DETAILED ACTION
This office action is in response to Applicant’s arguments and amendments filed on April 30, 2026. The application contains claims 1-41:
Claims 1-21 were previously cancelled
Claims 22 and 32 are amended
Claims 22-41 are pending.
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 .
Response to Arguments
Applicant's arguments and amendments filed on April 30, 2026 have been fully considered and the objections and rejections are updated accordingly.
Objection to the Specification
In view of the amendments to the abstract, the objection to the specification is withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to the new limitations introduced with the amendments are addressed with new rationale as set forth in the updated prior art rejections below.
However, for purposes of clarity, the examiner responds to Applicant’s main argument on page 2 of Applicant’s Arguments/Remarks Made in an Amendment as quoted below:
“… Connolly discusses matching metadata of social media posts to the personal profiles of social networking users, but fails entirely to disclose or suggest comparing a value derived from an analysis of data values to a user- specific dataset attribute comprising a dataset-related characteristic describing data of a dataset. In other words, Connolly identifies social network connections by comparing hashtags on photographs or mutual "likes" on a status update, rather than comparing characteristics describing the actual data within a dataset associated with a second user.”
The examiner disagrees with Applicant’s allegation that Connolly et al. (US 20160055159 A1) does not teach the new limitations introduced with the amendments. Applicant’s above underlined characterization of Connolly is false. To the contrary, Connolly teaches inferring each user’s interests and preferences from the user’s actions and identifying users relevant to each other by comparing the inferred information for each user. Please refer to the support in Connolly as follows:
Paragraph [0032] teaches computing affinity scores to approximate a user's interest for other user in the social networking system 140 based on the actions performed by the user. Paragraphs [0033] and [0034] teach retrieving and analyzing action information about a user to identify the user’s interests and identifying additional users of interest to the user based on the actions taken by the additional users. Paragraph [0028] explicitly teaches inferring interests or preferences of a user using data from the action log 220. Paragraph [0024] teaches profile information included in a user profile may be inferred by the social networking system 140.
Clearly, Connolly determines user relevancy based on comparing users’ interests inferred from user actions, not by using any readily available information in the form of either metadata or user profiles.
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.
Claims 22-27, 29, 31-37, 39, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over BICER et al. (US 20170357653 A1), in view of Stojanovic et al. (US 20160092475 A1), and in further view of Connolly et al. (US 20160055159 A1).
With regard to claim 22,
BICER teaches
a computer-implemented method (Abstract), comprising:
obtaining a dataset having a first data format, the dataset associated with a first user (Fig. 1; [0032]: receive table 10 of city data as input to method 11, where table 10 may also be in the form of comma-separated values (CSVs), i.e., "a first data format", and city corresponds to "a first user");
generating an atomized dataset by transforming the dataset from the first data format to an atomized data format (Fig. 1; [0032]: method 11 transforms table 10 to table 12, which is a list of enhanced RDF triples, i.e., "an atomized data format");
BICER does not teach
deriving, from an analysis of data values within the dataset, an inferred dataset attribute associated with a subset of data within the dataset;
causing presentation of a data arrangement overview interface summarizing a value of the inferred dataset attribute;
obtaining global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users;
determining a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute derived from the analysis of the data values to a value of a user-specific dataset attribute comprising a dataset-related characteristic describing data of a dataset associated with the second user;
based on the degree of relevancy satisfying a relevance threshold, identifying the second user as a relevant collaborator; and
causing presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface.
Stojanovic teaches
deriving, from an analysis of data values within the dataset, an inferred dataset attribute associated with a subset of data within the dataset (Fig. 3B; [0077]-[0079]; [0081]: profile engine 326 may compute a pattern metric (e.g., a statistical frequency of different patterns in the data) for each of the different patterns identified in the data, wherein a metric pattern corresponds to “an inferred dataset attribute” and identifying different patterns and computing a pattern metric indicate deriving “from an analysis of data values within the dataset”);
causing presentation of a data arrangement overview interface summarizing a value of the inferred dataset attribute (Fig. 5C; [0119]: column profile 564 displays a summary of metric values calculated for a particular column 562, for example, the values in Min Length and Max Length correspond to "a value of the inferred dataset attribute");
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER to incorporate the teachings of Stojanovic to derive, from the dataset, an inferred dataset attribute associated with a subset of data within the dataset and cause presentation of a data arrangement overview interface summarizing a value of the inferred dataset attribute. Doing so would output a number of metrics and pattern information about each identified column, and can identify schema information in the form of names and types of the columns to match the data as taught by Stojanovic ([0062]).
