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 05/26/2026 has been entered. Claims 1 – 4, 10 – 13, 19 and 20 have been amended. Claims 1 – 20 are currently pending.
Response to Arguments
35 U.S.C. §103
3. Applicant's arguments, see Remarks pp. 8 - 13, filed 05/26/2026, with
respect to the rejections of claims 1 - 20 under 35 U.S.C. §103 have been fully
considered and they are persuasive.
The crux of Applicant’s arguments is that the amendments to the independent claims are not taught by the art of reference.
Examiner respectfully agrees
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini
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, 4, 6, 10, 13, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, and in further view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini
Regarding claim 1 Miller teaches a computing device (computer systems [0068]) comprising: a memory; (memory [0078]) and one or more processors (query processor [0088] – [0092]) that are configured (configured to [0064]) to execute (execute [0092]) machine readable instructions (instructions [0062], [0089], [0252], [0253], [0312]) stored in (stored by [0114]) the memory (memory [0078]) for: receiving a search query (receiving search query [0089], [0102]) associated with (associated with [0112]) a plurality of sets of structured (events of “structured” data [0059]) and unstructured data; (events of “unstructured” data [0057], [0059], [0061], [0387]) joining (using a late binding schema applied to data in the events to extract values for specific fields [0062]) such as “joining” SEE ALSO paragraph [0065], [0066] for sources of fields comprising “structured” and “unstructured” data the plurality of sets of structured (events of “structured” data [0059]) and unstructured data (events of “unstructured” data [0057], [0059], [0061], [0387]) initiating a search (facilitate searching [0078]) of the plurality of sets of structured (events of “structured” data [0059])and unstructured data(events of “unstructured” data [0057], [0059], [0061], [0387]) by providing (distribute [0102]) such as “providing” the search query (search query [0089], [0102]) to the interface layer, (the broadest reasonable interpretation of an “interface layer” in light of applicant’s specification [0024] includes a mechanism for communication and control between a front end and stored data such as a databases) (database [0059] such as “interface layer” wherein the search of the plurality of sets of structured (events of “structured” data [0059]) and unstructured data; (events of “unstructured” data [0057], [0059], [0061], [0387]) determining (determining [0073]) whether one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122]) within the (within the [0272] – [0274]) interface layer (the broadest reasonable interpretation of an “interface layer” in light of applicant’s specification [0024] includes a mechanism for communication and control between a front end and stored data such as a databases) (database [0059] such as “interface layer” satisfies a condition (An example of an extraction rule for extracting field label-value pairs is a rule that identifies a field label for a field based on text on the left hand side of an equal sign ("="), and identifies a value for a new data item or value associated with the field label based on text on the right hand side of the equal sign within a value of a data item [0254]) EXAMPLE “itemid=EST-14” [0255] determining (determining [0073]) whether one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122]) within the (within the [0272] – [0274]) interface layer (the broadest reasonable interpretation of an “interface layer” in light of applicant’s specification [0024] includes a mechanism for communication and control between a front end and stored data such as a databases) (database [0059] such as “interface layer” exceeds (occurrences exceed an upper occurrence threshold [0403]) a similarity score (similarity score [0322]) determining whether (determine whether [0322]) one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122])within the(within the [0272] – [0274]) interface layer (the broadest reasonable interpretation of an “interface layer” in light of applicant’s specification [0024] includes a mechanism for communication and control between a front end and stored data such as a databases) (database [0059] such as “interface layer” are returned as matches (matches one or more [0322]) merging (merging operation [0101]) the one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122])that satisfy the condition, (An example of an extraction rule for extracting field label-value pairs is a rule that identifies a field label for a field based on text on the left hand side of an equal sign ("="), and identifies a value for a new data item or value associated with the field label based on text on the right hand side of the equal sign within a value of a data item [0254]) EXAMPLE “itemid=EST-14” [0255] one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122])that exceeds(occurrences exceed an upper occurrence threshold [0403]) the similarity score (similarity score [0322])and the one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122])returned as matches(matches one or more [0322], [0323]) into a result set; (results generated [0085]) and returning the result set (one technique streams results back to a client in real-time as they are identified. Another technique waits to report results to the client until a complete set of results is ready to return to the client. Yet another technique streams interim results back to the client in real-time until a complete set of results is ready, and then returns the complete set of results to the client. In another technique, certain results are stored as "search jobs," and the client may subsequently retrieve the results by referencing the search jobs. [0085]) in response to (in response to [0108]) the search query (search query [0089], [0102])
