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 .
Status of Claims
In response to communications filed on 27 January 2026, claims 1-20 are presently pending in the application, of which, claims 1, 12 and 13 are presented in independent form. The Examiner acknowledges amended claims 1, 12, and 13. No claims were cancelled or newly added. In addition, the ‘After-Final’ amendment, filed 29 December 2025, has been entered with this RCE.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 January 2026 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Remarks/Arguments
All objections and/or rejections issued in the previous Office Action, mailed 27 October 2025, have been withdrawn, unless otherwise noted in this Office Action.
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable by Bauer, Walter (U.S. 2019/0317940 and known hereinafter as Bauer) in view of Yinglong, Xin, et al (U.S. 2021/0004374 and known hereinafter as Xia)(newly presented).
As per claim 1, Bauer teaches a computer-implemented method of storing Resource Description Framework (RDF) graph data in a graph database comprising a set of RDF tuples (e.g. Bauer, see paragraph [0569], which discloses a property graph database based on nodes that are connected by edges, with both nodes and edges having properties, and applied to the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions.), the method comprising:
obtaining one or more adjacency matrices, each adjacency matrix representing a group of tuples of the graph database comprising a same predicate (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.); and
the array including:
one or more indices each pointing to a sub-division of the adjacency matrix (e.g. Bauer, see paragraphs [0010-0017], which discloses providing one or more pointer that includes a predetermined length or size and can be stored in the same or inverse order as the bits are set in the bitmap.), and/or
one or more elements each representing a group of tuples of a RDF graph database of a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraphs [0356-0400, 0568-0572], which discloses storing the elements in the graph database.).
Baur does not explicitly disclose storing, for each of the one or more adjacency matrices, a data structure efficiently compressing the adjacent matrix, the data structure comprising an array, the array including: stored in one of a plurality of memory layout types, each memory layout type of the plurality of memory layout type using indices having a different size from other memory layout types of the plurality of memory layout types.
Xia teaches storing, for each of the one or more adjacency matrices, a data structure efficiently compressing the adjacent matrix, the data structure comprising an array (e.g. Xia, see paragraphs [0074-0075], which discloses in multi-modal graph representation, compressed sparse row is a common storage format to store the graph, where incoming edges are stored in compressed sparse column format and out-going edges in compressed sparse row format. Additionally, see Figure 3A, which discloses an input graph having vertices is presented in global adjacency matrix format and is divided into two partitions.), the array including:
stored in one of a plurality of memory layout types, each memory layout type of the plurality of memory layout type using indices having a different size from other memory layout types of the plurality of memory layout types (e.g. Xia, see paragraphs [0075-0077], where the graph processing system uses iterative graph computing with an edge-set based graph representation, where each subgraph partition is further converted into a set of edge-sets. Each edge-set contains vertices within a certain range by vertex ID, where incoming edges are stored in compressed sparse column format (memory layout type) and the out-going edges are stored in compressed row format (memory layout type).).
Baur is directed to efficient use of trie data structures in databases. Xia is directed to handling concurrent property graph queries. Both are analogous art because they are directed to improving graph database efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Bauer with the teachings of Xia to include the claimed features with the motivation to improve graph database efficiencies.
As per claim 12, Bauer teaches a non-transitory computer readable storage medium having recorded thereon a computer program that when executed by a computer causes the computer to implement the method according to claim 1 (see rejection of claim 1 above.).
As per claim 13, Bauer teaches a system comprising:
a processor coupled to a memory, the memory having recorded thereon a computer program for storing RDF graph data in a graph database comprising a set of RDF tuples (e.g. Bauer, see paragraphs [0008-0010], which discloses a processor coupled to memory.) that when executed by the processor causes the processor to be configured to:
obtaining one or more adjacency matrices, each adjacency matrix representing a group of tuples of the graph database comprising a same predicate (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.); and
the array including:
one or more indices each pointing to a sub-division of the adjacency matrix (e.g. Bauer, see paragraphs [0010-0017], which discloses providing one or more pointer that includes a predetermined length or size and can be stored in the same or inverse order as the bits are set in the bitmap.), and/or
one or more elements each representing a group of tuples of a RDF graph database of a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraphs [0356-0400, 0568-0572], which discloses storing the elements in the graph database.).
Baur does not explicitly disclose storing, for each of the one or more adjacency matrices, a data structure efficiently compressing the adjacent matrix, the data structure comprising an array, the array including: stored in one of a plurality of memory layout types, each memory layout type of the plurality of memory layout type using indices having a different size from other memory layout types of the plurality of memory layout types.
Xia teaches storing, for each of the one or more adjacency matrices, a data structure efficiently compressing the adjacent matrix, the data structure comprising an array (e.g. Xia, see paragraphs [0074-0075], which discloses in multi-modal graph representation, compressed sparse row is a common storage format to store the graph, where incoming edges are stored in compressed sparse column format and out-going edges in compressed sparse row format. Additionally, see Figure 3A, which discloses an input graph having vertices is presented in global adjacency matrix format and is divided into two partitions.), the array including:
stored in one of a plurality of memory layout types, each memory layout type of the plurality of memory layout type using indices having a different size from other memory layout types of the plurality of memory layout types (e.g. Xia, see paragraphs [0075-0077], where the graph processing system uses iterative graph computing with an edge-set based graph representation, where each subgraph partition is further converted into a set of edge-sets. Each edge-set contains vertices within a certain range by vertex ID, where incoming edges are stored in compressed sparse column format (memory layout type) and the out-going edges are stored in compressed row format (memory layout type).).
