DETAILED ACTION
This Office action has been issued in response to amendment filed February 23, 2026.
Claims 1, 6, 10 and 15 have been amended. Claims 1-18 are pending. Applicant’s arguments are carefully and respectfully considered and some are persuasive, while others are not. Accordingly rejections have been removed where arguments were persuasive, but rejections have been maintained where arguments were not persuasive. Also, a new rejections based on the newly added amendments have been set forth. Accordingly, claims 1-18 are rejected and this action has been made FINAL, as necessitated by amendment.
Response to Arguments
Applicant’s remarks and arguments directed to 35 USC 103 rejection, presented on 02/23/26 have been fully considered but they are moot in view of the new ground of rejection presented in this office action.
Objection
In claims 1, 6, 19 and 15 recited the limitations of “wherein K is an integer greater than zero or wherein each of K, N, M is an integer greater than zero”. The specification merely indicating “ value of N is increased to P, which is greater than N. For example, if N is 30, then P may be 60” ([0121]). However, it does not define wherein K is an integer greater than zero or wherein each of K, N, M is an integer greater than zero. Any number or a threshold value more than zero can read the amended claim recited limitation. The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o).
Claim Rejections- 35 USC § 103
5. 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.
6. 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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
7. Claims 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shu et al. (US 2023/0273940 A1), hereinafter Shu in view of Amer-Yahia et al. (US 2006/0112090 A1), hereinafter Amer-Yahia.
As for claim 1, Shu teaches a method comprising: receiving a vector query that targets a first sharded table and a second sharded table in a vector database (see [0003], e.g., embedding for the query represents the query as a multidimensional vector in a latent space, For a received query, the online system identifies items having embeddings nearest to the embedding for the query in the latent space to retrieve items for the query, [0006], e.g., an index key and generates different indices that each correspond to a different value of the specific attribute (e.g., index, indices or databases defining tables), [0008]);
in response to receiving the vector query, determining whether the vector query includes a non-collocated join condition on the first sharded table and the second sharded table (see [0003], e.g., query represents the query as a multidimensional vector in a latent space, [0008], e.g., receives a query from a user, the online system generates an embedding for the query (e.g., in response to receiving the vector query), [0054], e.g., an aggregate frequency of access for the specific shard as a combination (e.g., a sum) of frequencies with which the online system accessed indices stored in the specific shard (e.g., the specific shard as a combination) (e.g., a sum of frequencies with which the online system accessed indices stored in the specific shard describing a non-collocated join condition on the first sharded table and the second sharded table);
in response to determining that the vector query includes the non-collocated join condition: identifying a plurality of shards storing the first and second sharded tables; retrieving, from the plurality of shards, first data pertaining to the first sharded table and second data pertaining to the second sharded table (see [0003], e.g., query represents the query as a multidimensional vector in a latent space, [0006], e.g., an index key and generates different indices that each correspond to a different value of the specific attribute. Each index includes embeddings for items having a value of the specific attribute in the item database matching the value corresponding to the index (e.g., the index associated with databases which read the first or second table), [0008], e.g., receives a query from a user, the online system generates an embedding for the query, [0054], e.g., an aggregate frequency of access for the specific shard as a combination (e.g., a sum) of frequencies with which the online system accessed indices stored in the specific shard);
performing a join operation on the first data and the second data, wherein performing the join operation generates temporary results (see [0037], e.g., the machine-learned item availability model weight these factors differently, where the weights are a result of a “learning” or training process on the training datasets, [0054], e.g., an aggregate frequency of access for the specific shard as a combination (e.g., a sum) of frequencies with which the online system accessed indices (e.g. aggregate or sum describing join)),
transmitting, to each shard of the plurality of shards, a portion of the temporary results; for each shard of the plurality of shards, receiving, from said each shard, a…result that is based on the portion of the temporary results that were transmitted to said each shard; wherein….an integer…..;generating a….. result based on the….results from the plurality of shards; generating a response to the vector query based on the….result; wherein the method is performed by one or more computing devices (see [0007], e.g., the online system distributes the indices into a number of shards (e.g., integer), the number of shards to allocate an index to a shard based on the value of the specific attribute for the index (e.g., integer), [0037], e.g., the machine-learned item availability model weight these factors differently, where the weights are a result of a “learning” or training process on the training datasets,. [0040], the training datasets periodically updated with recent previous delivery orders, also updated with item availability information provided directly from shoppers, [0061], e.g., computing devices capable of receiving user input as well as transmitting and/or receiving data via the network).
Shu teaches the claimed invention but does not explicitly teach the limitations of “a top K result, wherein K is an integer greater than zero; a final top K result; the top K results; the final top K result”. Although, Shu teaches taxonomy identifies a category and associates one or more specific items, matching and higher level of category, greater number of attribute satisfying the query ([0024]). However, in the same field of endeavor, Amer-Yahia teaches the limitations of “a top K result, wherein K is an integer greater than zero; a final top K result; the top K results; the final top K result” (see Amer-Yahia, [0062], e.g., computing top-k matches to XML queries, fig. 2(a), fig. 2(b), result of the queries, [0122], e.g., score exceeds a certain threshold).
