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 .
Response to Amendment
2. The Amendment filed on March 27th 2026 has been entered. Claims 1, 7, 16 and 17 have been amended and claims 2, 9 – 15 and 18 have been cancelled. Claims 1, 3 – 8, 16 and 17 are currently pending.
Response to Arguments
35 U.S.C. §103
3. Applicant's arguments, see Remarks pp. 9 -13, filed March 27th 2026, with
respect to the rejections of claims 1, 3 – 8, 16 and 17 under 35 U.S.C. §103 have been fully considered and they are persuasive.
The crux of Applicant arguments is that the amendments to the independent claims are not taught by art of records
Examiner respectfully agrees
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of Faruk et al., (United States Patent Publication Number 2017/0078232) hereinafter Faruk and Rogers et al., (United States Patent Publication Number 2022/0004546) hereinafter Rogers
Claim Rejections – 35 U.S.C. §103
4. 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.
5. The factual inquiries set forth in Graham v John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied for establishing a background for determining obviousness
under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art
b. Ascertaining the differences between the prior art and the claims at issue
c. Resolving the level of ordinary skill in the pertinent art
d. Considering objective evidence present in the application indicating
obviousness or nonobviousness
Claims 1, 6, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (Chinese Patent Publication Number CN 113672590 ), hereinafter Yu.), in view of Yue et al., (United States Patent Publication Number 20220067011) hereinafter Yue and in further view of Faruk et al., (United States Patent Publication Number 2017/0078232) hereinafter Faruk
Regarding claim 1 Yu teaches a graph database processing method, (data cleaning method of graph database device Page 5) such as “method” comprising: storing graph data (storing line data including information of a plurality of vertices and information of edges connected to the vertices Page 5) in a graph database, (graph database device Page 5) the graph data (line data Page 5) comprising: vertex data corresponding to a plurality of vertexes (vertex data Page 5) of a graph data structure; (graph structure Page 7) and edge data corresponding to a plurality of edges (edge data Page 5) of the graph data structure, (graph structure Page 7) wherein each edge (edge data Page 5)indicates a relationship between two or more vertexes (and information of edges connected to the vertices Page 5) obtaining by computing device (computer-readable storage medium 100 Page 11) a current system time point (the current system time is taken as the timestamp of the first storage unit Page 9) such as “current system time point” of the graph database system; (graph database apparatus Page 5) obtaining a timestamp (and setting life cycle and time stamp for a storage unit storing the edge data in the plurality of storage units Page 5) of each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO “information of sides connected to vertices (i.e., side data” page 7 from a graph database; (graph databases Page 6) see example Janusgraph Page 7 and determining expired edge data (if the life cycle of the storage unit is finished, the data stored in the storage unit is determined to be out of date Page 8) from each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 based on the current system time point, (the current system time is taken as the timestamp of the first storage unit Page 9) such as “current system time point” the timestamp (setting a corresponding life cycle and a time stamp for the second storage unit; specifically, information of the vertex to which each edge is connected (that is, the connection point information including the life cycle of the vertex and the time stamp of the vertex) may be acquired first, and then the connection point information is analyzed to set the life cycle and the time stamp of the second storage unit. For example, as shown in fig. 3, taking the "people going out" side as an example, it is connected with the vertex "people" and the vertex "track", the life cycle and the time stamp of the vertex "people" and the vertex "track" can be analyzed to calculate the life cycle and the time stamp of the "people going out" side Page 8) NOTE “time stamp” such as “timestamp” of the “people going out” edge of each piece of edge data, (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 and the survival time period (setting a corresponding life cycle and a time stamp for the second storage unit; specifically, information of the vertex to which each edge is connected (that is, the connection point information including the life cycle of the vertex and the time stamp of the vertex) may be acquired first, and then the connection point information is analyzed to set the life cycle and the time stamp of the second storage unit. For example, as shown in fig. 3, taking the "people going out" side as an example, it is connected with the vertex "people" and the vertex "track", the life cycle and the time stamp of the vertex "people" and the vertex "track" can be analyzed to calculate the life cycle and the time stamp of the "people going out" side Page 8) NOTE “life cycle” such as “survival time period” of the “people going out” edge corresponding to the edge type ("people going out" edge type Page 10) and deleting the expired edge data (a data cleaning method, a graph database device and a computer readable storage medium, which can clean out expired data Page 5)while retaining non-expired edge data (Step 210: and if the time difference between the current time stamp and the time stamp of the storage unit is greater than the life cycle of the storage unit, determining that the life cycle of the storage unit is ended, and cleaning the data in the storage Page 4) (If the time difference between the current time stamp and the time stamp of the storage unit is less than or equal to the life cycle of the storage unit, the storage unit is not expired, and the data cleaning operation is not required to be executed. Page 10) in the graph database(graph databases Page 6) see example Janusgraph Page 7
Yu does not fully disclose wherein the edge data is stored in the graph database in a sorted order based on at least the timestamp and the edge type; based on the sorted order of the edge data; parsing each piece of the edge data to extract a plurality of edge attributes or a plurality of edge identifiers; obtaining, from the plurality of edge attributes or the plurality of edge identifiers and according to a ranking of the edge data, wherein the graph database processing method is applied to a computing device, the graph database processing method, by the computing device, by the computing device, by the computing device, wherein the graph database processing method further comprises: obtaining, by the computing device, the survival time period of each piece of edge data from a system configuration file of the graph database system based on an edge type of each piece of edge data.
