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 .
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 18TH December 2025 has been entered.
Response to Amendment
3. The Amendment filed on 18TH December 2025 has been entered. Claims 1, 10 and 19 have been amended, claims 1 - 20 are pending in the application.
Response to Arguments
35 U.S.C. §103
4. Applicant's arguments, see Remarks pp. 7 -11, filed 18TH December 2025, with
respect to the rejections of claims 1-20 under 35 U.S.C. §103 have been fully
considered and they are persuasive.
Applicant argues that the interface layer of the Miller reference is deficient in teaching the amendments to the independent claims. At a minimum the “interface template” of Miller does not teach or suggest the “interface layer” of amended claim 1. As currently amended, applicant argues that the interface template of the Miller reference does not teach, “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”
Examiner respectfully agrees
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert
Claim Rejections – 35 U.S.C. §103
5. 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.
6. 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, 10, 13 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 Sathe et al. (United States Patent Publication Number 20110022600), hereinafter referred to Sathe and in further view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert
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
Sathe teaches uses a retrieval operator, (search engine [0076]) such as “retrieval operator” a user-defined function (UDF) operator, (a fuzzy similarity measure between the attributes based on statistics [0076]) such as “a user-defined function (UDF)” operator” and an artificial intelligence (AI) operator (IDF [0056], [0059], [0061], ]0075]) such as “an artificial intelligence (AI) operator” submitted to the interface layer; (user interface [0064]) associated with (associated with [0059]) the retrieval operator; (search engine [0076]) such as “retrieval operator” associated with (associated with [0059]) the UDF operator; (a fuzzy similarity measure between the attributes based on statistics [0076]) such as “a user-defined function (UDF)” from the AI operator, (IDF [0056], [0059], [0061], ]0075]) such as “an artificial intelligence (AI) operator” wherein the AI operator(fuzzy similarity measure [0076]) such as “an artificial intelligence (AI) operator” provides the matches from one or more AI models; (fuzzy similarity measure [0076]) such as “an artificial intelligence (AI) operator” associated with (associated with [0059]) the retrieval operator; (search engine [0076]) such as “retrieval operator” associated with(associated with [0059]) the UDF operator; (a fuzzy similarity measure between the attributes based on statistics [0076]) such as “a user-defined function (UDF)” from the AI operator (a fuzzy similarity measure between the attributes based on statistics [0076]) such as “a user-defined function (UDF)”
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 Sathe whereby 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. By doing so it enables answering user queries over very large collections of documents containing structured and unstructured data. Sathe [0021]
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]
Claims 10 and 19 correspond to claim 1 and are rejected accordingly
Regarding claim 4 Miller in view of Sather and Benjamin-Deckert teaches the computing device of claim 1,
Miller as modified further teaches, wherein 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” 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]) 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
Sathe teaches associated with the retrieval operator(search engine [0076]) 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 to incorporate the teachings of Sathe whereby uses a retrieval operator. By doing so it enables answering user queries over very large collections of documents containing structured and unstructured data. Sathe [0021]
Claim 13 corresponds to claim 4 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 Sathe et al. (United States Patent Publication Number 20110022600), hereinafter referred to Sathe, in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert and in further view of Malhotra et al., (United States Patent Publication Number 20220083611) hereinafter Malhotra
Regarding claim 2 Miller in view of Sathe and Benjamin-Deckert 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 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 Sathe and Benjamin-Deckert 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 Sathe et al. (United States Patent Publication Number 20110022600), hereinafter referred to Sathe in view of Benjamin-Deckert (United States Patent Publication Number 20190179948) hereinafter Benjamin-Deckert and in further view of Bierner et al., (United States Patent Publication Number 20230021868) hereinafter Bierner
Regarding claim 3 Miller in view of Sathe and Benjamin-Deckert 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 Sathe and Benjamin-Deckert 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 Sathe et al., (United States Patent Publication Number 20110022600), hereinafter referred to Sathe in view of Benjamin-Deckert and in further view of Antoniades et al., (United States Patent Publication Number 20220309116) hereinafter Antoniades
Regarding claim 5 Miller in view of Sathe and Benjamin-Deckert 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 Sathe and Benjamin-Deckert 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 6 and 15 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 Sathe et al., (United States Patent Publication Number 20110022600), hereinafter referred to Sathe in view of Benjamin-Deckert and in further view of Ripley et al., (United States Patent Publication Number 2004/0078364) hereinafter Ripley
Regarding claim 6 Miller in view of Sathe and Benjamin-Deckert 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.
Ripley teaches wherein the UDF operator (SM 130 [0081]) such as “UDF operator” comprises one or more user-defined functions (Fig. 10 example dataset resulting form and SQL statement calling the UDF MYFUNCTION; [0034]) SEE ALSO user defined functions [00789], [0081], [0082], [0098], [0114] that determine the similarity score (to yield an overall similarity score for the document. [0080])
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 and Benjamin-Deckert to incorporate the teachings of Ripley wherein the UDF operator comprises one or more user-defined functions that determine the similarity score. By doing so the SSE's similarity measures are implemented in the DBMS as User Defined Functions (UDFs). These are represented as Ml, M2, ... , Mn in the SQL statement shown above. The input parameters passed to the measure UDF are the anchor value and target field that contains the target values to be scored. The normalized weights Wl, W2 ... Wn are treated as constants, so once the UDF results are available, the overall score can be calculated as a simple weighted sum: S-s1 *W1+s2*W2+ . .. +sn*Wn. Ripley [0201].
Claim 15 corresponds to claim 6 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 Sathe et al., (United States Patent Publication Number 20110022600), hereinafter referred to Sathe in view of Benjamin-Deckert and in further view of Shi et al., (United States Patent Publication Number 20210191990) hereinafter Shi
Regarding claim 7 Miller in view of Sathe and Benjamin-Deckert 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 Sathe and Benjamin-Deckert 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 Sathe et al., (United States Patent Publication Number 20110022600), hereinafter referred to Sathe in view of Benjamin-Deckert and in further view of Subrahmanya et al., (United States Patent Publication Number 2015/0348160) hereinafter Subrahmanya
Regarding claim 8 Miller in view of Sathe and Benjamin-Deckert 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 Sathe and Benjamin-Deckert 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 Sathe et al., (United States Patent Publication Number 20110022600), hereinafter referred to Sathe in view of Benjamin-Deckert and in further view of Ciravegna et al., (United States Patent Publication Number 20100174704) hereinafter Ciravegna
Regarding claim 9 Miller in view of Sathe and Benjamin-Deckert 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 Sathe and Benjamin-Deckert 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
7. The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Brandon Svec (United States Patent Publication Number 20190155803 ) teaches, “Fig. 7, (704) Step for generating hash ID by hashing key-value pairs (704)”
8. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Kweku Halm whose telephone number is (469) 295-
9144. The examiner can normally be reached on 7:30AM - 5:30PM Mon - Thur. If
attempts to reach the examiner by telephone are unsuccessful, the examiner's
supervisor, Sanjiv Shah can be reached on (571) 272-4098. The fax phone
number for the organization where this application or proceeding is assigned is 571-273-
8300.
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/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166