DETAILED ACTION
1. This is a Final Office Action Correspondence in response to arguments/amendments for U.S. Application No. 17/559636 filed on July 09, 2026.
Notice of Pre-AIA or AIA Status
3. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant
4. Applicant is encouraged to the contact the Examiner in hopes of reaching allowable subject matter in light of compact prosecution.
Response to Arguments
5. Applicant’s arguments have been considered but are not fully persuasive.
On Pg. 10-12 of remarks in regards to the 35 U.S.C. 103 rejection, Applicant argues the amendments claims.
Examiner replies that a new reference was introduced to teach the amended limitations.
Claim Rejections - 35 USC § 103
9. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
10. 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.
11. Claim(s) 1, 2, 6, 8, 14, 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al. U.S. Patent Application Publication No. 2021/0241343 (herein as ‘Arora’) in combination with Xu et al. U.S. Patent Application Publication No. 2021/0201090 (herein as ‘Xu’) as further in view of Shilane et al. U.S. Patent No. 8,935,446 (herein as ‘Shilane’).
As to claim 1 Arora teaches an electronic device, comprising: a plurality of nodes, each node configured to:
Arora teaches: receive an input index vector (Par. 0069 Xu discloses receiving a vector index containing image data);
comprising a plurality of indices including first and second instances of a same index value (Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings);
Arora does not teach but Xu teaches wherein the index vector is for retrieving embedding table data associated with a machine learning model (Par. 0037-0039 Xu discloses using a first machine learning model to output labels. The output labels are seen as the embedding table data);
Arora and Xu are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Xu, to allow for improved accuracy. The suggestion/motivation to combine is that it would be obvious to try in order to speed up classification during video reviewing (Par. 0002-0003 Xu).
Arora teaches generate a compressed index vector that includes the same index value once and excludes the second instance of the same index value (Par. 0079 Arora discloses creating a set of merged lists of item recommendations. When the same items are encountered, only the item with the high score is included in the merged list, and therefore the lower score item is removed from the merged part. Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector);
Arora does not teach but Shilane teaches generate location data that identifies a location of the second instance in the index vector (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index);
Arora and Shilane are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Shilane, to allow reduce the search time. The suggestion/motivation to combine is that it would be obvious to try in order to reduce the time and resources needed to search large number of records (Col. 1 Lines 20-38 Shilane).
Arora teaches a corresponding location of the same index value in the compressed index vector (Par. 0092 Arora discloses merging duplicate items into a single item list, therefore the duplicate will not be included);
Generate compressed lookup data by retrieving embedding table data using the compressed index vector (Par. 0105 Arora discloses creating the index based upon the concatenation of the query vector which includes item embeddings).
As to claim 2 Arora in combination with Shilane and Xu teaches each and every limitation of claim 1.
Arora teaches wherein each node is further configured to: communicate the location data and the compressed lookup data to one or more other computing nodes of the plurality of computing nodes (Par. 105 Arora discloses performing a batch lookup for each item in the basket sets of the top 30 nearest neighbors. Performing the search is seen as using the lookup of the item beddings to discover if they exist within the storage of each neighbor. Using the item embeddings as a search to the top 30 nearest neighbors is seen as communicating the compressed lookup data for a respective part to each other node).
As to claim 6 Arora in combination with Shilane and Xu teaches each and every limitation of claim 1.
Shilane teaches wherein the embedding table data of the machine learning model is distributed across local memories of the plurality of computing nodes such that each computing node stores only a portion of the embedding table data, and wherein the compressed lookup data comprises embedding table data retrieved from a portion stored by a respective computing node of the plurality of computing nodes by associating (Par. 0105 and Par. 0108 Arora discloses creating the index based upon the concatenation of the query vector which includes item embeddings that are stored in memory);
As to claim 8 Arora teaches a comprising:
receiving, by processing circuitry of a given computing node of a plurality of computing nodes, an index vector comprising a plurality of indices including first and second instances of a same index value (Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings);
Arora does not teach but Xu teaches wherein the index vector is for retrieving embedding table data associated with a machine learning model (Par. 0037-0039 Xu discloses using a first machine learning model to output labels. The output labels are seen as the embedding table data);
Arora and Xu are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Xu, to allow for improved accuracy. The suggestion/motivation to combine is that it would be obvious to try in order to speed up classification during video reviewing (Par. 0002-0003 Xu).