Yue teaches parsing (performing a hash operation the SourceVertexID of the Edge [0199]) such as “parsing” each piece of the edge data (edge data [0198]) to extract a plurality of edge attributes (properties attached to the relationships between entities; [0112] / properties attached to Edge [0115]) such as “edge attributes” EXAMPLE Edge IndexID, Graph PartitionID, Data Type and Property [0144] or a plurality of edge identifiers; (Edge IndexID [0140]) obtaining, from the plurality of edge attributes (properties attached to the relationships between entities; [0112] / properties attached to Edge [0115]) such as “edge attributes” EXAMPLE Edge IndexID, Graph PartitionID, Data Type and Property [0144] or the plurality of edge identifiers (Edge IndexID [0140])) and according to a ranking of the edge data, (the Value of Edge is the value of multiple properties assembled in the order of the Schema [0147]) wherein the graph database processing method (ABS., data processing method) (Figs., 2 – 4 various operations in a graph database [0098] – [0100]) is applied to a computing device, (computing device [0205]) the graph database processing method, (ABS., data processing method) (Figs., 2 – 4 various operations in a graph database [0098] – [0100]) by the computing device, (computing device [0205])by the computing device, (computing device [0205])by the computing device, (computing device [0205])wherein the graph database processing method (ABS., data processing method) (Figs., 2 – 4 various operations in a graph database [0098] – [0100]) further comprises: obtaining, (obtains [0187], [0189], [0192]) by the computing device, (computing device [0205])the survival time period (time to live (referred to as TTL) [0153]) such as “survival time period) of each piece of edge data (edge data [0198]) from a system configuration file (tag schema or edge schema [0122]) such as “system configuration file” of the graph database system (ABS., system of a distributed graph database) (system of a distributed graph database [0122]) based on (based on [0121]) an edge type (edge type [0120]) of each piece of edge data. (edge data [0198])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu to incorporate the teachings of Yue wherein parsing each piece of the edge data to extract a plurality of edge attributes or a plurality of edge identifiers; obtaining, from the plurality of edge attributes or the plurality of edge identifiers and according to a ranking of the edge data, the graph database processing method is applied to a computing device, the graph database processing method, by the computing device, by the computing device, by the computing device, wherein the graph database processing method further comprises: obtaining, by the computing device, the survival time period of each piece of edge data from a system configuration file of the graph database system based on an edge type of each piece of edge data. By doing so realizes balanced storage of distributed index and concurrent computing, reduces network overhead caused by data operation, and improves the efficiency of data operation effectively. Yue [0095]
Faruk teaches wherein the edge data (edge properties [0043]) is stored in the graph database ( stored in a graph index 312 as a network of nodes 314 and edges 316 [0041]) in a sorted order based on at least the timestamp (a timestamp of the activity [0043])and the edge type; (The one or more ranking models 310 are operable to calculate and assign weights to edges 316 based on various factors, such as the type of activity [0043]) based on the sorted order (a measure of the social distance [0044]) such s ”sorted order” of the edge data(edge properties [0043])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu in view of Yue to incorporate the teachings of Faruk wherein the edge data is stored in the graph database in a sorted order based on at least the timestamp and the edge type; based on the sorted order of the edge data. By doing so Faruk weights can be assigned to the edges. Faruk [0043]
Claims 16 and 17 correspond to claim 1 and are rejected accordingly