Shu and Amer-Yahia both references teach features that are directed to analogous art and they are from the same field of endeavor, such as sharding, partitioning data, aggregating or joining the data, matching, score associated with data item results, ranking the data items, generate taxonomy, tree for the data items and store them in databases.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Amer-Yahia’s teaching to Shu’s system to prune irrelevant answers as early as possible in an evaluation process. Hence provide an adaptive query processing that permits different plans for different partial matches and maximizes the best scores is more appropriate. An adaptive query processing allows to reduce the number of server operations, and therefore leads to reduction in query processing time (see Amer-Yahia, [0061]).
As for claim 6, Shu teaches a method comprising: receiving a vector query that targets a first sharded table and a second sharded table in a vector database (see [0003], e.g., embedding for the query represents the query as a multidimensional vector in a latent space, For a received query, the online system identifies items having embeddings nearest to the embedding for the query in the latent space to retrieve items for the query, [0006], e.g., an index key and generates different indices that each correspond to a different value of the specific attribute (e.g., index defining tables in databases), [0008]);
in response to receiving the vector query, determining whether the vector query includes a non-collocated join condition on the first sharded table and the second sharded table; in response to determining that the vector query includes the non-collocated join condition: identifying a plurality of shards of the first sharded table (see [0003], e.g., query represents the query as a multidimensional vector in a latent space, [0006], e.g., an index key and generates different indices that each correspond to a different value of the specific attribute. Each index includes embeddings for items having a value of the specific attribute in the item database matching the value corresponding to the index (e.g., the index associated with databases which read the first or second table), [0008], e.g., receives a query from a user, the online system generates an embedding for the query, [0054], e.g., an aggregate frequency of access for the specific shard as a combination (e.g., a sum) of frequencies with which the online system accessed indices stored in the specific shard);
retrieving, from each of the plurality of shards, a…result pertaining to the first sharded table; sorting the….results from the plurality of shards to generate a sorted….result; retrieving, from each of the plurality of shards, data pertaining to the second sharded table; performing a join operation on the sorted….result and the data pertaining to the second sharded table; identifying a….result of the vector query after performing the join operation; wherein each of……is an integer……; wherein the method is performed by one or more computing devices (see [0043], any online system capable of retrieving items, [0006], embeddings included in different indices as well as the number of indices that are generated, [0007], e.g., the online system distributes the indices into a number of shards (e.g., integer), [0037], e.g., the machine-learned item availability model weight these factors differently, where the weights are a result of a “learning” or training process on the training datasets,. [0040], the training datasets periodically updated with recent previous delivery orders, also updated with item availability information provided directly from shoppers, [0061], e.g., computing devices capable of receiving user input as well as transmitting and/or receiving data via the network).
Shu teaches the claimed invention but does not explicitly teach the limitations of “a top N result; the top N results; the sorted top M result; a top K result”. Although, Shu teaches taxonomy identifies a category and associates one or more specific items, matching and higher level of category ([0024]). However, in the same field of endeavor, Amer-Yahia teaches the limitations of “a top N result; the top N results; the sorted top M result; a top K result, wherein each of K, N, M is an integer greater than zero” (see Amer-Yahia, [0062], e.g., computing top-k matches to XML queries, fig. 2(a), fig. 2(b), result of the queries, [0122], e.g., score exceeds a certain threshold).
Shu and Amer-Yahia both references teach features that are directed to analogous art and they are from the same field of endeavor, such as sharding, partitioning data, aggregating or joining the data, matching, score associated with data item results, ranking the data items, generate taxonomy, tree for the data items and store them in databases.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Amer-Yahia’s teaching to Shu’s system to prune irrelevant answers as early as possible in an evaluation process. Hence provide an adaptive query processing that permits different plans for different partial matches and maximizes the best scores is more appropriate. An adaptive query processing allows to reduce the number of server operations, and therefore leads to reduction in query processing time (see Amer-Yahia, [0061]).
As for claim 10,
The limitations therein have substantially the same scope as claim 1 because claim 10 is a non-transitory storage media claim for implementing those steps of claim 1. Therefore, claim 10 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Amer-Yahia’s teaching to Shu’s system to prune irrelevant answers as early as possible in an evaluation process. Hence provide an adaptive query processing that permits different plans for different partial matches and maximizes the best scores is more appropriate. An adaptive query processing allows to reduce the number of server operations, and therefore leads to reduction in query processing time (see Amer-Yahia, [0061]).
As for claim 15,
The limitations therein have substantially the same scope as claim 1 because claim 15 is a storage media claim for implementing those steps of claim 1. Therefore, claim 15 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Amer-Yahia’s teaching to Shu’s system to prune irrelevant answers as early as possible in an evaluation process. Hence provide an adaptive query processing that permits different plans for different partial matches and maximizes the best scores is more appropriate. An adaptive query processing allows to reduce the number of server operations, and therefore leads to reduction in query processing time (see Amer-Yahia, [0061]).