Baur is directed to efficient use of trie data structures in databases. Xia is directed to handling concurrent property graph queries. Both are analogous art because they are directed to improving graph database efficiencies and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Bauer with the teachings of Xia to include the claimed features with the motivation to improve graph database efficiencies.
As per claim 2, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 1, each of the one or more elements of the array including: a set of one or more coordinate pairs wherein each pair represents respective coordinates in the adjacency matrix (e.g. Bauer, see paragraph [0461], which discloses a value pair that are stored together.).
As per claim 3, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 2, wherein an element of the one or more elements includes a data structure of 32-bit indices, and with a layout having:
a tag representing the size and/or a type of the data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together and associated tags.), and
one or more pairs of coordinates comprising a row and a column of the adjacency matrix (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 4, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 2, wherein an element of the one or more elements includes a data structure of 16-bit indices with a layout having:
a tag representing the size and/or a type of the data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together and associated tags.), and
one or more pairs of coordinates comprising a row and a column of the adjacency matrix (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 5, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 2, wherein an element of the one or more elements includes a data structure of 8-bit indices with a layout having:
a tag representing the size and/or a type of the data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together and associated tags.), and
one or more pairs of coordinates comprising a row and a column of the adjacency matrix (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 6, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 2, wherein an element of the one or more elements includes a data structure of m-bit indices, the method further comprising:
determining a tag corresponding to the element and based on the one or more pairs of coordinates, the tag representing the size and/or a type of a data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 7, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 1, wherein the stored data structure further includes a bit array of bitmap representation of a group of RDF tuples in a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.).
As per claim 8, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 1, wherein the array of the stored data structure is a 2D array of size 2" x2" or a 1D array of size 22n, n being a positive integer (e.g. Bauer, see paragraph [0572-0573], which discloses arrays of long integers that include index sizes.).
As per claim 9, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 1, wherein the storing the data structure comprises:
allocating a slot thereby obtaining a root index, the root index representing a location of a tree data structure in a memory (e.g. Bauer, see paragraph [0475-0477], which discloses a root node that is associated with a root index and includes the stored pair values.); and
setting the array at the root index to an array of coordinate pairs (e.g. Bauer, see paragraph [0475-0477], which discloses a root node that is associated with a root index and includes the stored pair values.).
As per claim 10, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 1, wherein the method of storing an RDF graph further comprises implementing one or more of the following functions on the RDF graph database, each function being configured to apply on each adjacency matrix of the one or more adjacency matrices:
a Test function configured to check if a given cell of the adjacency matrix is set to a first specified value,
a Set function configured to set a given cell of the adjacency matrix to a first specified value,
a Reset function configured to set a given cell of the adjacency matrix to a second specified value,
a ScanAll function configured to output respective coordinates of all cells of the adjacency matrix with a first specified value,
a ScanRow function configured to output respective coordinates of cells of a given row in the adjacency matrix with a first specified value, and/or
a ScanColumn function configured to output respective coordinates of cells of a given column in the adjacency matrix with a first specified value (e.g. Bauer, see paragraphs [0412-0419], which discloses function calls and combined state of all operators perform instructions associated with the system.).
As per claim 11, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 1, wherein the obtaining one or more adjacency matrices comprises performing a vertical partitioning for each predicate of the graph database (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.).
As per claim 14, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 3, wherein an element of the one or more elements includes a data structure of 16-bit indices with a layout having:
a tag representing the size and/or a type of the data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together and associated tags.), and
one or more pairs of coordinates comprising a base column, a base row, an offset column, and an offset row of the adjacency matrix (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 15, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 3, wherein an element of the one or more elements includes a data structure of 8-bit indices with a layout having:
a tag representing the size and/or a type of the data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together and associated tags.), and
one or more pairs of coordinates comprising a base column, a base row, an offset column, and an offset row of the adjacency matrix (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 16, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 4, wherein an element of the one or more elements includes a data structure of 8-bit indices with a layout having:
a tag representing the size and/or a type of the data structure (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together and associated tags.), and
one or more pairs of coordinates comprising a base column, a base row, an offset column, and an offset row of the adjacency matrix (e.g. Bauer, see paragraph [0461, 0466-0468], which discloses a value pair that are stored together in a row and column of a database.).
As per claim 17, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 2, wherein the stored data structure further includes a bit array of bitmap representation of a group of RDF tuples in a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.).
As per claim 18, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 3, wherein the stored data structure further includes a bit array of bitmap representation of a group of RDF tuples in a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.).
As per claim 19, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 4, wherein the stored data structure further includes a bit array of bitmap representation of a group of RDF tuples in a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.).
As per claim 20, the modified teachings of Bauer with Xia teaches the computer-implemented method of claim 5, wherein the stored data structure further includes a bit array of bitmap representation of a group of RDF tuples in a respective sub-division of the adjacency matrix (e.g. Bauer, see paragraph [0569], which discloses the context of the Resource Description Framework (RDF) with its subject-predicate-object expressions, which further includes storing each occurring term and the document ID.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 February 19, 2026