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 23 February 2026, claims 1-21 are presently pending in the application, of which, claims 1, 9, and 20-21 are presented in independent form. The Examiner acknowledges amended claims 1 and 9. No claims were cancelled or newly added.
Priority
The Examiner acknowledges the instant application claims priority to European Patent Application No. 23307293,3, filed on 20 December 2023, and has been accorded the earliest effective file date.
Allowable Subject Matter
Claims 20 and 21 are allowed.
Response to Remarks/Arguments
All objections and/or rejections issued in the previous Office Action, mailed 22 October 2025, have been withdrawn, unless otherwise noted in this Office Action.
Applicant’s arguments, see page 11, filed supra, with respect to the rejection(s) of claims 1-19 under 35 U.S.C. 102(a)(2) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of a non-patent literature titled ‘iHDT++: Improving HDT for SPARQL Triple Pattern Resolution,’ by Hernandez-Illera, Antonio, et al, Journal of Intelligent & Fuzzy Systems, 39(2), 2020, (IDS Submission on 12/20/2024) in view of Das, Udipta, et al (U.S. 2024/0378070).
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 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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable by a non-patent literature titled ‘iHDT++: Improving HDT for SPARQL Triple Pattern Resolution,’ by Hernandez-Illera, Antonio, et al, Journal of Intelligent & Fuzzy Systems, 39(2), 2020, (IDS Submission on 12/20/2024 and known hereinafter as Hernandez-Illera) in view of Das, Udipta, et al (U.S. 2024/0378070 and known hereinafter as Das).
As per claim 1, Hernandez-Illera teaches a computer-implemented read-only data structure for archiving a snapshot of a Resource Description Framework (“RDF”) dataset into a file, the RDF dataset having one or more graphs and the read-only data structure being directly queried using all possible triple patterns of a SPARQL query engine, the data structure comprising:
an indexed list of the one or more graphs of the RDF dataset (e.g. Hernandez-Illera, see pages 1-2, which discloses an array of one or more indicies where each index point represents a group of tuples in the RDF graph database.);
for each graph of the RDF dataset, an indexed list of predicates (e.g. Hernandez-Illera, see pages 2-3, which discloses storing RDF graph database where one or more adjacent matrices include a number of subjects, predicates, and or objects that corresponds to triple exists in the RDF dataset.);
a dictionary mapping each RDF term of the dataset to a respective index and inversely (e.g. Hernandez-Illera, see pages 2-3, which discloses a dictionary mapping of each term between the graphs, where the RDF graph includes pattern of RDF tuples, that represent terms.); and
for each predicate indexed in the indexed list of predicates of each graph, an adjacency matrix representing a group of tuples of the RDF dataset, thereby storing a compressed representation of the RDF dataset that can be directly queried using all possible triple patterns of a SPARQL query engine (e.g. Hernandez-Illera, see pages 3-4, which discloses indexed predicates, adjacent matrix representing a pattern of tuples, using triple patterns of SPARQL query engine.).
Hernandez-Illera does not explicitly disclose obtaining a succinct read-only data structure supporting zero-copy reads.
Das teaches obtaining a succinct read-only data structure supporting zero-copy reads (e.g. Das, see paragraphs [0059-0060], which discloses support performing a zero-copy read or zero-copy write, where the read component may be configured for reading the data from the at least one memory buffer without copying the data to another memory location, where the at least one memory buffer is configured to accept the data from the file system server before the data is read from the at least one memory buffer.).
Hernandez-Illera is directed to enhancing iHDT++ that allows HDT++ files to be efficiently queried. Das is directed to zero-copy concurrent file sharing protocol access from a virtual machine. Both are analogous art because they are directed to efficient access to data without compression 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 Hernandez-Illera with the teachings of Das to include the claimed feature with the motivation to improve indexing of query resolution.