Regarding claim 6 Yu in view of Yue and Faruk teaches the graph database processing method (data cleaning method of graph database device Page 5) such as “method according to claim 1
Yu as modified further teaches wherein an edge identifier (Table 2 Label ID Page 7) such as “edge identifier” of the edge data(plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 comprises the edge type, (Table 2 “Type” distinguishing between vertices and edges, v denotes a point Label, e denotes an edge Label, specified by the user at schema creation Pages 7 – 8) and the graph database processing method (data cleaning method of graph database device Page 5) such as “method” further comprises: extracting (obtain Page 9) such as “extracting” by the computing device (graph database device Page 7) such as “computing device” the edge type (Table 2 “Type” distinguishing between vertices and edges, v denotes a point Label, e denotes an edge Label, specified by the user at schema creation Pages 7 – 8) of each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 from a parsed (analyzed Page 8) such as “parsed” edge identifier (Table 2 Label ID Page 7) such as “edge identifier” of each piece of edge data; (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 and obtaining the survival time period (setting a corresponding life cycle and a time stamp for the second storage unit; specifically, information of the vertex to which each edge is connected (that is, the connection point information including the life cycle of the vertex and the time stamp of the vertex) may be acquired first, and then the connection point information is analyzed to set the life cycle and the time stamp of the second storage unit. For example, as shown in fig. 3, taking the "people going out" side as an example, it is connected with the vertex "people" and the vertex "track", the life cycle and the time stamp of the vertex "people" and the vertex "track" can be analyzed to calculate the life cycle and the time stamp of the "people going out" side Page 8) NOTE “life cycle” such as “survival time period” of the “people going out” edge of each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 from a system configuration file (column cluster of an HBase table page 6) such as “system configuration file” of the graph database system(graph database apparatus Page 5) based on the edge type (Table 2 “Type” distinguishing between vertices and edges, v denotes a point Label, e denotes an edge Label, specified by the user at schema creation Pages 7 – 8) of each piece of edge data. (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (Chinese Patent Publication Number CN 113672590 ), hereinafter Yu.), in view of Yue et al., (United States Patent Publication Number 20220067011) hereinafter Yue in view of view of Faruk et al., (United States Patent Publication Number 2017/0078232) hereinafter Faruk and in further view of Wu et al. (Chinese Patent Publication Number CN 113010744, hereinafter referred to as Wu.
Regarding claim 3 Yu in view of Yue and Faruk teaches the graph database processing method (data cleaning method of graph database device Page 5) such as “method according to claim 1,
Yu as modified further teaches and the obtaining a timestamp (and setting life cycle and time stamp for a storage unit storing the edge data in the plurality of storage units Page 5) of each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO “information of sides connected to vertices (i.e., side data” page 7 from a graph database comprises: obtaining by the computing device (graph database device Page 7) such as “computing device” each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7 from the graph database; (graph databases Page 6) see example Janusgraph Page 7ta Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7
Yu does not fully disclose wherein an edge identifier of the edge data comprises a timestamp, extracting by the computing device the edge attribute from each piece of obtained edge data; parsing by the computing device the extracted edge attribute of each piece of edge data; and extracting by the computing device the timestamp of each piece of edge data from the parsed edge attribute of each piece of edge data.