Miller does not fully disclose wherein the interface layer comprises a plurality of data store partitions including data of the plurality of sets of structured and unstructured data, and is implemented using a hash table, vector embeddings, key-value index embeddings, or feature embeddings; uses a retrieval operator, a user-defined function (UDF) operator, and an artificial intelligence (AI) operator submitted to the interface layer; associated with the retrieval operator; associated with the UDF operator; from the AI operator, wherein the AI operator provides the matches from one or more AI models; associated with the retrieval operator; associated with the UDF operator; from the AI operator; receiving a search query associated with sets of structured data and sets of unstructured data, the search query comprising a bind sequence that triggers joining the sets of structured data and the sets of unstructured data; in response to receiving the search query, joining the sets of structured data and the sets of unstructured data into an interface layer according to the bind sequence; wherein the one or more data items that satisfy the condition associated with the retrieval operator are stored in a first table or dataset, the one or more data items that exceeds the similarity score associated with the UDF operator are stored in a second table or dataset, the one or more data items returned as matches from the Al operator are stored in a third table or dataset, and wherein the result set comprises a combined dataset or table various formats that merges the first, the second, and the third tables or datasets;
Benjaimin-Deckert teaches wherein the interface layer (database management interface [0140]) comprises a plurality of data store partitions including data of the plurality of sets of structured (In operation 712, the unstructured data record is updated or rewritten by adding or including, with the original data therein, the primary key-name:key-value pair and the hash value as an indexing key to create a modified data record. [0147] – [0148]) NOTE this modified data record is able to be stored in a structured database (ABS., Fig 6, (6120 (614) and unstructured data, (an unstructured data record that adheres to JavaScript Object Notation (JSON) or binary JavaScript Object Notation (BSON) [0140]) and is implemented using a hash table, (In operation 710, the primary key-value in a primary key-name:key-value pair is hashed, using any known hashing algorithm, to obtain a hash value [0144]) vector embeddings, key-value index embeddings, (generated primary key value from primary key name [0142] – [0143]) or feature embeddings;
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 Miller in view of Sathe to incorporate the teachings of Benjaimin-Deckert wherein the interface layer comprises a plurality of data store partitions including data of the plurality of sets of structured and unstructured data, and is implemented using a hash table, vector embeddings, key-value index embeddings, or feature embeddings. By doing so the method includes
updating a Key-Sequenced Data Set (KSDS) VSAM database index to include an entry for the modified data record, the entry including the hash value. Benjaimin-Deckert [0008]
Finnerty teaches uses a retrieval operator, (The batch mode operator may be a nested loop join operator (NLJ) Col 2 ln 24, Col 7 ln 31) a user-defined function (UDF) operator, (user defined function “classify_event” Col 5 ln 15 – 36) and an artificial intelligence (AI) operator (machine learning (ML) function Col 7 ln 6)” the retrieval operator; (The batch mode operator may be a nested loop join operator (NLJ) Col 2 ln 24, Col 7 ln 31)” associated with (associated with Col 5 ln 57) the UDF operator; (user defined function “classify_event” Col 5 ln 15 – 36) from the AI operator, (machine learning (ML) function Col 7 ln 6) wherein the AI operator(machine learning (ML) function Col 7 ln 6) provides the matches from one or more AI models; (specialized models Col 3 ln 5) associated with (associated with Col 5 ln 57) the retrieval operator; (The batch mode operator may be a nested loop join operator (NLJ) Col 2 ln 24, Col 7 ln 31) associated with(associated with Col 5 ln 57) the UDF operator; (user defined function “classify_event” Col 5 ln 15 – 36) from the AI operator (machine learning (ML) function Col 7 ln 6) wherein the one or more data items (one row from each of the outer node and the inner node Col 7 ln 31 – 34) that satisfy the condition associated with (determine if the extracted rows pass the join, Col 7 ln 33 – 34) the retrieval operator (The batch mode operator may be a nested loop join operator (NLJ) Col 2 ln 24, Col 7 ln 31) are stored in a first table or dataset, (tuple store Col 