As per claim 9, Hernandez-Illera teaches a computer-implemented method for storing modifications to be applied on a read-only data structure for archiving a snapshot of a RDF dataset into a file, the RDF dataset including one or more graphs, the read-only data structure that can be directly queried using all possible triple patterns of a SPARQL query engine, the data structure including an indexed list of the one or more graphs of the RDF dataset, for each graph of the RDF dataset, an indexed list of predicates, a dictionary mapping each RDF term of the dataset to a respective index and inversely, and for each predicate indexed in the indexed list of predicates of each graph, an adjacency matrix representing a group of tuples of the RDF dataset, thereby obtaining a complete representation of the RDF dataset that can be directly queried using all possible triple patterns of a SPARQL query engine, the method for storing modifications (e.g. Hernandez-Illera, see paragraphs [0039-0049]) to be applied on a read-only data structure comprising:
obtaining a first list of added and/or deleted tuples of the RDF dataset (e.g. Hernandez-Illera, see pages 1-2, which discloses an array of one or more indicies where each index point represents a group of tuples in the RDF graph database.);
computing a first read-only data structure for archiving a snapshot of a RDF dataset into a file, wherein for each predicate of the added and/or deleted tuples of the RDF dataset of the first list (e.g. Hernandez-Illera, see pages 2-3, which discloses storing RDF graph database where one or more adjacent matrices include a number of subjects, predicates, and or objects that corresponds to triple exists in the RDF dataset.):
a first adjacency matrix represents the added tuples of the RDF dataset comprising the same predicate, and/or a second adjacency matrix represents the deleted tuples of the RDF dataset having the same predicate (e.g. Hernandez-Illera, see pages 3-4, which discloses indexed predicates, adjacent matrix representing a pattern of tuples, using triple patterns of SPARQL query engine.); and
storing the computed read-only data structure into a first file (e.g. Hernandez-Illera, see pages 3-4 which discloses maps of adjacent vertices to other adjacent vertices that have the same predicate, object, or label of the graph, where the data is stored in a file.).
Hernandez-Illera does not explicitly disclose obtaining a succinct read-only data structure supporting zero-copy reads.
Das teaches obtaining a succinct read-only data structure supporting zero-copy reads (e.g. Das, see paragraphs [0059-0060], which discloses support performing a zero-copy read or zero-copy write, where the read component may be configured for reading the data from the at least one memory buffer without copying the data to another memory location, where the at least one memory buffer is configured to accept the data from the file system server before the data is read from the at least one memory buffer.).
Hernandez-Illera is directed to enhancing iHDT++ that allows HDT++ files to be efficiently queried. Das is directed to zero-copy concurrent file sharing protocol access from a virtual machine. Both are analogous art because they are directed to efficient access to data without compression 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 Hernandez-Illera with the teachings of Das to include the claimed feature with the motivation to improve indexing of query resolution.
As per claims 2 and 13, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 1 and the computer-implemented method of claim 9, respectively, wherein the dictionary has a fixed length when each value of the dictionary has a length that is equal or smaller than a predetermined threshold, the predetermined threshold being 2, 4 or 8 Bytes (e.g. Hernandez-Illera, see pages 3-4, which discloses 2, 4 or 8 bytes in the dictionary.).
As per claims 3 and 14, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 2 and the computer-implemented method of claim 9, respectively, wherein the dictionary has a variable length when at least one value of the dictionary has a length larger than the predetermined threshold, each value having a length larger than the predetermined threshold is indexed to and stored in an overflow data structure, the overflow data structure being part of the dictionary (e.g. Hernandez-Illera, see pages 3-4, which discloses each subtype may have a base pair r, c, and/or a vector of variable length depending on the subtype. The length refers to the number of used row-column coordinates in the data structure).
As per claims 4 and 15, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 1 and the computer-implemented method of claim 9, respectively, wherein at least parts of the dictionary are encoded, wherein values are encoded using IEEE Standard for Floating-Point Arithmetic (IEEE 754-2019) or prefix of values are encoded using a hexadecimal key (e.g. Hernandez-Illera, see pages 3-4 that illustrates a prefix of values.).
As per claims 5 and 16, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 1 and the computer-implemented method of claim 9, respectively, wherein the dictionary includes two or more dictionaries, each dictionary mapping each RDF term of an RDF datatype of the dataset to a respective index and inversely or each dictionary mapping each RDF term of an IRI prefix of the dataset to a respective index and inversely (e.g. Hernandez-Illera, see paragraphs [0044-0049], which discloses a dictionary mapping of each term between the graphs, where the RDF graph includes pattern of RDF tuples, that represent terms.).