Arora teaches generating, by the processing circuitry, a compressed index vector that includes the same index once and excludes the second instance of the same index (Par. 0079 Arora discloses creating a set of merged lists of item recommendations. When the same items are encountered, only the item with the high score is included in the merged list, and therefore the lower score item is removed from the merged part. Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector);
Arora does not teach but Shilane teaches generating, by the processing circuitry, location data that identifies: a location of the second instance in the index vector (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index);
Arora and Shilane are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Shilane, to allow reduce the search time. The suggestion/motivation to combine is that it would be obvious to try in order to reduce the time and resources needed to search large number of records (Col. 1 Lines 20-38 Shilane).
Arora teaches and a corresponding location of the same index value in the compressed index vector (Par. 0092 Arora discloses merges duplicate items into a single item list, therefore the duplicate will not be included);
generating, by the processing circuitry, compressed lookup data by retrieving embedding table data using the compressed index vector (Par. 0105 Arora discloses creating the index based upon the concatenation of the query vector which includes item embeddings);
As to claim 14 Arora in combination with Shilane and Xu teaches each and every limitation of claim 8.
Arora teaches wherein the embedding table data of the machine learning model is distributed across local memories of the plurality of computing nodes such that each computing node stores only a portion of the embedding table data, and wherein the compressed lookup data comprises embedding table data retrieved from a portion stored by a respective computing node of the plurality of computing nodes (Par. 0105 and Par. 0108 Arora discloses creating the index based upon the concatenation of the query vector which includes item embeddings that are stored in memory);
As to claim 15 Arora teaches a computing node, comprising:
at least one local memory storing a portion of embedding table data of a machine learning model; and at least one processor comprising circuitry configured to:
receive an index vector comprising a plurality of indices including first and second instances of a same index value (Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings);
Arora does not teach but Xu teaches wherein the index vector is for retrieving embedding table data associated with a machine learning model (Par. 0037-0039 Xu discloses using a first machine learning model to output labels. The output labels are seen as the embedding table data);
Arora and Xu are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Xu, to allow for improved accuracy. The suggestion/motivation to combine is that it would be obvious to try in order to speed up classification during video reviewing (Par. 0002-0003 Xu).
Arora teaches generate a compressed index vector that includes the same index value once and excludes the second instance of the same index value (Par. 0079 Arora discloses creating a set of merged lists of item recommendations. When the same items are encountered, only the item with the high score is included in the merged list, and therefore the lower score item is removed from the merged part. Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector);
Arora does not teach but Shilane teaches generate location data that identifies: a location of the second instance in the index vector (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index);
Arora and Shilane are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Shilane, to allow reduce the search time. The suggestion/motivation to combine is that it would be obvious to try in order to reduce the time and resources needed to search large number of records (Col. 1 Lines 20-38 Shilane).
Arora teaches and a corresponding location of the same index value in the compressed index vector (Par. 0092 Arora discloses merging duplicate items into a single item list, therefore the duplicate will not be included);
generate compressed lookup data by retrieving embedding table data using the compressed index vector (Par. 0105 Arora discloses creating the index based upon the concatenation of the query vector which includes item embeddings);
As to claim 19 Arora in combination with Shilane and Xu teaches each and every limitation of claim 15.
Shilane teaches wherein the embedding table data of the machine learning model is distributed across local memories of a plurality of computing nodes such that each computing node stores only a portion of the embedding table data, and wherein the compressed lookup data comprises embedding table data retrieved from a portion stored by a respective computing node of the plurality of computing nodes (Par. 0105 and Par. 0108 Arora discloses creating the index based upon the concatenation of the query vector which includes item embeddings that are stored in memory).