2 ln 31) the one or more data items (one row from each of the outer node and the inner node Col 7 ln 31 – 34) that exceeds the similarity score associated with (rows are being analyzed until a particular number of results that match particular conditions are found Col 6 ln 5 – 10)the UDF operator (user defined function “classify_event” Col 5 ln 15 – 36) are stored in a second table or dataset, (plurality of rows Col 5 ln 59 - 60) the one or more data items (one row from each of the outer node and the inner node Col 7 ln 31 – 34) returned as matches (that match Col 6 ln 8) from the Al operator (machine learning (ML) function Col 7 ln 6) are stored in a third table or dataset, (fetched future rows of machine learning results Col 2 ln 34 - 35) and wherein the result set comprises a combined dataset or table various formats that merges the first, the second, and the third tables or datasets; ( The batch mode operator can be extended to include an execution context that is passed to the machine learning function. The execution context may be implemented as a tuple store that includes a list of rows that are to be operated on by the machine learning function. The machine learning function can obtain machine learning results for the batch of machine learning requests, and then can pass the results back to the nested loop join operator one row at a time Col 2 ln 35 – 39) such as “combined result set”
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 Miller to incorporate the teachings of Finnerty wherein the one or more data items that satisfy the condition associated with the retrieval operator are stored in a first table or dataset, the one or more data items that exceeds the similarity score associated with the UDF operator are stored in a second table or dataset, the one or more data items returned as matches from the Al operator are stored in a third table or dataset, and wherein the result set comprises a combined dataset or table various formats that merges the first, the second, and the third tables or datasets. By doing so embodiments may implement one or more
query transforms 300 to ensure batch mode is used whenever possible, while also allowing users to formulate queries in the most natural way. Finnerty Col 8 ln 40 - 43
Priyadarshini teaches receiving a search query (Fig. 5, (510) receive a query [0042]) associated with sets of structured data and sets of unstructured data, (with respect to structured data and unstructured data [0041]) the search query (the query [0042]) comprising a bind sequence (a single data frame having both a set of structured
data and a set of unstructured data is constructed in a dynamic fashion by the database driver [0016]) such as “bind sequence” that triggers (occur in an automated fashion [0043]) joining the sets of structured data and the sets of unstructured data; (In response to receiving the query, a single data frame having both a set of structured data and a set of unstructured data is constructed in a dynamic fashion by the database driver at block 530 [0042]) in response to receiving the search query, (Fig. 5, (510) receive a query [0042]) joining the sets of structured data and the sets of unstructured data into an interface layer (Figs. 3 – 5, Java Database Connectivity (JDBC) submitted to the interface layer; (Figs. 3 – 5, Java Database Connectivity (JDBC) associated with (associated with [0059]) results-set [0008] – [0010]) such as “interface layer” according to the bind sequence (a single data frame having both a set of structured data and a set of unstructured data is constructed in a dynamic fashion by the database driver [0016]) such as “bind sequence”
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 Miller to incorporate the teachings of Priyadarshini whereby receiving a search query associated with sets of structured data and sets of unstructured data, the search query comprising a bind sequence that triggers joining the sets of structured data and the sets of unstructured data; in response to receiving the search query, joining the sets of structured data and the sets of unstructured data into an interface layer according to the bind sequence. By doing so may provide performance or efficiency benefits for structured data and unstructured data integration management to provide the valid JDBC results-set (e.g., speed, flexibility, responsiveness, resource usage, productivity). Priyadarshini [0020]
Claims 10 and 19 correspond to claim 1 and are rejected accordingly
Regarding claim 4 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller as modified further teaches, wherein determining whether (determine whether [0322]) the one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122]) within the (within the [0272] – [0274])interface layer (the broadest reasonable interpretation of an “interface layer” in light of applicant’s specification [0024] includes a mechanism for communication and control between a front end and stored data such as a databases) (database [0059] such as “interface layer” satisfies the condition (An example of an extraction rule for extracting field label-value pairs is a rule that identifies a field label for a field based on text on the left hand side of an equal sign ("="), and identifies a value for a new data item or value associated with the field label based on text on the right hand side of the equal sign within a value of a data item [0254]) comprises determining an attribute (incident attribute fields 711 [0119]) associated with the condition (An example of an extraction rule for extracting field label-value pairs is a rule that identifies a field label for a field based on text on the left hand side of an equal sign ("="), and identifies a value for a new data item or value associated with the field label based on text on the right hand side of the equal sign within a value of a data item [0254]) and returning (returns [0079], [0085], [0091], [0092], [0458]) the one or more data items (data items [0055], [0056], [0059], [0066], [0069], [0122]) that comprise the attribute (incident attribute fields 711 [0119])
