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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 15 2026 has been entered.
Claims 1-20 are pending. Claims 1, 11 and 20 are amended.
Response to Arguments
Referring to the 35 USC 112(b) rejection of claims 1-20, Applicant’s amendments are acknowledged. As such the 35 USC 112(b) rejection of the claims is withdrawn.
Applicant’s arguments with respect to claims 1-8, 11-18 and 20 have been considered but are not persuasive.
Applicant argues that Liu/Yishay/Lu/Mukhopadhyay does not teach grouping the plurality of database tables into clusters. However Examiner respectfully disagrees. Lu discloses that vectors are used to represent tables within a vector embedding space and that a comparison of two vectors for tables A and B show a similarity or closeness of association of two tables which is referred to as the distance between the two tables [para 75, Fig 7; see distances between tables, Fig 6]. Lu furthermore discloses that tables within a predetermined distance of a subject table (X) are considered sufficiently similar to the subject table [para 77, Fig 8, 12]. Examiner submits that the tables within the predetermined distance to the subject table, as shown in Fig 8 and 12, show that the tables within the predetermined distance are clustered because they lie within the predetermined distance boundary in the space. As such, Examiner maintains that Liu/Yishay/Lu/Mukhopadhyay does teach grouping the plurality of database tables into clusters.
Applicant argues that Liu/Yishay/Lu/Mukhopadhyay, specifically Mukhopadhyay does not teach ‘moving positions of the respective vectors representing the plurality of database tables in the multidimensional vector space by modifying coordinate values of respective vectors’. However, Examiner respectfully disagrees. Yishay discloses that document vectors are moved between specific scales or regions of the multidimensional space based on a hash function applied to each vector in the space [Yishay, para 55; Fig 1; Fig 3B, element 70, Fig 3C, element 86 and specification], however did not specify that the multidimensional space contained coordinates for its vector regions. Mukhopadhyay is relied upon to teach this feature in that the feature vectors within its two-dimensional space are represented by (x,y) coordinates [para 57].
The combined teachings of Yishay and Mukhopadhyay would show that the moving of the document vectors across regions or scales of the multi-dimensional space of Yishay could be achieved in the (x,y) coordinate space of Mukhopadhyay because the 2-dimensional space of Yishay is a variation of the multi-dimensional space of Yishay. As such, Examiner maintains that Liu/Yishay/Lu/Mukhopadhyay does teach moving positions of the respective vectors representing the plurality of database tables in the multidimensional vector space by modifying coordinate values of respective vectors’, as claimed.
All other claims are also rejected for at least the reasons stated above and further as addressed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8, 11-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Publication ‘TableSeer: Automatic Table Metadata Extraction and Searching in Digital Libraries’ by Liu et al (hereafter Liu), as disclosed by Applicant in IDS dated 1/17/2025, in view of US 2020/0142916 by Yishay et al (hereafter Yishay), as disclosed by Applicant in IDS dated 1/17/2025, in view of US 2021/0209083 by Lu et al (hereafter Lu) and further in view of US 2020/0073878 by Mukhopadhyay et al (hereafter Mukhopadhyay).
Referring to claim 1, Liu discloses a computer-implemented method [TableRank algorithm, page 91, Abstract; page] 5, section 3.5 comprising:
representing a plurality of database tables as respective vectors in a vector space [wherein TableRank replaces document vectors with table vectors in vector space, page 95, section 3.5, para 2; table and query can be represented as a vector, page 96, section 4.1, para 3]; and
determining that the plurality of database tables are related based on positions of the respective vectors representing the plurality of database tables in the vector space [vector matrix of (query,table) vectors are constructed and similarity between query and each table is computed by using the cosine of the angle between these two vectors, page 96, section 4.1, para 4, see Table 2, page 97].
Referring to claim 1, while Liu discloses all of the above claimed subject matter and also discloses determining a similarity between a table and a query within a vector space [Liu, similarity is measured with respect to query q and each table represented as a vector- see vector matrix, Table 2, page 96, section 4.1, para 3-4]], it remains silent as to the vector space being a multidimensional vector space; receiving an indication that a first database table and a second database table in the multidimensional vector space are related to each other, wherein the first database table is represented by a first vector and the second database table is represented by a second vector; and moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space by modifying coordinate values of the respective vectors; and grouping the plurality of database tables into one or more table clusters.
