DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to the arguments and remarks, filed on 12/29/2025, in which claim(s) 2-5, 7, 9, 10, 12, 13 and 15-18 is/are presented for further examination.
Claim(s) 1, 6, 8, 11 and 14 has/have been previously cancelled.
Response to Arguments
Applicant’s arguments, see the middle of page 6 to page 12 of applicant’s remarks, filed on 12/29/2025, with respect to the rejection(s) of claim(s) 2, 12 and 18 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Crook et al., EP 32309797 B1.
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.
Claim(s) 2-5, 7, 9, 10, 12, 13 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nair et al., US 2018/0330331 A1 (hereinafter “Nair”) in view of Vaquero Gonzalez et al., US 2018/0011945 A1 (hereinafter “Vaquero”) in further view of Crook et al., EP 32309797 B1 (hereinafter “Crook”) in further view of Kale et al., US 2018/0052884 (hereinafter “Kale”).
Claims 2, 12 and 18
Nair discloses a system for running queries over unstructured data, comprising:
a processor (Nair, Fig. 3, 320); and
a memory coupled with the processor (Nair, Fig. 3, 330), wherein the memory is configured to provide the processor with instructions which when executed cause the processor to:
generate a schema from the unstructured data, wherein the schema comprises a graph having a plurality of nodes (Nair, Abstract, see “convert a portion of the data from an unstructured format to a structured format. … The network representation may include a plurality of nodes representing characteristics of the data and a plurality of edges connecting at least some of the plurality of nodes”);
Nair does not appear to explicitly disclose infer a structure from the unstructured data by adding an edge to the schema, the edge being generated to define a relationship between at least two pieces of data as captured by respective ones of at least two nodes interconnected by the edge, the at least two pieces of data based at least in part on data extracted from the unstructured data, the edge being associated with a probabilistic confidence level for the relationship between the at least two nodes that expresses uncertainty in validity of the relationship between the at least two nodes interconnected by the edge;
receive, from a user, user feedback associated with the edge;
re-evaluate the probabilistic confidence level for the relationship between at least two nodes based at least in part on the user feedback and one or more attributes of the user stored in one or more nodes in a graph model, to reenforce or revise the addition of the edge; and
receive a query and return a query result, wherein the query result is provided by searching the schema, wherein the schema is configured to receive and be modified by user input.
Vaquero discloses infer a structure from the unstructured data by adding an edge to the schema, the edge being generated to define a relationship between at least two pieces of data as captured by respective ones of at least two nodes interconnected by the edge, the at least two pieces of data based at least in part on data extracted from the unstructured data (Vaquero, [0001], see the process of creating a graph topology from data in different format or even with no format at all (unstructured data) may be highly complex and time consuming; Vaquero, [0016], see inferring a graph topology by dynamically inspecting the database schema without making any assumption on the structure of the tables of the database. Graph topologies can also be inferred by applying the solution described in the present disclosure. The disclosed solution utilizes edge attributes as they require users to indicate the mapping between a table column and an edge attribute. A first type of edge called edge without attributes can be inferred from a column in a table in the database. A second type of edge called edge with attributes can be inferred from two columns in a table in the database. An edge can be an entity that links tables within the database. Examples of edge attributes are data associated with edges: most commonly edge weights, or visualization parameters. An edge attribute can be inferred from a column in a table or other information associated with an entity that links tables in the database. The present disclosure proposes a solution that solves the aforementioned technical problem as it can infer edge attributes in the process of graph topology extraction from relational data; Vaquero, [0021], see the method may comprise analyzing a set of unclassified tables and for each table the method may comprise obtaining a primary key, the primary key representing a set of columns of an unclassified table from the set of unclassified tables, obtaining a set of foreign keys, each foreign key representing a column of the unclassified table and identifying a parameter based on the unclassified table, the primary key and the set of foreign keys. The parameter may comprise a node identification, a set of node attributes, a set of edges without attributes, a set of edges with attributes, or a set of edge attributes. The method may further comprise causing the display of the graph topology based on the analyzed set of unclassified tables; and Vaquero, [0058], see FIG. 10 shows a block diagram 1000 of an example of a machine-readable storage medium 1005 according to an example of the present disclosure for inferring a graph topology. … In particular, the storage medium 1005 can comprise instructions 1010 to process a set of unclassified tables associated with a graph topology and for each table, the following instructions can be executed: instructions 1020 to specify a primary key from a table, instructions 1030 to specify a set of foreign keys from the table and instructions 1040 to identify parameters from the table associated with the graph. … Instructions 1090 can comprise instructions to transmit, provide and store the inferred graph topology).