Wu teaches wherein an edge identifier of the edge data (attributes of the edges Page 6) comprises a timestamp, (the field may not be a vertex when the field is a timestamp, may be an edge when more than two vertices occur in a piece of data Page 9) extracting by the computing device (device Page 7) the edge attribute from each piece of obtained edge data; (if the two vertexes meet the edge extraction rule, determining that an association relation exists between the two vertexes and an edge exists between the two vertexes; extracting the attributes of the vertexes and the attributes of the edges from the associated data by using an attribute extraction rule in the graph feature extraction rules; … and the edge and the attribute of the edge constitute edge information in the graph feature information Page 6) parsing by the computing device (device Page 7) the extracted edge attribute of each piece of edge data; (generating the in-degree side information … and outgoing degree side information Page 6) NOTE side information is the edge and extracting by the computing device (device Page 7) the timestamp (the graph feature extraction rules provided in the embodiment of the present application may further include a user-defined graph feature extraction rule, for example, the field may not be a vertex when the field is a timestamp, may be an edge when more than two vertices occur in a piece of data, and may be an attribute when the associated data field is a non-vertex, Page 9) of each piece of edge data (edge and the attribute of the edge constitute edge information in the graph feature information Page 6) from the parsed edge attribute (attribute of the edge Page 6) of each piece of edge data (edge and the attribute of the edge constitute edge information in the graph feature information Page 6)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu in view of Yue and Faruk to incorporate the teachings of Wu wherein an edge identifier of the edge data comprises a timestamp, extracting the edge attribute from each piece of obtained edge data; parsing the extracted edge attribute of each piece of edge data; and extracting the timestamp of each piece of edge data from the parsed edge attribute of each piece of edge data. By doing so according to the graph feature extraction function and the user-defined graph feature extraction rule, the corresponding point edge attribute set can be generated to generate the graph features of the associated data. Wu page 9
Regarding claim 4 Yu in view of Yue and Faruk teaches the graph database processing method (data cleaning method of graph database device Page 5) such as “method according to claim 1,
Yu further teaches wherein an edge attribute (Table 2 Label Name Page 7) such as “edge attribute”, and the obtaining a timestamp (and setting life cycle and time stamp for a storage unit storing the edge data in the plurality of storage units Page 5)of each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO “information of sides connected to vertices (i.e., side data” page 7 comprises: obtaining by the computing device (graph database device Page 7) such as “computing device” each piece of edge data (plurality of storage units storing edge data Page 5) SEE ALSO information of sides connected to vertices “(i.e., side data” page 7from the graph database; (graph databases Page 6) see example Janusgraph Page 7
Yu as modified does not fully disclose of the edge data comprises a timestamp attribute; extracting by the computing device the edge attribute from each piece of obtained edge data; parsing by the computing device the extracted edge attribute of each piece of edge data; and extracting by the computing device the timestamp of each piece of edge data from the parsed edge attribute of each piece of edge data.
Wu teaches of the edge data comprises a timestamp attribute; (the field may not be a vertex when the field is a timestamp, may be an edge when more than two vertices occur in a piece of data Page 9) extracting by the computing device (device Page 7)the edge attribute from each piece of obtained edge data; (if the two vertexes meet the edge extraction rule, determining that an association relation exists between the two vertexes and an edge exists between the two vertexes; extracting the attributes of the vertexes and the attributes of the edges from the associated data by using an attribute extraction rule in the graph feature extraction rules; … and the edge and the attribute of the edge constitute edge information in the graph feature information Page 6) parsing by the computing device (device Page 7)the extracted edge attribute of each piece of edge data; (generating the in-degree side information … and outgoing degree side information Page 6) NOTE side information is the edge and extracting by the computing device (device Page 7)the timestamp (the graph feature extraction rules provided in the embodiment of the present application may further include a user-defined graph feature extraction rule, for example, the field may not be a vertex when the field is a timestamp, may be an edge when more than two vertices occur in a piece of data, and may be an attribute when the associated data field is a non-vertex, Page 9)of each piece of edge data (edge and the attribute of the edge constitute edge information in the graph feature information Page 6) from the parsed edge attribute (attribute of the edge Page 6)of each piece of edge data. (edge and the attribute of the edge constitute edge information in the graph feature information Page 6)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu in view of Yue and Faruk to incorporate the teachings of Wu of the edge data comprises a timestamp attribute; extracting the edge attribute from each piece of obtained edge data; parsing the extracted edge attribute of each piece of edge data; and extracting the timestamp of each piece of edge data from the parsed edge attribute of each piece of edge data. By doing so operations such as data verification, vertex extraction, edge extraction and attribute extraction can be performed on batch or real-time data according to the graph feature extraction rule. Wu Page 10.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (Chinese Patent Publication Number CN 113672590 ), hereinafter Yu.), in view of Yue et al., (United States Patent Publication Number 20220067011) hereinafter Yue in view of view of Faruk et al., (United States Patent Publication Number 2017/0078232) hereinafter Faruk and in further view of G – G – Yifante – Lopez (Chinese Patent Publication Number CN 107771333, hereinafter referred to as Lopez.
