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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 4 recites a “computer-readable medium” storing a data that perform various functions. In the Specification of the present application, the “computer-readable medium” is not excluding transmission media (see paragraph [0006]). Further the claim recites a “computer readable medium”, and the specification fails to provide a definition for that term. It also does not provide any indication that such storage medium is non-transitory. Thus, the broadest, reasonable interpretation of “computer-readable medium” encompasses non-statutory subject matter (transmission media) that is unpatentable under 35 U.S.C. 101.
Accordingly, Claim 4 fails to recite statutory subject matter under 35 U.S.C. 101.
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 (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.
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.
Claim(s) 1-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Otto et al. (US Patent No. 12,210,508) in view of Inoue (US Patent Application Publication No. 2022/0245325).
Regarding claim 1, Otto discloses a data comparison method executed by an information processing device, the method comprising [see abstract; matches table is generated which includes the key field(s), comparison field(s), and a source field whose value indicates the originating table of the data in the row]:
acquiring character strings in respective cells that are included in a first table in
document data [see col. 2, lines 15-18; comparing the first row of the first table with the first row of the second table, comparing the second row of the first table with the second row of the second table, and so on for all the rows in the tables. Although such a process will identify differences between the two tables from a database perspective, these are not the differences the user is typically seeking. For example, if the second table is missing one row at the beginning but is otherwise identical to the first table, the result of this approach would be an indication that all rows of the two tables are different; which corresponds to extract text arrays from cell in a first table within document];
acquiring character strings in respective cells that are included in a second table in
the document data, the second table being different from the first table [see col. 2, lines 62-67 and col.3, lines 1-8 and figure 1; First table 102 and second table 104 can each include at least one row. Each row can include respective values for each of a plurality of fields. The plurality of fields can include at least one field designated as a “key” field and at least one field designated as a “comparison” field. Optionally, the plurality of fields can include at least one field designated as an “other” field. For example, first table includes rows, each of which includes values for a date field, an engine field, and a quantity field. The date field and the engine field are designated as key fields in the example, whereas the quantity field is designated as a comparison field; which corresponds to the second table being different from the first table];
determining whether the second table corresponds to the first table based on
similarities between the character strings in the cells that are included in the first table and
the character strings in the cells that are included in the second table [see col. 5, lines 34-46 and figure 2; a request to compare first and second tables is received. The request can be initiated by a user via a user interface of the database system, or by a processor or another component of the database system. The request can also include one or more of the following. an indication of which field(s) common to the first and second table are to be designated as the key field(s); an indication of which field(s) common to the first and second tables are to be compared (e.g., which fields to designate as the comparison fields); and an indication of which field(s) common to the first and second tables to designate as other fields (e.g., fields whose values will not be compared and will appear in the results table); which corresponds to the character strings in the cells that are included in the second table]; and when determining that the second table corresponds to the first table, that are included in the first table and the character strings in the cells that are included in the second table and correspond to the cells included in the first table [see col. 8; lines 40-58 and figure 4; the second table can be stored in memory of a database system, and a processor can fetch any rows of the second database table whose key combination matches the key combination in the matches table and insert those rows into the matches table. Adding the row(s) from the second table to the matches table can include populating the source field of the matches table with the value “second table” to indicate that the row originated from the second table. The rows in the matches table from the first and second tables are compared, and the results table is populated with the results of the comparison. The matches table is then cleared at, and it is determined at whether there are more rows in the first table (e.g., whether the last row fetched from the first table was the last row in the first table)]; however, Otto fails to explicitly teach identifying a difference between the character strings in the cells.
Inoue discloses identifying a difference between the character strings in the cells [see para. 0005, 0103 and figures 5-7; acquiring a plurality of design documents about a system; identifying a plurality of label items representing elements of the system based on appearance frequency information of character strings included in the plurality of design documents acquired; and generating structure information in which the plurality of label items are hierarchized based on appearance positions at which the character string corresponding to each of the plurality of label items identified appears in the plurality of design documents and calculates appearance frequency information of the extracted character string; which corresponds to identify differences by calculating character strings in the cells].
It would have been obvious to one of an ordinary skill in the art, having the teachings of Otto and Inoue before the affective filing date of the claimed invention to modify Otto’s data table comparison method to incorporate Inoue’s position variation for processing cell strings.