Miller as modified does not fully disclose associated with the retrieval operator
Finnerty teaches associated with (associated with Col 5 ln 57) the retrieval operator (The batch mode operator may be a nested loop join operator (NLJ) Col 2 ln 24, Col 7 ln 31)
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 Miller in view of Benjamin-Deckert and Priyadarshini to incorporate the teachings of Finnerty whereby uses a retrieval operator. By doing so it enables an execution context 204 to the batch mode operator. With the execution context, the ML function invoked by the batch
mode operator can request more rows from the outer node until a batch is full. Finnerty Col 7 ln 35 - 40
Claim 13 corresponds to claim 4 and is rejected accordingly
Regarding claim 6 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller does not fully disclose wherein the UDF operator comprises one or more user-defined functions that determine the similarity score.
Finnerty teaches wherein the UDF operator” comprises one or more user-defined functions (user defined function “classify_event” Col 5 ln 15 – 36) that determine the similarity score (matching a particular conditions Col 6 ln 5 – 10) such as “similarity score”
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 Miller in view Miller in view of Benjamin-Deckert, and Priyadarshini to incorporate the teachings of Finnerty wherein the UDF operator comprises one or more user-defined functions that determine the similarity score. By doing so embodiments may implement one or more
query transforms 300 to ensure batch mode is used whenever possible, while also allowing users to formulate queries in the most natural way. Finnerty Col 8 ln 40 - 43
Claim 15 corresponds to claim 6 and is rejected accordingly
Claims 2, 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller in view of Miller, in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini and in further view of Malhotra et al., (United States Patent Publication Number 20220083611) hereinafter Malhotra
Regarding claim 2 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller as modified teaches wherein the sets of structured data (events of “structured” data [0059]) and the sets of unstructured data; (events of “unstructured” data [0057], [0059], [0061], [0387])
Miller does not fully disclose comprises an in-memory semantic graph database.
Malhotra teaches comprises an in-memory semantic graph database (Fig. 2 WEBDAS-Data may reside in an in-memory graph database GDBMS 260 [0076], [0091])
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 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini to incorporate the teachings of Sathe comprises an in-memory semantic graph database. By doing so the modeled graph structures may be stored in representations that are amenable or suitable for semantic queries. Malhotra [0066]
Claims 11 and 20 correspond to claim 2 and are rejected accordingly
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini and in further view of Bierner et al., (United States Patent Publication Number 20230021868) hereinafter Bierner
Regarding claim 3 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller as modified teaches wherein the plurality of sets of structured (events of “structured” data [0059]) and unstructured data; (events of “unstructured” data [0057], [0059], [0061], [0387])
Miller does not fully disclose are partitioned into a plurality of shards.
Bierner teaches are partitioned into a plurality of shards (single-name shards are maintained within a threshold variance such each of the single-name shards having within ±1 % of the number of records of the average or median value for single-name shards, while multi-name shards and no-name shards are separately partitioned. [0131])
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 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini to incorporate the teachings of Bierner wherein partitioned into a plurality of shards. By doing so the sharding in a plurality of dimensions yields time and cost savings over existing approaches and solutions. Bierner [0007]
Claim 12 corresponds to claim 3 and is rejected accordingly
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller, in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini and in further view of Antoniades et al., (United States Patent Publication Number 20220309116) hereinafter Antoniades
Regarding claim 5 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller does not fully disclose wherein the similarity score is determined based on numerical, geometric, combinatorial, or string-matching algorithms using distributed methods.