Nair and Vaquero are analogous art because they are from the same field of endeavor such as processing unstructured data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Nair and Vaquero before him/her, to modify the graph of Nair to include the edge inferring of Vaquero because it would allow the ability to extend/expand graphs.
The suggestion/motivation for doing so would have been to create a graph topology by inferring information, see Vaquero, [0015].
Therefore, it would have been obvious to combine Vaquero with Nair to obtain the invention as specified in the instant claim(s).
The combination of Nair and Vaquero does not appear to explicitly disclose the edge being associated with a probabilistic confidence level for the relationship between the at least two nodes that expresses uncertainty in validity of the relationship between the at least two nodes interconnected by the edge;
receive, from a user, user feedback associated with the edge;
re-evaluate the probabilistic confidence level for the relationship between at least two nodes based at least in part on the user feedback and one or more attributes of the user stored in one or more nodes in a graph model, to reenforce or revise the addition of the edge; and
receive a query and return a query result, wherein the query result is provided by searching the schema, wherein the schema is configured to receive and be modified by user input.
Crook discloses the edge being associated with a probabilistic confidence level for the relationship between the at least two nodes that expresses uncertainty in validity of the relationship between the at least two nodes interconnected by the edge (Crook, [0027], see weights for the state graph are general mathematical functions and are not singular values. For example, the weight on an edge in a state graph may be specified as matrix of values that encodes the co-variance between the states of the connected nodes. These "weight" functions can be set-up and/or updated to not only to reflect the original graph structure but to also encode additional information, such as the strength of relationships observed in other auxiliary data, e.g. collated logs of users interacting with the system or, alternatively, user preferences (explicitly expressed or inferred). The weights may be manually/programmatically predetermined to encode desired relationships, or their values could be computed using standard graph optimization techniques that compute weight values which maximize some objective function; and Crook, [0028], see create a state graph by adding evidence nodes 328 with a confidence indicator and a weighted connection for each matched tag and/or user data to the matched entity on the knowledge base framework. In some embodiments, DSBT system 112 creates a state graph by adding nodes with a confidence indicator and a weighted connection for each unmatched tag and/or user data to the probabilistic model graph. In further embodiments, user preferences, likes, and/or dislikes are utilized to change the weighting of the connection and/or the nodes. In some embodiments, user like and/or preferences will be labeled as "on", while user dislikes will be labeled as "off when added to the state graph);
receive, from a user, user feedback associated with the edge (Crook, [0028], see create a state graph by adding evidence nodes 328 with a confidence indicator and a weighted connection for each matched tag and/or user data to the matched entity on the knowledge base framework. In some embodiments, DSBT system 112 creates a state graph by adding nodes with a confidence indicator and a weighted connection for each unmatched tag and/or user data to the probabilistic model graph. In further embodiments, user preferences, likes, and/or dislikes [i.e., “user feedback”] are utilized to change the weighting of the connection and/or the nodes. In some embodiments, user like and/or preferences will be labeled as "on", while user dislikes will be labeled as "off when added to the state graph);
re-evaluate the probabilistic confidence level for the relationship between at least two nodes based at least in part on the user feedback and one or more attributes of the user stored in one or more nodes in a graph model, to reenforce or revise the addition of the edge (Crook, [0028], see create a state graph by adding evidence nodes 328 with a confidence indicator and a weighted connection for each matched tag and/or user data [i.e., “one or more attributes of the user stored in one or more nodes in a graph model”] to the matched entity on the knowledge base framework. In some embodiments, DSBT system 112 creates a state graph by adding nodes with a confidence indicator and a weighted connection for each unmatched tag and/or user data to the probabilistic model graph. In further embodiments, user preferences, likes, and/or dislikes [i.e., “user feedback”] are utilized to change the weighting of the connection and/or the nodes. In some embodiments, user like and/or preferences will be labeled as "on", while user dislikes will be labeled as "off when added to the state graph).
