be specific, the triplet includes a head node, a connecting edge, and a tail node that are connected to each other. The ontology information includes at least a type of each graph element in the subgraph, for example, includes a type of the head node, a relationship type of the connecting edge, and a type of the tail node. Both the type of the head node and the type of the tail node are node types or entity types.”; Par 129 – “An embodiment of this specification further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed in a computer, the computer is enabled to perform the method described in any one of FIG. 1 to FIG. 2 .”; KUMAR also teaches the limitations: Par 49 –“ An LLM may include a neural network specifically trained to determine probabilistic relationships between members of a sequence of lexical elements, characters, strings or tags (e.g., words, parts of speech, or other subparts of a string), the sequence presumed to conform to rules and structure of one or more natural languages and/or the syntax, convention, and structure of a particular programming language and/or the rules or convention of a data structuring format (e.g., JSON, XML, HTML, Markdown, and the like).”; Note that Applicant admits that the computer program Java is well-known.), the hierarchical Java model describing relationships between the objects (ZHAO Par 49 – “The knowledge graph includes a plurality of nodes and connecting edges between the nodes. The node represents an entity. Therefore, the node may also be referred to as an entity node. The connecting edge between nodes is used to represent a relationship between entity nodes. The entity is a thing in the real world, for example, a person, a place name, a concept, medicine, a company, an organization, an institution, a device, a number, a date, a currency, or an address, which are countless. The entity can be represented by an entity word, and the entity word has a noun property. For example, cola and a beverage are entities. A relationship is used to express a certain connection between different entities. For example, in a connection relationship “Cola-is-a beverage”, a relationship is “is”, and represents relationship data such as “Cola is a beverage”.”).
REGARDING CLAIM 3, ZHAO in view of KUMAR discloses the method of claim 2, wherein creating the textual representation of the machine-readable model comprises traversing the hierarchical Java model to identify each object of the Java model (ZHAO Fig. 1 – “Cat, Cat food, Cola ,…., Beverage”; Par 77 – Table 3 – “A product includes cola …. A pet includes a cat.”; Par 78 – “The first three columns of the first row in Table 3 show the correspondence between a sentence component, graph data and ontology information in the sentence template. The node name is graph data, and the node type is ontology information. Each node in the subgraph K1 can generate a sentence based on the third-type template M3, or some nodes can be selected from the subgraph K1, and generate a sentence based on the third-type template M3.”), and for each identified object:
determining a type name of the identified object (ZHAO Par 50 – “The ontology information includes an entity type of the entity and a relationship type that represents a relationship between entities, namely, an entity type of a node and a relationship type of a connecting edge. The entity type can also be represented by a node type of a node.”; Par 83 – “Any logical reasoning rule includes a logical condition and a reasoning result. The logical reasoning rule includes the ontology information of the knowledge graph. For example, a rule a is “A merchant purchases a product (for a plurality of times), and a product belongs to a category—A merchant prefers a category”. The arrow follows a logical condition and is followed by a reasoning result. In the rule a, “merchant”, “product”, and “category” are node types, and “purchases” and “belongs to” are relationship types, and are both ontology information.”);
determining a relationship between the identified object and another object (ZHAO Par 49 – “The knowledge graph includes a plurality of nodes and connecting edges between the nodes. The node represents an entity. Therefore, the node may also be referred to as an entity node. The connecting edge between nodes is used to represent a relationship between entity nodes. The entity is a thing in the real world, for example, a person, a place name, a concept, medicine, a company, an organization, an institution, a device, a number, a date, a currency, or an address, which are countless. The entity can be represented by an entity word, and the entity word has a noun property. For example, cola and a beverage are entities. A relationship is used to express a certain connection between different entities. For example, in a connection relationship “Cola-is-a beverage”, a relationship is “is”, and represents relationship data such as “Cola is a beverage”.”); and
generating one of the textual statements describing the determined relationship between the identified object and the another object (ZHAO Par 77 Table 3 – “A product includes cola …. A pet includes a cat.”; Par 61 – “In step S220, several sentences are generated based on several pre-constructed sentence templates M, the graph data D1, and the ontology information B1. The several sentences are classified into a generated sentence set A.”; ).