BICER and Stojanovic do not teach
obtaining global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users;
determining a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute derived from the analysis of the data values to a value of a user-specific dataset attribute comprising a dataset-related characteristic describing data of a dataset associated with the second user;
based on the degree of relevancy satisfying a relevance threshold, identifying the second user as a relevant collaborator; and
causing presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface.
Connolly teaches
obtaining global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users ([0049]: the social networking system 140 maintains a global set of characteristics including a specified number of characteristics and specified types of characteristics. [0037]: example characteristics of a content item include attributes of a user who posted the content item to the social networking system, information describing additional social networking system users that interacted with the content item, or a combination of user-specific and global user attributes);
determining a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute derived from the analysis of the data values to a value of a user-specific dataset attribute comprising a dataset-related characteristic describing data of a dataset associated with the second user (Fig. 2; [0032]: compute affinity scores to approximate a user's interest for other user in the social networking system 140 based on the actions performed by the user. [0033]-[0034]: retrieve and analyze action information about a user to identify the user’s interests and identify additional users of interest to the user based on the actions taken by the additional users. [0028]: infer interests or preferences of a user using data from the action log 220. [0024]: profile information included in a user profile may be inferred by the social networking system 140);
based on the degree of relevancy satisfying a relevance threshold, identifying the second user as a relevant collaborator (Fig. 4; [0065]-[0067]: identify additional users 415 as users having at least a threshold number or percentage of characteristics matching or similar to attributes associated with the user, wherein a number or percentage of characteristics associated with the user corresponds to "a value of the inferred dataset attribute" while a number or percentage of characteristics associated with an additional user corresponds to "a value of a user-specific dataset attribute"); and
causing presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface (Fig. 4; [0071]: communicate 435 the selected one or more content items from the social networking system 140 to a client device 110 for presentation to the user along with an identifier associated with the selected additional user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER and Stojanovic to incorporate the teachings of Connolly to obtain global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users, determine a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute to a value of a user-specific dataset attribute associated with the second user, based on the degree of relevancy satisfying a relevance threshold, identify the second user as a relevant collaborator, and cause presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface. Doing so would overcome the drawback of a user being presented with content items or additional users in which the user may have minimal interest. If a social networking system presents the user with content items or users in which the user is uninterested, the user may discourage the user from interacting with the social networking system as taught by Connolly ([0003]).
With regard to claim 23,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the computer-implemented method of claim 22, wherein deriving the inferred dataset attribute comprises analyzing a column of data within the dataset to automatically infer a datatype or a data classification for the column ([0084]-[0085]: profile engine 326 can analyze data statistically to characterize the content of large quantities of data, and can provide global statistics about the data and a per-column analysis of the data's content. Column-specific statistics may include data type and subtype).
With regard to claim 24,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER further teaches
the computer-implemented method of claim 22, wherein generating the atomized dataset comprises storing the atomized dataset in a triplestore, the atomized data format comprising a graph data structure storing data as triples (Fig. 5, 11, and 12; [0044]: transform the set of denormalized records 41 into a set of new RDF triples 53, and the new RDF triples can be transformed into a RDF graph, shown in FIG. 12).
With regard to claim 25,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the computer-implemented method of claim 22, wherein the value of the inferred dataset attribute summarized in the data arrangement overview interface comprises a statistical characteristic of the subset of data, the statistical characteristic including at least one of a mean, a minimum value, a maximum value, or a standard deviation ([0084]-[0085]: the profile engine 326 can provide global statistics about the data and a per-column analysis of the data's content. For example, numeric data can be analyzed statistically, including, e.g., N, mean, maximum, minimum, standard deviation, etc.).
With regard to claim 26,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the computer-implemented method of claim 22, further comprising, responsive to a user interaction with the data arrangement overview interface, causing presentation of an interactive overlay window comprising additional summary characteristics for the subset of data (Fig. 5C; [0119]: the user can select the particular column on the user's client device and the corresponding column profile 564 can be displayed as shown in Fig. 5C).
With regard to claim 27,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the computer-implemented method of claim 22, wherein determining the degree of relevancy comprises calculating a relevancy score based on the comparison, and wherein the relevance threshold is a minimum relevancy score ([0112]: knowledge service 310 can provide a confidence score for a given pattern match. A threshold can be set in recommendation engine 308 such that matches having a confidence score greater than the threshold are applied automatically).
With regard to claim 29,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER further teaches
the computer-implemented method of claim 22, wherein the activity feed portion of the user interface includes a user interface element configured to, upon activation, initiate a process to link the atomized dataset with a second dataset associated with the relevant collaborator (Fig. 1; [0032]: link the output table 12 with other city linked data 13. Using an UI element to trigger this taught function is obvious and within the purview of one of ordinary skill in the art before the effective filing date of the claimed invention).