Nair, Vaquero and Crook are analogous art because they are from the same field of endeavor such as processing unstructured data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Nair, Vaquero and Crook before him/her, to modify the edge inferring graph of the combination of Nair and Vaquero to include the edge scoring of Crook because it would allow the ability to find relationships in graphs.
The suggestion/motivation for doing so would have been to utilize a dialogue belief tracking system to perform actions based on the determined one or more user goals and allow a device to engage in human like conversation with a user over multiple turns of a conversation, see Crook, [0003].
Therefore, it would have been obvious to combine Crook with the combination of Nair and Vaquero to obtain the invention as specified in the instant claim(s).
The combination of Nair, Vaquero and Crook does not appear to explicitly disclose receive a query and return a query result, wherein the query result is provided by searching the schema, wherein the schema is configured to receive and be modified by user input.
Kale discloses receive a query and return a query result, wherein the query result is provided by searching the schema, wherein the schema is configured to receive and be modified by user input (Kale, [0088], see “The knowledge graph 808 may be updated dynamically in some embodiments, for example by the AI orchestrator 206. That is, if the item inventory changes or if new user behaviors or new world knowledge data have led to successful user searches, the intelligent online personal assistant 106 is able to take advantage of those changes for future user searches. An assistant that learns may foster further user interaction, particularly for those users are less inclined toward extensive conversations. Embodiments may therefore modify the knowledge graph 808 may to adjust the information it contains and shares both with other sub-components within the NLU component 214 and externally, e.g. with the dialog manager 216.”).
Nair, Vaquero, Crook and Kale are analogous art because they are from the same field of endeavor such as processing unstructured data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Nair, Vaquero, Crook and Kale before him/her, to modify the scoring edge inferring graph of the combination of Nair, Vaquero and Crook to include the correcting/revision of Kale because it would allow the ability to correct errors.
The suggestion/motivation for doing so would have been to continuously identify and learn from user intents so that user identity and understanding is enhanced over time, see Kale, [0006].
Therefore, it would have been obvious to combine Kale with the combination of Nair, Vaquero and Crook to obtain the invention as specified in the instant claim(s).
Claim(s) 12 and 18 recite(s) similar limitations to claim 2 and is/are rejected under the same rationale.
With respect to claim 18, Nair discloses a computer program product for running queries over unstructured data, the computer program product being embodied in a tangible computer readable storage medium and comprising computer instructions (Nair, Fig. 3, 330).
Claim 3
With respect to claim 3, the combination of Nair, Vaquero, Crook and Kale discloses wherein generating the schema from the unstructured data includes storing the data extracted from the unstructured data as a data node in the graph (Nair, Abstract, see “convert a portion of the data from an unstructured format to a structured format. … The network representation may include a plurality of nodes representing characteristics of the data and a plurality of edges connecting at least some of the plurality of nodes”; Nair, [0003], see pre-process the data regarding the set of entities to convert a portion of the data from an unstructured format to a structured format. The one or more processors may generate, after pre-processing the data, a network representation of the data; and Nair, [0017], see role analysis platform 120 may generate a set of nodes corresponding to skills for roles at an organization and a set of edges corresponding to first order relationships, second order relationships, etc. between skills, and may provide an identification of the nodes and edges for display).
Claim 4
With respect to claim 4, the combination of Nair, Vaquero, Crook and Kale discloses wherein a property comprising a data type is associated with the data node (Nair, [0045], see type).
Claim 5
With respect to claim 5, the combination of Nair, Vaquero, Crook and Kale discloses wherein the processor is further configured to apply a model to the data node to determine the property associated with the data node (Nair, [0046], see role analysis platform 220 may utilize a machine learning technique to correct the erroneous data, such as by determining that a modification of the job experience to "11 years of experience" is associated with a threshold likelihood of being correct. In this way, role analysis platform 220 improves performance by ensuring that entities are not discarded from analysis based on a data error).