REGARDING CLAIM 8, ZHAO in view of KUMAR discloses the method of claim 2, wherein creating the textual representation of the machine-readable model comprises traversing the hierarchical Java model to identify each object of the Java model (ZHAO Fig. 1 – “Cat, Cat food, Cola ,…., Beverage”; Par 77 – Table 3 – “A product includes cola …. A pet includes a cat.”; Par 78 – “The first three columns of the first row in Table 3 show the correspondence between a sentence component, graph data and ontology information in the sentence template. The node name is graph data, and the node type is ontology information. Each node in the subgraph K1 can generate a sentence based on the third-type template M3, or some nodes can be selected from the subgraph K1, and generate a sentence based on the third-type template M3.”), and for each identified object:
determining a type name of the identified object (ZHAO Par 50 – “The ontology information includes an entity type of the entity and a relationship type that represents a relationship between entities, namely, an entity type of a node and a relationship type of a connecting edge. The entity type can also be represented by a node type of a node.”; Par 83 – “Any logical reasoning rule includes a logical condition and a reasoning result. The logical reasoning rule includes the ontology information of the knowledge graph. For example, a rule a is “A merchant purchases a product (for a plurality of times), and a product belongs to a category—A merchant prefers a category”. The arrow follows a logical condition and is followed by a reasoning result. In the rule a, “merchant”, “product”, and “category” are node types, and “purchases” and “belongs to” are relationship types, and are both ontology information.”);
using Java introspection to generate a value of the identified object (ZHAO Par 76 – “The node information includes a node name and a node type. The target node can be any node in the subgraph K1, or can be a central node or another specified node in the subgraph K1.”; Par 77 – “The sentence template M can include a third-type template M3. The several sentences include a third sentence, and the third sentence is generated based on the third-type template M3. For the third sentence, the node type in the node information is used as a subject, a preset word representing an inclusive relationship is used as a verb, and the node name in the node information is used as an object. The preset word representing the inclusive relationship can include “include”, “contain”, etc. For example, a sentence in Table 3 can be generated based on the third-type template M3 and the subgraph in FIG. 1 .”; In other words, a node type is a string value associated with the node.; SAMUEL also teaches nodes are associated with values in Par 54 -- “In other embodiments, the server 102 is programmed or configured to receive via the GUI a selection of a node or an edge of the graph, cause the GUI to display a value associated with the node or the edge, and receive a second user query including the value.”); and
generating one of the textual statements describing the determined relationship between the identified object and the object value (ZHAO Par 77 Table 3 – “A product includes cola …. A pet includes a cat.”; Par 61 – “In step S220, several sentences are generated based on several pre-constructed sentence templates M, the graph data D1, and the ontology information B1. The several sentences are classified into a generated sentence set A.”).