Claim(s) 3-7, 9-13, 16-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al. U.S. Patent Application Publication No. 2021/0241343 (herein as ‘Arora’) in combination with Xu et al. U.S. Patent Application Publication No. 2021/0201090 (herein as ‘Xu’), Shilane et al. U.S. Patent No. 8,935,446 (herein as ‘Shilane’) and further in view of Khatibi et al. U.S. Patent Application Publication No. 2022/0222543 (herein as ‘Khatibi’) and Tomei et al. U.S. Patent Application Publication No. 2021/0157485 (herein as ‘Tomei’).
As to claim 3 Arora in combination with Shilane and Xu teaches each and every limitation of claim 2.
Arora does not teach but Tomei teaches wherein the location data is usable by the one or more other computing nodes to reconstruct uncompressed lookup data by copying lookup data retrieved for the same index value to a location corresponding to the second instance in the index vector (Par. 0067 Tomei discloses an index to a set of block patterns that can be used to reconstruct the uncompressed cache block).
Arora and Tomei are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the reconstruction of uncompressed data blocks of Tomei, to improve performance. The suggestion/motivation to combine is that it would be obvious to try to improve performance of the compression results (Par. 0100 Tomei).
As to claim 10 Arora in combination with Shilane and Xu teaches each and every limitation of claim 9.
In addition Khatibi teaches wherein the location data is usable by the one or more other computing nodes to reconstruct uncompressed lookup data by copying lookup data retrieved for the same index value to a location corresponding to the second instance in the index vector (Par. 0067 Tomei discloses an index to a set of block patterns that can be used to reconstruct the uncompressed cache block).
Arora and Tomei are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the reconstruction of uncompressed data blocks of Tomei, to improve performance. The suggestion/motivation to combine is that it would be obvious to try to improve performance of the compression results (Par. 0100 Tomei).
9. Claim(s) 3-7, 9-13, 16-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al. U.S. Patent Application Publication No. 2021/0241343 (herein as ‘Arora’) in combination with Xu et al. U.S. Patent Application Publication No. 2021/0201090 (herein as ‘Xu’), Shilane et al. U.S. Patent No. 8,935,446 (herein as ‘Shilane’) and further in view of Khatibi et al. U.S. Patent Application Publication No. 2022/0222543 (herein as ‘Khatibi’).
As to claim 4 Arora in combination with Shilane and Xu teaches each and every limitation of claim 1.
Arora does not teach but Shilane teaches produce a record of locations that identifies: locations, in the index vector, of instances excluded from the compressed index vector (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index);
Arora teaches corresponding locations, in the compressed index vector, of index values retained in the compressed index vector; and a number of indices in the compressed index vector; and generate the location data based at least in part on the record of locations (Par. 0079 Arora discloses creating a set of merged lists of item recommendations. When the same items are encountered, only the item with the high score is included in the merged list, and therefore the lower score item is removed from the merged part. Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector).
As to claim 5 Arora in combination with Shilane and Xu teaches each and every limitation of claim 1.
Arora does not teach but Shilane teaches wherein the processing circuitry is configured to generate a respective compressed index vector and respective location data for each of multiple parts of a respective set of input index vectors, each part corresponding to lookup data to be used by a corresponding computing node of the plurality of computing nodes (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index).
As to claim 7 Arora in combination with Shilane and Xu teaches each and every limitation of claim 1.
In addition Arora teaches wherein generating the compressed lookup data comprises retrieving embedding table data corresponding to the same index value once, without separately retrieving embedding table data for the second instance (Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector).
As to claim 9 Arora in combination with Shilane and Xu teaches each and every limitation of claim 8.
Arora teaches communicating the location data and the compressed lookup data to one or more other computing nodes of the plurality of computing nodes (Par. 0079 Arora discloses creating a set of merged lists of item recommendations. When the same items is encountered, only the item with the high score is included in the merged list, and therefore the lower score item is removed from the merged part).
As to claim 11 Arora in combination with Shilane and Xu teaches each and every limitation of claim 9.