With regard to claim 31,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
Connolly further teaches
the computer-implemented method of claim 22, wherein the user-specific dataset attribute associated with the second user is an inferred dataset attribute derived from a second dataset associated with the second user (Fig. 4; [0065]-[0067]: identify additional users 415 as users having at least a threshold number or percentage of characteristics matching or similar to attributes associated with the user, wherein a number or percentage of characteristics associated with an additional user is “an inferred dataset attribute derived from” the additional user profile, i.e., “a second dataset associated with the second user”).
With regard to claim 32,
BICER teaches
a system (Fig. 13) comprising:
a memory (Fig. 13: memory 1328) including executable instructions; and
a processor (Fig. 13: processing unit 1316) configured to execute the instructions, the instructions comprising:
obtaining a dataset having a first data format, the dataset associated with a first user (Fig. 1; [0032]: receive table 10 of city data as input to method 11, where table 10 may also be in the form of comma-separated values (CSVs), i.e., "a first data format", and city corresponds to "a first user");
generating an atomized dataset by transforming the dataset from the first data format to an atomized data format (Fig. 1; [0032]: method 11 transforms table 10 to table 12, which is a list of enhanced RDF triples, i.e., "an atomized data format");
BICER does not teach
deriving, from an analysis of data values within the dataset, an inferred dataset attribute associated with a subset of data within the dataset;
causing presentation of a data arrangement overview interface summarizing a value of the inferred dataset attribute;
obtaining global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users;
determining a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute derived from the analysis of the data values to a value of a user-specific dataset attribute comprising a dataset-related characteristic describing data of a dataset associated with the second user;
based on the degree of relevancy satisfying a relevance threshold, identifying the second user as a relevant collaborator; and
causing presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface.
Stojanovic teaches
deriving, from an analysis of data values within the dataset, an inferred dataset attribute associated with a subset of data within the dataset (Fig. 3B; [0077]-[0079]; [0081]: profile engine 326 may compute a pattern metric (e.g., a statistical frequency of different patterns in the data) for each of the different patterns identified in the data, wherein a metric pattern corresponds to “an inferred dataset attribute” and identifying different patterns and computing a pattern metric indicate deriving “from an analysis of data values within the dataset”);
causing presentation of a data arrangement overview interface summarizing a value of the inferred dataset attribute (Fig. 5C; [0119]: column profile 564 displays a summary of metric values calculated for a particular column 562, for example, the values in Min Length and Max Length correspond to "a value of the inferred dataset attribute");
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER to incorporate the teachings of Stojanovic to derive, from the dataset, an inferred dataset attribute associated with a subset of data within the dataset and cause presentation of a data arrangement overview interface summarizing a value of the inferred dataset attribute. Doing so would output a number of metrics and pattern information about each identified column, and can identify schema information in the form of names and types of the columns to match the data as taught by Stojanovic ([0062]).
BICER and Stojanovic do not teach
obtaining global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users;
determining a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute derived from the analysis of the data values to a value of a user-specific dataset attribute comprising a dataset-related characteristic describing data of a dataset associated with the second user;
based on the degree of relevancy satisfying a relevance threshold, identifying the second user as a relevant collaborator; and
causing presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface.
Connolly teaches
obtaining global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users ([0049]: the social networking system 140 maintains a global set of characteristics including a specified number of characteristics and specified types of characteristics. [0037]: example characteristics of a content item include attributes of a user who posted the content item to the social networking system, information describing additional social networking system users that interacted with the content item, or a combination of user-specific and global user attributes);
determining a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute derived from the analysis of the data values to a value of a user-specific dataset attribute comprising a dataset-related characteristic describing data of a dataset associated with the second user (Fig. 2; [0032]: compute affinity scores to approximate a user's interest for other user in the social networking system 140 based on the actions performed by the user. [0033]-[0034]: retrieve and analyze action information about a user to identify the user’s interests and identify additional users of interest to the user based on the actions taken by the additional users. [0028]: infer interests or preferences of a user using data from the action log 220. [0024]: profile information included in a user profile may be inferred by the social networking system 140);
based on the degree of relevancy satisfying a relevance threshold, identifying the second user as a relevant collaborator (Fig. 4; [0065]-[0067]: identify additional users 415 as users having at least a threshold number or percentage of characteristics matching or similar to attributes associated with the user, wherein a number or percentage of characteristics associated with the user corresponds to "a value of the inferred dataset attribute" while a number or percentage of characteristics associated with an additional user corresponds to "a value of a user-specific dataset attribute"); and
causing presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface (Fig. 4; [0071]: communicate 435 the selected one or more content items from the social networking system 140 to a client device 110 for presentation to the user along with an identifier associated with the selected additional user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER and Stojanovic to incorporate the teachings of Connolly to obtain global dataset attribute data comprising a plurality of user-specific dataset attributes associated with a community of users, determine a degree of relevancy between the first user and a second user from the community of users by comparing a value of the inferred dataset attribute to a value of a user-specific dataset attribute associated with the second user, based on the degree of relevancy satisfying a relevance threshold, identify the second user as a relevant collaborator, and cause presentation of an identifier of the relevant collaborator in an activity feed portion of a user interface. Doing so would overcome the drawback of a user being presented with content items or additional users in which the user may have minimal interest. If a social networking system presents the user with content items or users in which the user is uninterested, the user may discourage the user from interacting with the social networking system as taught by Connolly ([0003]).