REGARDING CLAIM 9, ZHAO in view of KUMAR discloses the method of claim 2, wherein creating the textual representation of the machine-readable model comprises traversing the hierarchical Java model to identify each object of the Java model (ZHAO Fig. 1 – “Cat, Cat food, Cola ,…., Beverage”; Par 77 – Table 3 – “A product includes cola …. A pet includes a cat.”; Par 78 – “The first three columns of the first row in Table 3 show the correspondence between a sentence component, graph data and ontology information in the sentence template. The node name is graph data, and the node type is ontology information. Each node in the subgraph K1 can generate a sentence based on the third-type template M3, or some nodes can be selected from the subgraph K1, and generate a sentence based on the third-type template M3.”), and for each identified object:
determining a type name of the identified object (ZHAO Par 50 – “The ontology information includes an entity type of the entity and a relationship type that represents a relationship between entities, namely, an entity type of a node and a relationship type of a connecting edge. The entity type can also be represented by a node type of a node.”; Par 83 – “Any logical reasoning rule includes a logical condition and a reasoning result. The logical reasoning rule includes the ontology information of the knowledge graph. For example, a rule a is “A merchant purchases a product (for a plurality of times), and a product belongs to a category—A merchant prefers a category”. The arrow follows a logical condition and is followed by a reasoning result. In the rule a, “merchant”, “product”, and “category” are node types, and “purchases” and “belongs to” are relationship types, and are both ontology information.”);
examining isX( ) and isY( ) methods of the identified object that return boolean or Boolean (ZHAO Par 85 – “During matching, all triplets in the subgraph K1 each can be matched with the several logical reasoning rules. For example, when a triplet 1 “A store xx-purchases-Cola” is matched with the rule a, whether a type of a head node in the triplet 1 is “merchant”, whether a relationship type is “purchases”, and whether a type of a tail node is “product” can be determined, and whether a type of a head node in a triplet 2 “Cola-is-a beverage” connected to the triplet 1 is “product”, whether a relationship type is “is”, and whether the beverage is a category continue to be determined. If results of the above-mentioned determining are all yes, it is determined that matching succeeds at one time. When the triplet in the subgraph successfully matches the logical condition for a plurality of times, the rule a is referred to as a matching rule.”; In other words, for each note, method/system of ZHAO determines whether or not a node is “merchant” and/or “product” (i.e., isMerchant(node)? isProduct(node)?)); and
generating one of the textual statements describing the determined relationship between the identified object and the result of the isX( ) and isY( ) methods (ZHAO Par 86 – “After the graph data D1 and the ontology information B1 are matched with the several logical reasoning rules, one or more matching rules can be obtained. For each matching rule, a sentence corresponding to the matching rule can be obtained by combining the graph data D1 with the matching rule.”; Par 87 – “In step 3, the node information can be a node name, so that the node name in the graph data D1 can correspond to the reasoning result of the matching rule. The node name is substituted into the reasoning result, to obtain a generated sentence. In an example, for a process of combining the triplet with the matching rule, references can be made to Table 4.”).
REGARDING CLAIM 10, ZHAO in view of KUMAR discloses the method of claim 1.
KUMAR further discloses the method/system further comprising using interactive voice response system to receive natural language instructions regarding storage system configuration changes, and using the large language model to parse the instructions regarding the storage system configuration changes (KUMAR Par 162 – “These suggestions can include and/or may be associated with one or more “preconfigured prompts” that are engineered to cause an LLM to provide particular output. More specifically, a preconfigured prompt may be a static string of characters, symbols and words, that causes—deterministically or pseudo-deterministically—the LLM to provide consistent output. For example, a preconfigured prompt may be “generate a summary of changes made to all documents in the last two weeks.” Preconfigured prompts can be associated with an identifier or a title shown to the user, such as “Summarize Recent System Changes.” In this example, a button with the title “Summarize Recent System Changes” can be rendered for a user in a UI as described herein. Upon interaction with the button by the user, the prompt string “generate a summary of changes made to all documents in the last two weeks” can be retrieved from a database or other memory, and provided as input to the generative output service 116.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of ZHAO to include responding to a user query using the LLM, as taught by KUMAR.
One of ordinary skill would have been motivated to include responding to a user query using the LLM, in order to enhance the interaction between a user and a machine for a specific domain interaction.
REGARDING CLAIM 11, ZHAO in view of KUMAR discloses a system for providing an interactive voice response interface to a storage system management application, comprising:
one or more processors and one or more storage devices storing instructions that are configured, when executed by the one or more processors, to cause the one or more processors to perform operations (ZHAO Par 130 – “a memory and a processor”) comprising: performing the steps of claim 1; thus, it is rejected under the same rationale.
Claim 12 is similar to claim 2; thus, it is rejected under the same rationale.
Claim 13 is similar to claim 3; thus, it is rejected under the same rationale.
Claim 18 is similar to claim 8; thus, it is rejected under the same rationale.
Claim 19 is similar to claim 9; thus, it is rejected under the same rationale.
Claim 20 is similar to claim 10; thus, it is rejected under the same rationale.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C KIM whose telephone number is (571)272-3327. The examiner can normally be reached Monday to Friday 8:00 AM thru 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew C Flanders can be reached at 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655
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