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 .
Status of Claims
In response to communications filed on 23 March 2025, claims 1-3, 5-12, 14-22 are presently pending in the application, of which, claims 1, 10, and 19 are presented in independent form. The Examiner acknowledges amended claims 1-3, 5-12, and 14-19, canceled claims 4 and 13, and newly added claims 20-22.
Priority
The Examiner acknowledges the instant application claims priority to U.S. Patent Application No. 17/490,186, filed 30 September 2021, now issued as U.S. Patent 12,282,757, which is a continuation of U.S. Patent Application No. 15/683,551, filed 22 August 2017, now issued as U.S. Patent 11,137,987, which is a continuation of U.S. Provisional 62/378,147, 62/378,150, 62/378,143, 62/378,143, 62/378,151, 62/378,152, and 62/378,146, all filed on 22 August 2016, and has been accorded the earliest effective file date.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 23 March 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Terminal Disclaimer
The terminal disclaimer filed on 23 March 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. 11,137,987 and U.S. 12,282,757 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Arguments
Applicant’s arguments, see page 8, filed supra, with respect to claims 1-20 under 35 U.S.C. 101 have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of claims 1-20 has been withdrawn.
Applicant’s arguments, see page 7, filed supra, with respect to the rejections of claims 1-3, 5-12, and 14-22 under 35 U.S.C. 102(a)(1)/(a)(2) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Stojanovic, Alexander Sasha, et al (U.S. 2016/0092476, filed 24 September 2015, published 31 March 2016 and known hereinafter as Stojanovic).
The Examiner notes that due to the incorrect prior art cited in the previous office action, filed 22 September 2025, the current rejection set forth herewith will be a non-final rejection.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-12, and 14-22 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Stojanovic, Alexander Sasha, et al (U.S. 2016/0092476, filed 24 September 2015, published 31 March 2016 and known hereinafter as Stojanovic)(newly presented).
As per claim 1, Stojanovic teaches a method for use with a data integration or other computing environment, comprising:
providing, at a system including one or more processors, a computational environment, including a user interface, for design and management of software application pipelines (e.g. Stojanovic, see paragraphs [0039, 0043, 0055, and 0089], which discloses prior to loading data into a warehouse the data is processed through a pipeline which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare stage, profile stage, transform stage, and publish stage. A user can specify via a user interface input for identifying data sources from which to obtain input data.),
wherein an application pipeline defines a data flow having a plurality of semantic actions that operate on an input data for preparation as an output data (e.g. Stojanovic, see paragraph [0041], which discloses the data may be processed semantically by a number of processing stages (e.g. semantic pipeline). These processing stages can include preparation stages, enrichment stages, and publishing stages, where the processed data is output data.);
wherein data is received from a data source operating as an input hub and comprising a source dataset (e.g. Stojanovic, see paragraph [0039], which discloses prior to loading data into a data warehouse the data is processed through a pipeline which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare state, profile stage, transform stage, and publish stage.), and provided to a target dataset at a same or other hub, according to the defined data flow (e.g. Stojanovic, see paragraphs [0039, 0043, 0050-0055, and 0089], which discloses when a data source is added, the data source and/or the data stored thereon can be processed through a pipeline prior to loading the data source. The pipeline can include one or more processing engines that are configured to process the data and/or data source before publishing the processed data to one or more data targets. The processing engines can include an ingest engine that extracts raw data from the new data source and provides the raw data to a prepare engine. The prepare engine can identify a format associated with the raw data and can convert the raw data into a format (e.g. normalize the raw data) that can be processed by the data enrichment service. A profile engine can extract and/or generate metadata associated with the normalized data and a transform engine can transform (e.g. repair and/or enrich) the normalized data based on the metadata. The resulting enriched data can be provided to the publish engine to be sent to one or more data targets.); and
providing, for use with the application pipeline, a recommended mapping of semantic actions between the source dataset and the target dataset, based on a profiling of the datasets (e.g. Stojanovic, see paragraphs [0019, 0117, 0055, 0060-0064], which discloses a user can interact with the data enrichment service through user interface. Clients can render graphical user interface to display the user’s data, recommendations for transforming the user’s data, and to send and/or receive instructions (‘transformation instructions’) to the data enrichment service through user interface. The output, or results, of profile engine may be metadata (e.g. source profile) indicating profile information about the data from a source. The metadata may indicate one or more patterns about the data and/or a classification of the data. As further described below, the metadata may include statistical information based on analysis of the data. For example, profile engine can 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.), including:
receiving a request for mapping associated with an application, together with information identifying one or more input hub, dataset, entity, or attribute, to be used in searching to find a potential candidate set for mapping (e.g. Stojanovic discloses a client can submit a data enrichment request to data enrichment service 102 identifies one or more of the data sources 104 (or portions thereof, e.g., particular tables, datasets, etc.). The data enrichment service 102 may then request data to be processed from the identified data sources 104 [0041]. Recommendation engine 308 can request (e.g., query) knowledge service 310 for data that can be recommended to a user for the data obtained for a source [0062]. Transform scripts listed in panel 402 may be automatically applied to the data and reflected in the portion of the data 406 displayed in the interactive user interface 400. For example, the transform scripts listed in patent 402 include renaming columns to be descriptive of their content. Columns 408 shown in interactive user interface 400 have been renamed according to the transform scripts 402 (e.g., column 0003 is now named date time 02, column 0007 is no named “url”, etc.) [0108]);