However Yishay discloses that information items are represented by different respective document vectors in a multi-dimensional vector space [Abstract; para 3, 54]; moving the respective vectors representing the plurality of documents in the multi-dimensional vector space [wherein a hash function is applied to each vector corresponding to a specific scale and the resulting hash value represents the region 44 to which the vector is mapped, para 55; Fig 1; vectors mapped to a selected hash value are moved to next smallest or next largest scale, Fig 3B, element 70, Fig 3C, element 86 and specification].
Liu and Yishay are analogous art because they are directed to querying for content within data embedded in vector spaces. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the vector space of Liu with the multidimensional vector space of Yishay, and to modify the vectors within the vector space related to a given query of Liu by the moving of those vectors based on computed hash values in Yishay because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make the aforementioned modifications because Liu is directed to querying for data using table vectors based on a determined similarity between the vectors and a given query in a vector space. Yishay further refines the Liu’s querying for table data through the specific use of hashing functions and further refines the similarity of Liu to include a similarity based on Yishay’s calculated hash function scales.
Still referring to claim 1, while Liu/Yishay discloses all of the above claimed subject matter, and also discloses determining a similarity between a table and a query within a vector space [Liu, similarity is measured with respect to query q and each table represented as a vector- see vector matrix, Table 2, page 96, section 4.1, para 3-4]; and moving the vectors between specific scales or regions of the multidimensional space based on a hash function applied to each vector in the space [Yishay, para 55; Fig 1; Fig 3B, element 70, Fig 3C, element 86 and specification], it remains silent as to: the moving of the vectors across coordinates in the multi-dimensional space; and receiving an indication that a first database table and a second database table are related to each other, wherein the first database table is represented by a first vector and the second database table is represented by a second vector; and grouping the plurality of database tables into one or more table clusters.
Mukhopadhyay discloses feature vectors that represent different rows of implicit tables are represented by two coordinates (x,y) in a two-dimensional space and that a similarity measure of two feature vectors representing two different rows is determined using a Euclidean distance function between the two feature vectors [para 57].
Liu, Yishay and Mukhopadhyay are analogous art because they are directed to the same field of endeavor- locating content within vector spaces. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the multidimensional vector space of Yishay with the 2-dimensional (x,y) space of Mukhopadhyay because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the 2-dimensional space of Yishay is a variation of the multi-dimensional space of Yishay. As such, the moving of the vectors across regions or clusters in the multi-dimensional space of Yishay could be accomplished through moving positions via the (x,y) coordinates of the 2 dimensional space of Mukhopadhyay.
Still referring to claim 1, while Liu/Yishay/Mukhopadhyay discloses all of the above claimed subject matter, and also discloses determining a similarity between a table and a query within a vector space [Liu, similarity is measured with respect to query q and each table represented as a vector- see vector matrix, Table 2, page 96, section 4.1, para 3-4], it remains silent as to: an indication that a first database table and a second database table are related to each other, wherein the first database table is represented by a first vector and the second database table is represented by a second vector and grouping the plurality of database tables into one or more table clusters.
Lu discloses comparing two different vectors that each represent different tables A and B that show a similarity or closeness of association of the two tables based on a distance constraint [para 74,75,76,82; Fig 7,8,12 and corresponding portions of specification]. Lu also discloses that vectors are used to represent tables within a vector embedding space and that a comparison of two vectors for tables A and B show a similarity or closeness of association of two tables which is referred to as the distance between the two tables [para 75, Fig 7; see distances between tables, Fig 6]. Lu furthermore discloses that tables within a predetermined distance of a subject table (X) are considered sufficiently similar to the subject table [para 77, Fig 8, 12]. Examiner submits that the tables within the predetermined distance to the subject table, as shown in Fig 8 and 12, show that the tables within the predetermined distance are clustered because they lie within the predetermined distance boundary in the space.
Liu, Yishay, Mukhopadhyay and Lu are analogous art because they are directed to the same field of endeavor- representing content within vector spaces. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the TableRank algorithm of Liu to include determining that tables representing vectors are related and grouping them into clusters because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because both Liu and Lu deal with the use of vectors to determine a similarity that involves database tables. Lu’s disclosure of the similarity between two tables used to group the tables would provide Liu with an extra dimension within its table ranking.