As to claim 2, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
wherein the method further comprising, prior to receiving the top K result: for each shard of the plurality of shards, performing, by said each shard, a second join operation that joins (i) third data from the first sharded table with (ii) fourth data from the portion of the temporary results; wherein the top K result is based on results from the second join operation (see Shu, [0007], [0024], [0054]; Also see, Amer-Yahia, [0062]).
As to claim 3, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
wherein the vector query includes a query vector, the method further comprising: for each shard of the plurality of shards: for each result of the results from the second join operation: generating a vector distance between the query vector and a vector associated said each result; adding the vector distance to a set of vector distances; identifying, by said each shard, the top K vector distances from the set of vector distances; transmitting, by said each shard, the top K vector distances to a query coordinator (see Shu, [0003], [0054], [0061]; Also see, Amer-Yahia, [0062]).
As to claim 4, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
further comprising, prior to transmitting the portion: receiving, from each shard of the plurality of shards, a request for at least the portion of the temporary results (see Shu, [0003], [0054], [0061], [0070]).
As to claim 5, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
wherein generating the final top K result comprises: aggregating the top K results from the plurality of shards to generate an aggregated result; sorting the aggregated result to generate a sorted aggregated result; identifying the final top K from the sorted aggregated result (see Shu, [0006], [0054]; Also see, Amer-Yahia, [0062]).
As to claim 7, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
further comprising: determining whether the number of items in the result of the join operation is greater than or equal to K; in response to determining that the number of items is not greater than or equal to K: identifying an additional set of results from the top N results from the plurality of shards; performing a second join operation on the additional set of results and the data pertaining to the second sharded table; wherein identifying the top K result is also based on results of the second join operation (see Shu, [0024], [0054], claim 3; Also see, Amer-Yahia, [0062]).
As to claim 8, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
further comprising: determining whether the number of items in the result of the join operation is greater than or equal to K; in response to determining that the number of items is not greater than or equal to K: increasing N to P; retrieving, from each of the plurality of shards, a top P result pertaining to the first sharded table; sorting the top P results from the plurality of shards to generate a sorted top Q result; identifying the data pertaining to the second sharded table; performing a second join operation on the sorted top Q result and the data pertaining to the second sharded table; wherein identifying a top K result is based on results of the second join operation (see Shu, [0024], [0054], claim 3; Also see, Amer-Yahia, [0062]).
As to claim 9, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Shu and Amer-Yahia teaches:
wherein the vector query includes a query vector, the method further comprising: for each shard of the plurality of shards: performing a search, by said each shard, of a vector index based on the query vector and data from the first sharded table; wherein performing the search results in identifying the top N result; sending, by said each shard, the top N result to a query coordinator (see Shu, [0007], [0048], [0054]; Also see, Amer-Yahia, [0062]).
Claims 11-14 correspond in scope to claims 2-5 and are similarly rejected.
Claims 16-18 correspond in scope to claims 7-9 and are similarly rejected.
Prior Arts
8. US 2020/0242157 A1 teaches partitions or sharding is a data tier architecture where data is horizontally partitioned across independent database instances, where each independent database instance is referred to as a “shard.” A collection of shards, together, makes up a single logical database which is referred to as a “sharded database” (“SDB”). Tables in a sharded database are horizontally partitioned across shards ([0010]).
US 2012/0310916 A1 teaches query involved joining six tables: customer, orders, supplier, nation, and region. The fully optimized version used referential partitioning, split semijoin, and post-join aggregation. It involve one repartitioned join (the join with the supplier table). Query included two MapReduce jobs, the first one performed the join and partial aggregation and the second job computed the global sum of revenue per nation ([0160]).
WO2017/062288 teaches shards or collection of shards make a single logical shaded database. Tables in a sharded database are horizontally partitioned across shards ([0026]).
Also see, US 11204900, US 20230306028, US 20190258613, US 20170103094, US 10496614, US 20170103116, US 20170103092, US 20190220450, US 10268710, US 10983970, US 20170103098, US 20210073208, US 20210406252, US 10025822, US 1116995, US 9171044, US 10747814, US 11397768, US 20200242157, US 20220374424, US 20160203061, these reference also read the claim recited limitation. These references are state of the art at the time of the claimed invention.
Conclusion
9. The examiner suggests, in response to this Office action, support being shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application (see 37 C.F.R. § 1.75(d)(1), 37 C.F.R. § 1.83(f)).
10. The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action (see MPEP § 7.96). Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
11. 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 final action.
12. Any inquiry concerning this communication or earlier communication from the examiner should be directed to Daniel A Kuddus whose telephone number is (571) 270-1722. The examiner can normally be reached on Monday to Thursday 8.00 a.m.-5.30 p.m. The examiner can also be reached on alternate Fridays from 8.00 a.m. to 4.30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Boris Gorney can be reached on (571) 270-5626. The fax phone number for the organization where this application or processing is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from the either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only.
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/DANIEL A KUDDUS/ Primary Examiner, Art Unit 2154
06/10/26
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