Claim 7
With respect to claim 7, the combination of Nair, Vaquero, Crook and Kale discloses wherein at least one of the at least two nodes is connected to another node in the plurality of nodes by the edge (Nair, [0003], see the network representation may include a plurality of nodes representing characteristics of the data and a plurality of edges connecting at least some of the plurality of nodes and representing a relationship between each pair of connected nodes of the plurality of nodes).
Claims 9 and 15
With respect to claims 9 and 15, the combination of Nair, Vaquero, Crook and Kale discloses further comprising instructions which, when executed, cause the processor to:
generate an inferred edge that is added to the graph (Crook, [0028], see create a state graph by adding evidence nodes 328 with a confidence indicator and a weighted connection for each matched tag and/or user data to the matched entity on the knowledge base framework. In some embodiments, DSBT system 112 creates a state graph by adding nodes with a confidence indicator and a weighted connection for each unmatched tag and/or user data to the probabilistic model graph. In further embodiments, user preferences, likes, and/or dislikes are utilized to change the weighting of the connection and/or the nodes. In some embodiments, user like and/or preferences will be labeled as "on", while user dislikes will be labeled as "off when added to the state graph) based on user input (Kale, Abstract; and Kale, [0088]), the inferred edge being based at least in part on an output of a machine learning model (Nair, [0046], see “role analysis platform 220 may utilize a machine learning technique to correct the erroneous data, such as by determining that a modification of the job experience to "11 years of experience" is associated with a threshold likelihood of being correct. In this way, role analysis platform 220 improves performance by ensuring that entities are not discarded from analysis based on a data error”).
Claims 10 and 16
With respect to claims 10 and 16, the combination of Nair, Vaquero, Crook and Kale discloses wherein the processor is further configured to adjust the machine learning model based at least in part on the user input (Nair, [0046], see “role analysis platform 220 may utilize a machine learning technique to correct the erroneous data, such as by determining that a modification of the job experience to "11 years of experience" is associated with a threshold likelihood of being correct. In this way, role analysis platform 220 improves performance by ensuring that entities are not discarded from analysis based on a data error”; Kale, Abstract; and Kale, [0088]).
Claim 13
With respect to claim 13, the combination of Nair, Vaquero, Crook and Kale discloses further comprising receiving user input and modifying the schema based at least in part on the user input (Nair, [0003], see pre-process the data regarding the set of entities to convert a portion of the data from an unstructured format to a structured format. The one or more processors may generate, after pre-processing the data, a network representation of the data; Kale, Abstract; and Kale, [0088]).
Claim 17
With respect to claim 17, the combination of Nair, Vaquero, Crook and Kale discloses further comprising:
receiving user input (Kale, Abstract; and Kale, [0088]);
extracting additional data in response to the user input (Kale, Abstract; and Kale, [0088]); and
modifying the schema based at least in part on the user input, including by processing the additional data to generate one or more additional nodes (Nair, Abstract, see “convert a portion of the data from an unstructured format to a structured format. … The network representation may include a plurality of nodes representing characteristics of the data and a plurality of edges connecting at least some of the plurality of nodes”; Kale, Abstract; and Kale, [0088]), wherein the one or more additional nodes can be linked to another node in the graph via an edge (Nair, [0017], see role analysis platform 120 may generate a set of nodes corresponding to skills for roles at an organization and a set of edges corresponding to first order relationships, second order relationships, etc. between skills, and may provide an identification of the nodes and edges for display).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
– Vigoda et al., 2018/0032913 for machine learning data analysis;
– Batra et al., 2023/0315996 for identifying data of interest using machine learning; and
– Grye et al., 2012/0330649 for extracting patterns from graph and unstructured data.
Point of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUBERT G CHEUNG whose telephone number is (571) 270-1396. The examiner can normally be reached M-R 8:00A-5:00P EST; alt. F 8:00A-4:00P EST.
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HUBERT G. CHEUNG
Assistant Examiner
Art Unit 2152
Examiner: Hubert Cheung
/Hubert Cheung/Assistant Examiner, Art Unit 2152Date: February 27, 2026
/NEVEEN ABEL JALIL/Supervisory Patent Examiner, Art Unit 2152