With regard to claim 33,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the system of claim 32, wherein deriving the inferred dataset attribute comprises analyzing a column of data within the dataset to automatically infer a datatype or a data classification for the column ([0084]-[0085]: profile engine 326 can analyze data statistically to characterize the content of large quantities of data, and can provide global statistics about the data and a per-column analysis of the data's content. Column-specific statistics may include data type and subtype).
With regard to claim 34,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER further teaches
the system of claim 32, wherein generating the atomized dataset comprises storing the atomized dataset in a triplestore, the atomized data format comprising a graph data structure storing data as triples (Fig. 5, 11, and 12; [0044]: transform the set of denormalized records 41 into a set of new RDF triples 53, and the new RDF triples can be transformed into a RDF graph, shown in FIG. 12).
With regard to claim 35,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the system of claim 32, wherein the value of the inferred dataset attribute summarized in the data arrangement overview interface comprises a statistical characteristic of the subset of data, the statistical characteristic including at least one of a mean, a minimum value, a maximum value, or a standard deviation ([0084]-[0085]: the profile engine 326 can provide global statistics about the data and a per-column analysis of the data's content. For example, numeric data can be analyzed statistically, including, e.g., N, mean, maximum, minimum, standard deviation, etc.).
With regard to claim 36,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the system of claim 32, further comprising, responsive to a user interaction with the data arrangement overview interface, causing presentation of an interactive overlay window comprising additional summary characteristics for the subset of data (Fig. 5C; [0119]: the user can select the particular column on the user's client device and the corresponding column profile 564 can be displayed as shown in Fig. 5C).
With regard to claim 37,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
Stojanovic further teaches
the system of claim 32, wherein determining the degree of relevancy comprises calculating a relevancy score based on the comparison, and wherein the relevance threshold is a minimum relevancy score ([0112]: knowledge service 310 can provide a confidence score for a given pattern match. A threshold can be set in recommendation engine 308 such that matches having a confidence score greater than the threshold are applied automatically).
With regard to claim 39,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER further teaches
the system of claim 32, wherein the activity feed portion of the user interface includes a user interface element configured to, upon activation, initiate a process to link the atomized dataset with a second dataset associated with the relevant collaborator (Fig. 1; [0032]: link the output table 12 with other city linked data 13. Using an UI element to trigger this taught function is obvious and within the purview of one of ordinary skill in the art before the effective filing date of the claimed invention).
With regard to claim 41,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
Connolly further teaches
the system of claim 32, wherein the user-specific dataset attribute associated with the second user is an inferred dataset attribute derived from a second dataset associated with the second user (Fig. 4; [0065]-[0067]: identify additional users 415 as users having at least a threshold number or percentage of characteristics matching or similar to attributes associated with the user, wherein a number or percentage of characteristics associated with an additional user is “an inferred dataset attribute derived from” the additional user profile, i.e., “a second dataset associated with the second user”).
Claims 28 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over BICER et al. (US 20170357653 A1), in view of Stojanovic et al. (US 20160092475 A1), and in further view of Connolly et al. (US 20160055159 A1) and ADAMI et al. (US 20180032327 A1).
With regard to claim 28,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER and Stojanovic and Connolly do not teach
the computer-implemented method of claim 22, wherein the obtained dataset is a restricted dataset, the method further comprising transmitting authorization data to permit the deriving of the inferred dataset attribute from the restricted dataset.