Regarding claim 5 Yu in view of Yue and Faruk teaches the graph database processing method according to claim 1,
Yu does not fully disclose wherein the survival time period of each piece of edge data comprises a survival time period of each piece of edge data entered by a user.
Lopez teaches wherein the survival time period (TTL values Page 7) of each piece of edge data (Fig 4 edges 414a – 414l Page 12) comprises a survival time period (TTL values Page 7) of each piece of edge data (Fig 4 edges 414a – 414l Page 12 entered by a user (The attribute assignor 68 may specify, for example, TTL values (e.g., cell TTL, link TTL, path TTL, etc.), contexts (e.g., via security policy, usage policy, conditional policy, etc.), weights, and the like. In one example, attributes (e.g., TTL) may be assigned based on the type of data source and/or the type of data (e.g., sensors and/or sensor data in an internet of things computer network). In another example, attributes can be assigned automatically and/or in response to user input (e.g., input from a data source, from a data owner, from a management entity, etc. Pages 7 - 8))
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu in view of Yue and Faruk to incorporate the teachings of Lopez wherein the survival time period of each piece of edge data comprises a survival time period of each piece of edge data entered by a user. By doing so a conditional rule may specify cell TTL and/or link TTL values based on the presence or absence of a particular computational cell and/or a particular link in a particular path. Lopez Page 11
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (Chinese Patent Publication Number CN 113672590 ), hereinafter Yu.) in view of Yue et al., (United States Patent Publication Number 20220067011) hereinafter Yue, and in further view of Scheepens et al., (United States Patent Publication Number 20210200758) hereinafter Scheepens and in further view of Rogers et al., (United States Patent Publication Number 2022/0004546) hereinafter Rogers
Regarding claim 7 Yu teaches a graph database processing method, (data cleaning method of graph database device Page 5) such as “method” comprising storing graph data (storing line data including information of a plurality of vertices and information of edges connected to the vertices Page 5) in a graph database, (graph database device Page 5the graph data (line data Page 5) comprising: vertex data corresponding to a plurality of vertexes (vertex data Page 5) of a graph data structure; (graph structure Page 7) and edge data corresponding to a plurality of edges (edge data Page 5) of the graph data structure, (graph structure Page 7) wherein each edge indicates a relationship (edges are also called relationships (Relationship) Page 6)between two or more vertexes; (each relationship representing the manner of association between two nodes. Page 6) obtaining, by the computing device, (computer-readable storage medium 100 Page 11) a current system time point (the current system time is taken as the timestamp of the first storage unit Page 9) such as “current system time point” of a graph database system; (graph database apparatus Page 5) and deleting the expired edge data (a data cleaning method, a graph database device and a computer readable storage medium, which can clean out expired data Page 5)while retaining non-expired edge data (Step 210: and if the time difference between the current time stamp and the time stamp of the storage unit is greater than the life cycle of the storage unit, determining that the life cycle of the storage unit is ended, and cleaning the data in the storage Page 4) (If the time difference between the current time stamp and the time stamp of the storage unit is less than or equal to the life cycle of the storage unit, the storage unit is not expired, and the data cleaning operation is not required to be executed. Page 10) in the graph database(graph databases Page 6) see example Janusgraph Page 7 for each type of edge data, determining, by the computing device, the first piece of expired edge data in the type of edge data based on the current system time point and a survival time period corresponding to the edge type, and determining, by the computing device, that all edge data whose timestamp is ranked after that of the first piece of expired edge data in the type of edge data is expired edge data;
Yu does not fully disclose the edge data is stored in the graph database in a sorted order; in response to determining the first piece of expired edge data, determining, by the computing device based on the sorted order, that all other edge data in the same type of edge data having a timestamp that is subsequent in the sorted order to the timestamp of the first piece of expired edge data is expired edge data; parsing each piece of the edge data to extract a plurality of edge identifiers (IDs) including a start point ID, an edge type, a timestamp, and an endpoint ID, wherein the edge data is sorted based on the start point ID, the edge type, the timestamp, and the endpoint ID; classifying, by the computing device, the edge data in the graph database based on the start point ID and the edge type in the edge identifier;