One would have been motivated to make such a combination in order to enhance the accuracy and automation of identifying correspondences and differences character strings in the cells.
Regarding claim 2, Otto and Inoue discloses wherein a similarity between the character string in one of the cells that are included in the first table and the character string in one of the cells that are included in the second table is defined as a cell similarity, and the data comparison method further comprises [see col. 6, lines 48-64; a matches table which has been populated with a set of partner rows; all three rows of matches table share the same value of the key fields (i.e., the date and engine fields in the example). After comparison via the techniques described herein, the results table is populated with the results of the comparison. In the example, the results include a row of type “changed” that includes the old and new values of the comparison field (i.e., the quantity field in the example), and a row of type “deleted” that includes the old value of the comparison field which was present in the first table but not the second table in conjunction with the present key combination can be performed multiple times, one time for each unique key combination. After each iteration the matches table is emptied, and the results table is updated. After has been performed for each key combination, the results table reflects differences between the data in the first and second data tables; which corresponds to access tables and cells aligned sufficiently implied by the matching process]:
Inoue discloses calculating the cell similarities for all combinations of the cells that are included in the first table and the cells that are included in the second table; extracting cell similarities that include a highest value from the calculated cell similarities; and determining whether the second table corresponds to the first table based on an average value of the extracted cell similarities [see para. 0103, 0104; the calculation unit calculates appearance frequency information of the extracted character string. The appearance frequency information of the character string includes, for example, the appearance frequency of the character string in each design document di and the ratio of the design documents including the character string among the design documents d1 to dn. The appearance frequency of the character string in each design document di may be said to be an index indicating how important the character string is in each design document di. The ratio of the design documents including the character string among the design documents d1 to dn may be said to be an index indicating a proportion at which the character string appears in all the design documents d1 to dn. For example, the calculation unit calculates the tf-idf value in each design document di for each group formed by grouping the extracted character strings. The calculation unit calculates the tf-idf value in each design document di for each combined group corresponding to the extracted combined character string; which corresponds to calculate and averaging to confirm links for groupings]. It would have been obvious to one of an ordinary skill in the art, having the teachings of Otto and Inoue before the affective filing date of the claimed invention to modify Otto’s data table comparison method to incorporate Inoue’s position variation for processing cell strings.
One would have been motivated to make such a combination in order to enhance the accuracy and automation of identifying correspondences and differences character strings in the cells.
Regarding claim 3, Otto and Inoue discloses wherein a similarity between character strings in a row included in the first table and character strings in a row included in the second table is defined as a row similarity, and the data comparison method further comprises [see col. 9, lines 9-19 and figure 5; At, the rows in the matches table from the first and second tables are compared, and the results table is populated with the results of the comparison. The matches table is then cleared and it is determined whether there are more rows in the first table (e.g., whether the last row fetched from the first table was the last row in the first table)]:
Inoue discloses calculating the row similarities for all combinations of the rows that are included in the first table and the rows that are included in the second table based on the average value of the cell similarities [see para, 0128-0135; based on the appearance position of a character string corresponding to each label item Lj in each design document di including the character string, the generation unit calculates a variation degree of the appearance positions of the character string among the design documents d1 to dn. The generation unit generates system structure information based on the calculated variation degree of the appearance positions of the character string and the positional relationship in each design document di between the character strings corresponding to the respective label items Lj]; extracting row similarities including a highest value from the calculated row similarities; and determining that the second table corresponds to the first table on condition that an average value of the extracted row similarities is greater than or equal to a predetermined specified value [see para. 0146-0147; When calculating the variation degree of the appearance positions, the generation unit detect an appearance position that is an outlier and calculate the variation degree by using the appearance positions other than the outlier. The generation unit calculate the variation degree by using the median value of the appearance positions. As the variation degree of the appearance positions of the character string, the variation degree only in one of the lateral direction (column direction) and the vertical direction (row direction) considered. For example, a certain type of design document has a tendency that even information at a conceptually high level has a large variation of the appearance positions in the vertical direction (row direction) while having only a small variation of the appearance positions in the lateral direction. The generation unit consider the variation degree only in the lateral direction as the variation degree of the appearance positions of the character string; which corresponds to identify differences by calculating character strings in the cells].