In addition Khatibi teaches producing a record of locations that identifies: locations, in the index vector, of instances excluded from the compressed index vector, (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index);
corresponding locations in the compressed index vector, of index values retained in the compressed index vector; and a number of indices in the compressed index vector; and generating the location data based at least in part on the record of locations (Par. 0079 Arora discloses creating a set of merged lists of item recommendations. When the same items are encountered, only the item with the high score is included in the merged list, and therefore the lower score item is removed from the merged part. Par. 0088 Arora discloses the item embeddings are vectors. Par. 0092 Arora discloses merging duplicate items into a single item list; therefore, the duplicate will be excluded. Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector).
As to claim 12 Arora in combination with Shilane and Xu teaches each and every limitation of claim 8.
In addition Khatibi teaches further comprising acquiring by the processing circuitry, lookup data from at least one embedding table stored in a local memory using the compressed input index vectors, the lookup data comprising data from rows of the at least one embedding table, and the compressed input index vector comprising indices identifying rows of the at least one embedding table (Pp.77 Khatibi discloses using the information to locate duplicate the data sets).
As to claim 13 Arora in combination with Shilane and Xu teaches each and every limitation of claim 8.
In addition Khatibi teaches generating a respective compressed index vector and respective location data for each of multiple parts of a respective set of input index vectors, each part corresponding to lookup data to be used by a corresponding computing node of the plurality of computing nodes (Col. 9 Lines 26-36 Shilane discloses duplicate entries that point to the particular location of the base entry. The duplicate entry is seen as the duplicate index).
As to claim 16 Arora in combination with Shilane and Xu teaches each and every limitation of claim 15.
In addition Khatibi discloses wherein the circuitry is further configured to generate the compressed index vector by removing duplicate indices prior to retrieving embedding table data, such that each unique index in the compressed index vector is used for at most one embedding table lookup (Par. 0059 Khatibi discloses removing duplicate copies).
As to claim 17 Arora in combination with Shilane and Xu teaches each and every limitation of claim 15.
In addition Khatibi teaches generate compressed lookup data based at least in part on the compressed input index vector; and communicate, to the one or more other computing nodes of the plurality of computing nodes, the compressed lookup data after communicating the location data (Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora disclose concatenating the item embeddings. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The fast look up is seen as decompressed lookup data. The index of query vectors is seen as the input index vector).
Arora and Khatibi are analogous art because they are in the same field of endeavor, data processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the input vector index of Arora to include the location and removal of duplicate records of Khatibi, to allow reduce the search time. The suggestion/motivation to combine is that it would be obvious to try in order to reduce the time and resources needed to search large number of records (Par. 0003 Khatibi).
As to claim 18 Arora in combination with Shilane and Xu teaches each and every limitation of claim 17.
In addition wherein the at least one processor is further configured to:produce a record of locations that identifies: locations, in the index vector of instances excluded from the compressed input index vector (Pp.77 Khatibi discloses using the information to locate duplicate the data sets);
Corresponding locations, in the compressed index vector, of index values retained in the compressed index vector; and a number of input indices in the compressed input index vector; and generate the location data based at least in part on the record of locations (Par. 0093 Khatibi discloses identifying a number of duplicate text. Par. 0077 Khatibi discloses merging duplicate records. Merging duplicate records is seen as removing an input);
As to claim 20 Arora in combination with Shilane and Xu teaches each and every limitation of claim 15.
Khatibi teaches wherein generating the compressed lookup data comprises retrieving embedding table data corresponding to the same index value once, without separately retrieving embedding table data for the second instance (Par. 105 Arora discloses a model using items in a basket set and creating a query vector using pre-trained user data and items embeddings. The multiple parts are seen as the item embeddings. Arora discloses concatenating the item embeddings. Concatenating the item embeddings is seen as compressing lookup data. Arora discloses creating and index using the items. Arora discloses the items embeddings can be stored for fast lookup. The item embeddings based upon the item selected by the user is seen as the input index vector).
Conclusion
12. 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.
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/J.A.M/ September 18, 2026Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