Referring to claim 11, the limitations of the claim are similar to those of claim 1, in the form of a computing system [Yishay, system 20, para 48, Fig 1] comprising: memory [Yishay, RAM, para 52, Fig 1]; one or more hardware processors coupled to the memory [Yishay, processor 24, para 48, Fig 1]; and one or more computer readable storage media storing instructions [Yishay, storage drive 25, para 48, 52, Fig 1]. As such, claim 11 is rejected of the same reasons as claim 1.
Referring to claim 20, the limitations of the claim are similar to those of claim 1, in the form of computer readable instructions [Yishay, storage drive 25 storing instructions, para 48, 52, Fig 1]. As such, claim 20 is rejected of the same reasons as claim 1.
Referring to claims 2 and 12, Liu/Yishay/Mukhopadhyay/Lu discloses placing database tables contained in a table cluster in a common host machine [Liu, tables, Abstract; Yishay, server, Fig 1, element 22; Lu, tables grouped based on distance, Fig 8, 12].
Referring to claims 3 and 13 , Liu/Yishay/Mukhopadhyay/Lu discloses responsive to a query command involving a database table contained in a table cluster, recommending a different database table from the table cluster [Liu, advanced table search, page 96, para 1, Fig 5 and 6; Yishay, vectors within a region are returned in response to a query, para 92-93, Fig 4, element 106, Fig 5, element 44e; Lu, tables grouped based on distance, Fig 8, 12].
Referring to claims 4 and 14, Liu/Yishay/Mukhopadhyay/Lu discloses that moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space comprises moving the second vector closer to the first vector [Liu, tables, Abstract; Yishay, moving vectors mapped to selected hash value to next smaller scale, Fig 3B, element 70 and specification ].
Referring to claim 5, Liu/Yishay/Mukhopadhyay/Lu discloses that moving the second vector closer to the first vector comprises reducing a distance between the first vector and the second vector by a decrement size, wherein the decrement size progressively decreases when the distance between the first vector and the second vector decreases [Yishay, next smallest scale is progressively selected, Fig 3B, elements 70-74 and specification].
Referring to claims 6 and 16, Liu/Yishay/Mukhopadhyay/Lu discloses that moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space comprises moving a third vector representing a third database table other than the first and second database tables away from the first vector [Liu, tables, Abstract; Yishay, moving vectors mapped to selected hash value to next largest scale, Fig 3C, element 86 and specification].
Referring to claim 7, Liu/Yishay/Mukhopadhyay/Lu discloses that moving the third vector away from the first vector comprises increasing a distance between the first vector and the third vector by an increment size, wherein the increment size progressively decreases when the distance between the first vector and the third vector increases [Yishay, next largest scale is progressively selected, Fig 3C, elements 86-90 and specification].
Referring to claims 8 and 18, Liu/Yishay/Mukhopadhyay/Lu discloses that grouping the plurality of database tables comprises measuring distances or angles between respective vectors representing the plurality of database tables in the multi-dimensional vector space [Liu, cosine of angle between query and each table vectors, page 96, section 4.1 para 4; Lu, tables grouped based on distance, Fig 8, 12].
Referring to claim 15, Liu/Yishay/Mukhopadhyay/Lu discloses that moving the second vector closer to the first vector comprises reducing a distance between the first vector and the second vector by a decrement size, wherein the decrement size is adaptive to the distance between the first vector and the second vector [Yishay, next smallest scale is progressively selected, Fig 3B, elements 70-74 and specification].
Referring to claim 17, Liu/Yishay/Mukhopadhyay/Lu discloses that moving the third vector away from the first vector comprises increasing a distance between the first vector and the third vector by an increment size, wherein the increment size is adaptive to the distance between the first vector and the third vector [Yishay, next largest scale is progressively selected, Fig 3C, elements 86-90 and specification].
Allowable Subject Matter
Claims 9, 10 and 19 were indicated as objected to as being dependent upon rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims and to overcome the 35 USC 112(b) rejections addressed above.
The reasons for the indication of allowable subject matter were provided in the Non-final Office action dated 10/2/2025.
Conclusion
The prior art or art made of record and not relied upon is considered pertinent to applicant's disclosure:
Natesan et al (US 2022/0343191): directed to table column clustering using machine learning models, wherein table column vectors are mapped to a multidimensional clustering space and determining that table column clusters are related based on a distance measure threshold [para 136, Fig 8 and corresponding portions of specification].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERYL M SHECHTMAN whose telephone number is (571)272-4018. The examiner can normally be reached on M-F: 10am-6:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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