It would have been obvious to one of an ordinary skill in the art, having the teachings of Otto and Inoue before the affective filing date of the claimed invention to modify Otto’s data table comparison method to incorporate Inoue’s position variation for processing cell strings.
One would have been motivated to make such a combination in order to enhance the accuracy and automation of identifying correspondences and differences character strings in the cells.
Regarding claim 4, Otto discloses a computer-readable medium storing a data comparison program to be executed by an information processing device, wherein instructions included in the data comparison program includes: [see col. 9, lines 27-35; a computing system comprising: at least one hardware processor; at least one memory coupled to the at least one hardware processor; a first data table and second data table stored in memory, the first data table and the second data table each comprising a first key field, a second key field, and a comparison field; one or more non-transitory computer-readable media having stored therein computer-executable instructions that, when executed by the computing system]:
acquiring character strings in respective cells that are included in a first table in
document data [see col. 2, lines 15-18; comparing the first row of the first table with the first row of the second table, comparing the second row of the first table with the second row of the second table, and so on for all the rows in the tables. Although such a process will identify differences between the two tables from a database perspective, these are not the differences the user is typically seeking. For example, if the second table is missing one row at the beginning but is otherwise identical to the first table, the result of this approach would be an indication that all rows of the two tables are different; which corresponds to extract text arrays from cell in a first table within document];
acquiring character strings in respective cells that are included in a second table in
the document data, the second table being different from the first table [see col. 2, lines 62-67 and col.3, lines 1-8 and figure 1; First table 102 and second table 104 can each include at least one row. Each row can include respective values for each of a plurality of fields. The plurality of fields can include at least one field designated as a “key” field and at least one field designated as a “comparison” field. Optionally, the plurality of fields can include at least one field designated as an “other” field. For example, first table includes rows, each of which includes values for a date field, an engine field, and a quantity field. The date field and the engine field are designated as key fields in the example, whereas the quantity field is designated as a comparison field; which corresponds to the second table being different from the first table];
determining whether the second table corresponds to the first table based on
similarities between the character strings in the cells that are included in the first table and
the character strings in the cells that are included in the second table [see col. 5, lines 34-46 and figure 2; a request to compare first and second tables is received. The request can be initiated by a user via a user interface of the database system, or by a processor or another component of the database system. The request can also include one or more of the following. an indication of which field(s) common to the first and second table are to be designated as the key field(s); an indication of which field(s) common to the first and second tables are to be compared (e.g., which fields to designate as the comparison fields); and an indication of which field(s) common to the first and second tables to designate as other fields (e.g., fields whose values will not be compared and will appear in the results table); which corresponds to the character strings in the cells that are included in the second table]; and when determining that the second table corresponds to the first table, that are included in the first table and the character strings in the cells that are included in the second table and correspond to the cells included in the first table [see col. 8; lines 40-58 and figure 4; the second table can be stored in memory of a database system, and a processor can fetch any rows of the second database table whose key combination matches the key combination in the matches table and insert those rows into the matches table. Adding the row(s) from the second table to the matches table can include populating the source field of the matches table with the value “second table” to indicate that the row originated from the second table. The rows in the matches table from the first and second tables are compared, and the results table is populated with the results of the comparison. The matches table is then cleared at, and it is determined at whether there are more rows in the first table (e.g., whether the last row fetched from the first table was the last row in the first table)]; however, Otto fails to explicitly teach identifying a difference between the character strings in the cells.
Inoue discloses identifying a difference between the character strings in the cells [see para. 0005, 0103 and figures 5-7; acquiring a plurality of design documents about a system; identifying a plurality of label items representing elements of the system based on appearance frequency information of character strings included in the plurality of design documents acquired; and generating structure information in which the plurality of label items are hierarchized based on appearance positions at which the character string corresponding to each of the plurality of label items identified appears in the plurality of design documents and calculates appearance frequency information of the extracted character string; which corresponds to identify differences by calculating character strings in the cells].
It would have been obvious to one of an ordinary skill in the art, having the teachings of Otto and Inoue before the affective filing date of the claimed invention to modify Otto’s data table comparison method to incorporate Inoue’s position variation for processing cell strings.
