DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 4/23/2024.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-5, 8-9, 12-15, 18-19, and their dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 2-5, 8-9, 12-15 and 18-19 contains the trademark/trade name “Java”. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a program language and, accordingly, the identification/description is indefinite.
To further prosecution, the Examiner interprets the claims as follows:
Claims 2 and 12 – “… wherein the machine-readable model is a hierarchical model containing the objects describing the configuration of the storage system, the hierarchical model describing relationships between the objects.”
Claims 3 and 13 – “wherein creating the textual representation of the machine-readable model comprises traversing the hierarchical model to identify each object of the model, and for each identified object:”
Claims 4 and 14 – “wherein determining the relationship between the identified object and the another object is implemented by annotating the another object using an annotation method in the hierarchical model, wherein the annotation method specifies that the one of the textual statements needs to be created to describe the determined relationship between the identified object and the another object.”
Claims 5 and 15 – “by examining get* methods of the identified object that are annotated using the annotation method.”
Claims 8 and 18 – “wherein creating the textual representation of the machine-readable model comprises traversing the hierarchical model to identify each object of the model, and for each identified object:
determining a type name of the identified object;
using introspection to generate a value of the identified object; …”
Claims 9 and 19 also recite, “examining isX( ) and isY( ) methods of the identified object that return boolean or Boolean” The differences between “boolean” and “Boolean” is not clearly recited in the claim; it renders the claim indefinite. To further prosecution, the Examiner interprets the claim as, “wherein creating the textual representation of a machine-readable model comprises traversing the hierarchical model to identify each object of the model, and for each identified object:
determining a type name of the identified object;
examining isX( ) and isY( ) methods of the identified object that return true, false, or null;” according to [0051] of the as-field specification.
Allowable Subject Matter
Claims 4-7 and 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 and rewritten to overcome the 112 (b) rejections.
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 SAMUEL (US 2025/0278421 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, 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 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 description 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.]
ZHAO does not explicitly teach the [square-bracketed] limitations.
SAMUEL discloses the [square-bracketed] limitations. SAMUEL discloses a method/system for analyzing ontology-based databases for interaction comprising: a machine-readable model describing a configuration of a storage system managed by the storage system management application (SAMUEL Par 23 – “In some embodiments, the database management system 140 is programmed or configured to manage ontology-based databases, such as a graph database or a relational database.”; Par 41 – “As data related to the ontology is generally stored in the various databases, a user query that leads to updates of the ontology can similarly be translated into a database query, as noted above. Such a database query can include a CREATE, DELETE, UPDATE, or a similar clause. However, besides database queries that operate on actual data of the ontology, additional database queries can be required to update metadata of the ontology especially at the ontology level, such as an index, a high-level map, or a running report.”; Par 16 – “In some embodiments, the system is programmed to train (including fine-tuning) an LLM for converting a user query related to the ontology in natural language to database queries for accessing the various databases, which can also include relational databases. The system can be programmed to further train the LLM to preferentially convert a user query to a specific type of database query.”); and
[using the large language model by an interactive voice response system of the storage system management application (SAMUEL Par 21 – “The user query is typically in natural language. The processing can include training and executing one or more LLMs that generate database queries, executing the LLMs using the user query as the input data, transmitting the corresponding database query as the output data from the LLMs to another device for processing, and collecting the database query result.”) to respond to natural language queries about the configuration of the storage system] (SAMUEL Par 52 – “In step 412, the server 102 is programmed or configured to submit the set of database queries to a set of databases of the one or more databases to obtain a database query result.”; Par 53 – “In step 414, the server 102 is programmed or configured to transmit the database query result in response to the specific user query.”).
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 SAMEUL.
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 SAMEUL 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.”), 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 SAMEUL 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 SAMUEL 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 SAMEUL discloses the method of claim 2, wherein creating the textual representation of a 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 SAMEUL discloses the method of claim 1.
SAMUEL further discloses the method/system further comprising using interactive voice response system to receive natural language instructions regarding storage system configuration changes (SAMUEL Par 23 – “In some embodiments, the database management system 140 is programmed or configured to manage ontology-based databases, such as a graph database or a relational database.”; Par 41 – “As data related to the ontology is generally stored in the various databases, a user query that leads to updates of the ontology can similarly be translated into a database query, as noted above. Such a database query can include a CREATE, DELETE, UPDATE, or a similar clause. However, besides database queries that operate on actual data of the ontology, additional database queries can be required to update metadata of the ontology especially at the ontology level, such as an index, a high-level map, or a running report.”; Par 16 – “In some embodiments, the system is programmed to train (including fine-tuning) an LLM for converting a user query related to the ontology in natural language to database queries for accessing the various databases, which can also include relational databases. The system can be programmed to further train the LLM to preferentially convert a user query to a specific type of database query.”), and using the large language model to parse the instructions regarding the storage system configuration changes (SAMUEL Par 21 – “The user query is typically in natural language. The processing can include training and executing one or more LLMs that generate database queries, executing the LLMs using the user query as the input data, transmitting the corresponding database query as the output data from the LLMs to another device for processing, and collecting the database query result.”).
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 SAMEUL.
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 SAMEUL 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
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