ADAMI teaches
the computer-implemented method of claim 22, wherein the obtained dataset is a restricted dataset, the method further comprising transmitting authorization data to permit the deriving of the inferred dataset attribute from the restricted dataset ([0078]-[0079]: the dataset access control module 107 facilitate the protection from unauthorized access to the datasets stored in the relational database. The content stored on the DSB is restricted by an authentication and session management mechanism, wherein transmitting authorization data in order to gain access to the restricted dataset is inherently taught).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER and Stojanovic and Connolly to incorporate the teachings of ADAMI to transmit authorization data to permit the deriving of the inferred dataset attribute from the restricted dataset. Doing so would allow or deny the access to the dataset to a specific user in accordance with the permission given to that user as taught by ADAMI ([0078]).
With regard to claim 38,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER and Stojanovic and Connolly do not teach
the system of claim 32, wherein the obtained dataset is a restricted dataset, the method further comprising transmitting authorization data to permit the deriving of the inferred dataset attribute from the restricted dataset.
ADAMI teaches
the system of claim 32, wherein the obtained dataset is a restricted dataset, the instructions further comprising transmitting authorization data to permit the deriving of the inferred dataset attribute from the restricted dataset ([0078]-[0079]: the dataset access control module 107 facilitate the protection from unauthorized access to the datasets stored in the relational database. The content stored on the DSB is restricted by an authentication and session management mechanism, wherein transmitting authorization data in order to gain access to the restricted dataset is inherently taught).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER and Stojanovic and Connolly to incorporate the teachings of ADAMI to transmit authorization data to permit the deriving of the inferred dataset attribute from the restricted dataset. Doing so would allow or deny the access to the dataset to a specific user in accordance with the permission given to that user as taught by ADAMI ([0078]).
Claims 30 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over BICER et al. (US 20170357653 A1), in view of Stojanovic et al. (US 20160092475 A1), and in further view of Connolly et al. (US 20160055159 A1) and WU et al. (US 20130318070 A1).
With regard to claim 30,
As discussed regarding claim 22, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER and Stojanovic and Connolly do not teach
the computer-implemented method of claim 22, wherein obtaining the dataset is responsive to an operation instruction received in a high-level programming language, the method further comprising transforming the operation instruction into a graph-related data instruction to access the dataset when stored in the atomized data format.
WU teaches
the computer-implemented method of claim 22, wherein obtaining the dataset is responsive to an operation instruction received in a high-level programming language, the method further comprising transforming the operation instruction into a graph-related data instruction to access the dataset when stored in the atomized data format (Abstract; Fig. 1A; [0025]: receive a database query (e.g., an SQL query) from an application, the query being in a first query language format, then convert the query into a SPARQL query format and submit the converted query to a SPARQL endpoint. The SPARQL endpoint processes the SPARQL query (e.g., by accessing an RDF repository), and sends back the query results).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER and Stojanovic and Connolly to incorporate the teachings of WU to obtain the dataset responsive to an operation instruction received in a high-level programming language and transform the operation instruction into a graph-related data instruction to access the dataset when stored in the atomized data format. Doing so would process a native database query using through a gateway service in order to access a SPARQL endpoint that uses the resource description framework (RDF) language as taught by WU ([0008]).
With regard to claim 40,
As discussed regarding claim 32, BICER and Stojanovic and Connolly teach all the limitations therein.
BICER and Stojanovic and Connolly do not teach
the system of claim 32, wherein obtaining the dataset is responsive to an operation instruction received in a high-level programming language, the method further comprising transforming the operation instruction into a graph-related data instruction to access the dataset when stored in the atomized data format.
WU teaches
the system of claim 32, wherein obtaining the dataset is responsive to an operation instruction received in a high-level programming language, the instructions further comprising transforming the operation instruction into a graph-related data instruction to access the dataset when stored in the atomized data format (Abstract; Fig. 1A; [0025]: receive a database query (e.g., an SQL query) from an application, the query being in a first query language format, then convert the query into a SPARQL query format and submit the converted query to a SPARQL endpoint. The SPARQL endpoint processes the SPARQL query (e.g., by accessing an RDF repository), and sends back the query results).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified BICER and Stojanovic and Connolly to incorporate the teachings of WU to obtain the dataset responsive to an operation instruction received in a high-level programming language and transform the operation instruction into a graph-related data instruction to access the dataset when stored in the atomized data format. Doing so would process a native database query using through a gateway service in order to access a SPARQL endpoint that uses the resource description framework (RDF) language as taught by WU ([0008]).
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOQIN HU whose telephone number is (571)272-1792. The examiner can normally be reached on Monday-Friday 7:00am-3:30pm.
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/XIAOQIN HU/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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