Antoniades teaches wherein the similarity score is determined (computing a qualitative similarity score [0050], [0063], [0064]) based on numerical, (a numerical
similarity threshold. [0064]) geometric, combinatorial, or string-matching algorithms (top matching patterns [0050]) using distributed methods (sparse distributed representations of the highest occurring terms ( e.g. words or phrases). [0049])
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 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini to incorporate the teachings of Antoniades wherein the similarity score is determined based on numerical, geometric, combinatorial, or string-matching algorithms using distributed methods. By doing so the method ranks qualifying patterns based on a similarity score and retrieves the top matching patterns based on a threshold, for example. Antoniades [0050]
Claim 14 corresponds to claim 5 and is rejected accordingly
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller , in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini and in further view of Shi et al., (United States Patent Publication Number 20210191990) hereinafter Shi
Regarding claim 7 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller does not fully disclose wherein the matches from the AI operator comprise cross-modality predictions.
Shi teaches wherein the matches (matching text description [0066]) from the AI operator (deep binary hashing [0016]) comprise cross-modality (cross-modal retrieval [0016]) predictions (prediction [0017], [0018], [0045], [0049], [0065])
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 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini to incorporate the teachings of Shi wherein the matches from the AI operator comprise cross-modality predictions. By doing so Query module 480 identifies and retrieves the item in database 410 that is semantically closest to query item 420 in accordance with the prediction process. Shi [0073]
Claim 16 corresponds to claim 7 and is rejected accordingly
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini and in further view of Subrahmanya et al., (United States Patent Publication Number 2015/0348160) hereinafter Subrahmanya
Regarding claim 8 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller does not fully disclose wherein the result set comprises a subset of a semantic graph that satisfies the condition associated with the retrieval operator.
Subrahmanya teaches wherein the result set comprises a subset (subset of the result set can be attributes with the highest frequency amongst the products [0042]) of a semantic graph (semantic graph on products [0047]) that satisfies (that match [0031]) such as “satisfies” the condition (hard conditions on the attributes [0031]) associated with (associated with [0032]) the retrieval operator (relevance (query, {pgl , pg2, ... , pgN})=sum ofrelevance over all products in (pgl Upg2U ... UpgN) relevanceBase
( query, product), where pgX=product group X, N=the number of product groups included in the set) [0039]) such as “retrieval operator”
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 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini to incorporate the teachings of Subrahmanya wherein the result set comprises a subset of a semantic graph that satisfies the condition associated with the retrieval operator. By doing so the subset of the result set of attributes can include the most relevant attributes, which can beneficially limit the number of attributes that need to be considered in blocks 503 and 504, which can reduce processing time. Subrahmanya [0042].
Claim 17 corresponds to claim 8 and is rejected accordingly
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (United States Patent Publication Number 20190155803 ), hereinafter referred to as Miller in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert, in view of Finnerty et al., (United States Patent Number 11,966,396) hereinafter Finnerty, in view of Priyadarshini et al., (United States Patent Publication Number 2018/0032591) hereinafter Priyadarshini and in further view of Ciravegna et al., (United States Patent Publication Number 20100174704) hereinafter Ciravegna
Regarding claim 9 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini teaches the computing device of claim 1,
Miller does not fully disclose wherein the search query is written in a SPARQL query language.
Ciravegna teaches wherein the search query is written in a SPARQL query language (Query languages, such as, for example, SPARQL (SPARQL Protocol and RDF
Query Language) may be used to perform queries (searches) on the metadata in the triplestore data 108. [0043], [0044], [0047])
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 Miller in view of Benjamin-Deckert, Finnerty and Priyadarshini to incorporate the teachings of wherein the search query is written in a SPARQL query language. By doing so the query builder service may construct a SPARQL query using semantic search terms and pass the query to the triplestore interface 106. Ciravegna [0047]
Claim 18 corresponds to claim 9 and is rejected accordingly
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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