One would have been motivated to make such a combination in order to enhance the accuracy and automation of identifying correspondences and differences character strings in the cells.
Regarding claim 5, Otto discloses discloses an information processing device being configured [see abstract; matches table is generated which includes the key field(s), comparison field(s), and a source field whose value indicates the originating table of the data in the row]:
acquire character strings in respective cells that are included in a first table in
document data [see col. 2, lines 15-18; comparing the first row of the first table with the first row of the second table, comparing the second row of the first table with the second row of the second table, and so on for all the rows in the tables. Although such a process will identify differences between the two tables from a database perspective, these are not the differences the user is typically seeking. For example, if the second table is missing one row at the beginning but is otherwise identical to the first table, the result of this approach would be an indication that all rows of the two tables are different; which corresponds to extract text arrays from cell in a first table within document];
acquire character strings in respective cells that are included in a second table in
the document data, the second table being different from the first table [see col. 2, lines 62-67 and col.3, lines 1-8 and figure 1; First table 102 and second table 104 can each include at least one row. Each row can include respective values for each of a plurality of fields. The plurality of fields can include at least one field designated as a “key” field and at least one field designated as a “comparison” field. Optionally, the plurality of fields can include at least one field designated as an “other” field. For example, first table includes rows, each of which includes values for a date field, an engine field, and a quantity field. The date field and the engine field are designated as key fields in the example, whereas the quantity field is designated as a comparison field; which corresponds to the second table being different from the first table];
determine whether the second table corresponds to the first table based on
similarities between the character strings in the cells that are included in the first table and
the character strings in the cells that are included in the second table [see col. 5, lines 34-46 and figure 2; a request to compare first and second tables is received. The request can be initiated by a user via a user interface of the database system, or by a processor or another component of the database system. The request can also include one or more of the following. an indication of which field(s) common to the first and second table are to be designated as the key field(s); an indication of which field(s) common to the first and second tables are to be compared (e.g., which fields to designate as the comparison fields); and an indication of which field(s) common to the first and second tables to designate as other fields (e.g., fields whose values will not be compared and will appear in the results table); which corresponds to the character strings in the cells that are included in the second table]; and when determining that the second table corresponds to the first table, that are included in the first table and the character strings in the cells that are included in the second table and correspond to the cells included in the first table [see col. 8; lines 40-58 and figure 4; the second table can be stored in memory of a database system, and a processor can fetch any rows of the second database table whose key combination matches the key combination in the matches table and insert those rows into the matches table. Adding the row(s) from the second table to the matches table can include populating the source field of the matches table with the value “second table” to indicate that the row originated from the second table. The rows in the matches table from the first and second tables are compared, and the results table is populated with the results of the comparison. The matches table is then cleared at, and it is determined at whether there are more rows in the first table (e.g., whether the last row fetched from the first table was the last row in the first table)]; however, Otto fails to explicitly teach identifying a difference between the character strings in the cells.
Inoue discloses identifying a difference between the character strings in the cells [see para. 0005, 0103 and figures 5-7; acquiring a plurality of design documents about a system; identifying a plurality of label items representing elements of the system based on appearance frequency information of character strings included in the plurality of design documents acquired; and generating structure information in which the plurality of label items are hierarchized based on appearance positions at which the character string corresponding to each of the plurality of label items identified appears in the plurality of design documents and calculates appearance frequency information of the extracted character string; which corresponds to identify differences by calculating character strings in the cells].
It would have been obvious to one of an ordinary skill in the art, having the teachings of Otto and Inoue before the affective filing date of the claimed invention to modify Otto’s data table comparison method to incorporate Inoue’s position variation for processing cell strings.
One would have been motivated to make such a combination in order to enhance the accuracy and automation of identifying correspondences and differences character strings in the cells.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Currenti et al. (US 11,180,462) discloses a method and system for displaying the comparison output of two versions of a spreadsheet document such that the source formatting of the spreadsheet can be selectively suppressed to enhance the legibility of the comparison output.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAO H NGUYEN whose telephone number is (571)272-4053. The examiner can normally be reached on Mon-Fri 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached on 571-272-4057. The fax phone number for the organization where this application or proceeding 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 either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CAO H NGUYEN/Primary Examiner, Art Unit 2171