Yue teaches parsing (performing a hash operation the SourceVertexID of the Edge [0199]) such as “parsing” each piece of the edge data (edge data [0198]) to extract a plurality of edge attributes (properties attached to the relationships between entities; [0112] / properties attached to Edge [0115]) such as “edge attributes” EXAMPLE Edge IndexID, Graph PartitionID, Data Type and Property [0144] or a plurality of edge identifiers; (Edge IndexID [0140]) including a start point ID, an edge type, a timestamp, and an endpoint ID, (properties attached to the relationships between entities;[0112]) (properties attached to Vertex or Edge, [0115]) wherein the edge data is sorted based on the start point ID, the edge type, the timestamp, and the endpoint ID; (multiple
properties assembled in the order of the Schema; [0146])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu to incorporate the teachings of Yue wherein parsing each piece of the edge data to extract a plurality of edge identifiers (IDs) including a start point ID, an edge type, a timestamp, and an endpoint ID, wherein the edge data is sorted based on the start point ID, the edge type, the timestamp, and the endpoint ID. By doing so realizes balanced storage of distributed index and concurrent computing, reduces network overhead caused by data operation, and improves the efficiency of data operation effectively. Yue [0095]
Scheepens teaches classifying, (classify [0028]) by the computing device, (computing device [0022]) the edge data (Fig. 2, (204) edge table [0024]) such as “edge data” in the graph database (any suitable form and organized within any suitable type of data structure [0048]) such as “graph database” based on the start point ID (Fig. 3, source activity [0027])such as “start point ID” and the edge type (Fig. 3, destination activity [0027]) such as “edge type” in the edge identifier; (case identifier [0028]) such as “edge identifier”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu in view of Yue to incorporate the teachings of Scheepens wherein classifying, by the computing device, the edge data in the graph database based on the start point ID and the edge type in the edge identifier. By doing so at activity 106, the classification is evaluated. Scheepens [0017]
Rogers teaches the edge data is stored (stored graphs comprising edges [0102]) in the graph database (graph database [0301]) in a sorted order; (dependencies [0225])in response to determining the first piece of expired edge data, (edges considered to be expired based on validity durations for the edges and a configurable expiration time [0216])determining, (determine [0214]) by the computing device (computing devices [0081]) based on the sorted order, (dependencies [0225]) that all other edge data in the same type of edge data (edges of a special type or label (for example an edge of type "ConnectsTo", "DependsOn", or "Loggedinto"). [0228]) having a timestamp (start timestamp [00209]) that is subsequent (validity durations [0216])in the sorted order (dependencies [0225]) to the timestamp (end timestamp [0209]) of the first piece of expired edge data is expired edge data; (edges considered to be expired based on validity durations for the edges and a configurable expiration time [0216])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu in view of Yue and Sheepens to incorporate the teachings of Rogers in response to determining the first piece of expired edge data, determining, by the computing device based on the sorted order, that all other edge data in the same type of edge data having a timestamp that is subsequent in the sorted order to the timestamp of the first piece of expired edge data is expired edge data. By doing so when the textual query is submitted for execution against the entity relationship graph 162, the graph server 248 (for example) modifies the submitted query based on the time value associated with the query such that results of the modified query includes only state nodes with start timestamp values indicating start times before the specified times for the queries and end timestamp values either of zero or indicating end times after the specified times for the queries. Rogers [0218]
Regarding claim 8 Yu in view of Yue, Scheepens and Rogers teaches the graph database processing method (data cleaning method of graph database device Page 5) such as “method according to claim 7,
Yu as modified further teaches wherein a classification process of the edge data and/or a determining process of the first piece of expired edge data are implemented (If the time difference between the current time stamp and the time stamp of the storage unit is greater than the life cycle of the storage unit, it is indicated that the life cycle of the storage unit is ended, and the expiration time of the storage unit has been reached Page 10) based on a dichotomy. (Table 2, Label ID which is a primary key of metadata information of one type of data, is a self-increasing sequence and Label name is specified by the user at the time of schema creation. Page 8)
Conclusion
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire
THREE MONTHS from the mailing date of this action. In the event a first reply is
filed within TWO MONTHS of the mailing date of this final action and the advisory action
is not mailed until after the end of the THREE-MONTH shortened statutory
period, then the shortened statutory period will expire on the date the advisory
action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be
calculated from the mailing date of the advisory action. In no event, however, will
the statutory period for reply expire later than SIX MONTHS from the date of this
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/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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