As per claims 6 and 17, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 1 and the computer-implemented method of claim 9, respectively, wherein the adjacency matrix for each indexed predicate is obtained by performing a vertical partitioning for each predicate of the RDF dataset, the vertical partitioning being performed by a technique selected among:
a standard K2-triple, preferably built from a Dynamic K2 (e.g. Hernandez-Illera, see pages 3-4, which discloses a dynamic K2 Tree);
two static B+trees, where a first static B+tree expresses subject-to-object (S,O) correspondence and a second one expresses object-to-subject (O,S) correspondence; and a static XAM Tree engraved from a XAMTree as a read-only XAMTree (e.g. Hernandez-Illera, see pages 3-4, which discloses a read-only XAMTree.).
As per claims 7 and 18, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 1 and the computer-implemented method of claim 9, respectively, wherein each entry of the data structure has a maximum size comprised between 32 and 256 bits or the size of each entry is encoded on 32 bits or 64 bits (e.g. Hernandez-Illera, see pages 3-4, which discloses aa 32 bit size of the data structure.).
As per claims 8 and 19, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented read-only data structure of claim 1 and the computer-implemented method of claim 9, respectively, wherein the read-only data structure is a binary file including:
a Header comprising an offset to an end of a Footer record position, and a section comprised between the Header and the Footer, the Footer having an offset to a starting of a Header record position (e.g. Hernandez-Illera, see pages 3-4 and Figure 6A, which discloses a header and footer size.), and wherein the section stores:
the dictionary, and for each graph, the indexed list of predicates of the graph and the adjacency matrix of each predicate indexed in the indexed list of predicates of the graph (e.g. Hernandez-Illera, see pages 3-4, which discloses read only version of the graph database by scanning preexisting adjacent matrix or from a XAMTree.).
As per claim 10, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented method of claim 9, further comprising:
obtaining a second list of added and/or deleted tuples of the RDF dataset (e.g. Hernandez-Illera, see pages 3-4, which discloses obtaining graph results that include modification and deletion of relationships in conjunction with various data-processing operations in an RDF graph database.);
computing a second read-only data structure for archiving a snapshot of a RDF dataset into a file (e.g. Hernandez-Illera, see pages 3-4, which discloses obtaining graph results that include modification and deletion of relationships in conjunction with various data-processing operations in an RDF graph database.), wherein for each predicate of the added and/or deleted tuples of the RDF dataset of the first and second lists:
a first adjacency matrix represents the added tuples of the RDF dataset comprising the same predicate, and/or a second adjacency matrix represents the deleted tuples of the RDF dataset having the same predicate (e.g. Hernandez-Illera, see pages 2-3, which discloses indexed predicates, adjacent matrix representing a pattern of tuples, using triple patterns of SPARQL query engine.); and
storing the computed read-only data structure into a second file, thereby by forming modifications to be applied on the read-only data structure (e.g. Hernandez-Illera, see pages 2-3, which discloses indexed predicates, adjacent matrix representing a pattern of tuples, using triple patterns of SPARQL query engine.).
As per claim 11, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented method of claim 9, wherein the obtaining further comprises obtaining a list of added and/or deleted tuples of the RDF dataset for each graph of the RDF set, and wherein the computing further comprises computing a first adjacency matrix and second adjacency matrix for each graph of the RDF set (e.g. Hernandez-Illera, see pages 2-3, which discloses indexed predicates, adjacent matrix representing a pattern of tuples, using triple patterns of SPARQL query engine.).
As per claim 12, the modified teachings of Hernandez-Illera and Das teaches the computer-implemented method of claim 9, further comprising:
computing a mapping between indices of the dictionary of the RDF dataset and indices of each dictionary of each list of added and/or deleted tuples of the RDF dataset (e.g. Hernandez-Illera, see pages 3-4, which discloses a dictionary mapping of each term between the graphs, where the RDF graph includes pattern of RDF tuples, that represent terms.); and
storing into a file the computed mapping (e.g. Hernandez-Illera, see pages 3-4, which discloses a dictionary mapping of each term between the graphs, where the RDF graph includes pattern of RDF tuples, that represent terms.).
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.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 March 10, 2026