determining a candidate set of hubs and datasets for mapping, responsive to a search query (e.g. Stojanovic, see paragraphs [0062, 0098, 0091], which discloses the recommendation engine can identify, repair, transform and data enrichment recommendations for the data processed by profile engine. The metadata generated by profile engine can be used to determine recommendations for data based on the statistical analysis and/or classifications indicated by the metadata. Figure 5B, which shows a user interface that can include a profile metric panel. Panel show a summary of metrics associated with the selected data source, as further discloses in paragraph [0117].);
comparing pairs of source and target datasets associated with the candidate set, based on profiling the data therein (e.g. Stojanovic, see paragraphs [0092, 0100, 0103-0105], which discloses knowledge service can implement a matching method to compare the data to reference data available through knowledge service, where the knowledge service can implement a method to determine the semantic similarity between two or more data sets. The scoring formula may determine semantic similarity between two data sets and terms in the domain to obtain from a knowledge source. The domain for which the matching score indicates the best match (e.g. highest matching score) may be chosen as the domain having the greatest similarity with the input data set.); and
providing one or more recommended mappings of hubs and datasets to the user interface, reflective of a stage associated with the design or management of the application pipeline (e.g. Stojanovic, see paragraphs [0108, 0090, 0061-0064], which discloses a as shown in Figure 4A, an example interactive user interface can display transform scripts, recommend transforms and at least a portion of the data being analyzed/transformed. Transform scripts listed in panel can include indicate transforms that have been applied to the data and are visible in panel. Each transform script can be written in a simple declarative language intelligible to a business user. Transform scripts listed in panel may be automatically applied to the data and reflected in the portion of the data displayed in the interactive user interface, as described in paragraph [0108, 0090]. Additionally, in some embodiments, transform engine can present the user with the sampled data for each column, or sample rows from the input dataset through user interface. Through user interface, data enrichment service may present a user with recommended transformations. The transformations may be associated with transformation instructions, which may include and/or function calls to perform transformation actions. The transformation instructions may be invoked by a user based on selection at user interface, such as by selecting a recommendation for transformation or by receiving input indicating an operation.).
As per claim 10, Stojanovic teaches a system for auto-mapping of complex data structures, datasets or entities, for use with a data integration or other computing environment, comprising:
one or more processors operable to provide:
providing, at a system including one or more processors, a computational environment, including a user interface, for design and management of software application pipelines (e.g. Stojanovic, see paragraphs [0039, 0043, 0055, and 0089], which discloses prior to loading data into a warehouse the data is processed through a pipeline which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare stage, profile stage, transform stage, and publish stage. A user can specify via a user interface input for identifying data sources from which to obtain input data.),
wherein an application pipeline defines a data flow having a plurality of semantic actions that operate on an input data for preparation as an output data (e.g. Stojanovic, see paragraph [0041], which discloses the data may be processed semantically by a number of processing stages (e.g. semantic pipeline). These processing stages can include preparation stages, enrichment stages, and publishing stages, where the processed data is output data.);
wherein data is received from a data source operating as an input hub and comprising a source dataset (e.g. Stojanovic, see paragraph [0039], which discloses prior to loading data into a data warehouse the data is processed through a pipeline which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare state, profile stage, transform stage, and publish stage.), and provided to a target dataset at a same or other hub, according to the defined data flow (e.g. Stojanovic, see paragraphs [0039, 0043, 0050-0055, and 0089], which discloses when a data source is added, the data source and/or the data stored thereon can be processed through a pipeline prior to loading the data source. The pipeline can include one or more processing engines that are configured to process the data and/or data source before publishing the processed data to one or more data targets. The processing engines can include an ingest engine that extracts raw data from the new data source and provides the raw data to a prepare engine. The prepare engine can identify a format associated with the raw data and can convert the raw data into a format (e.g. normalize the raw data) that can be processed by the data enrichment service. A profile engine can extract and/or generate metadata associated with the normalized data and a transform engine can transform (e.g. repair and/or enrich) the normalized data based on the metadata. The resulting enriched data can be provided to the publish engine to be sent to one or more data targets.); and
providing, for use with the application pipeline, a recommended mapping of semantic actions between the source dataset and the target dataset, based on a profiling of the datasets (e.g. Stojanovic, see paragraphs [0019, 0117, 0055, 0060-0064], which discloses a user can interact with the data enrichment service through user interface. Clients can render graphical user interface to display the user’s data, recommendations for transforming the user’s data, and to send and/or receive instructions (‘transformation instructions’) to the data enrichment service through user interface. The output, or results, of profile engine may be metadata (e.g. source profile) indicating profile information about the data from a source. The metadata may indicate one or more patterns about the data and/or a classification of the data. As further described below, the metadata may include statistical information based on analysis of the data. For example, profile engine can 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.), including:
receiving a request for mapping associated with an application, together with information identifying one or more input hub, dataset, entity, or attribute, to be used in searching to find a potential candidate set for mapping (e.g. Stojanovic discloses a client can submit a data enrichment request to data enrichment service 102 identifies one or more of the data sources 104 (or portions thereof, e.g., particular tables, datasets, etc.). The data enrichment service 102 may then request data to be processed from the identified data sources 104 [0041]. Recommendation engine 308 can request (e.g., query) knowledge service 310 for data that can be recommended to a user for the data obtained for a source [0062]. Transform scripts listed in panel 402 may be automatically applied to the data and reflected in the portion of the data 406 displayed in the interactive user interface 400. For example, the transform scripts listed in patent 402 include renaming columns to be descriptive of their content. Columns 408 shown in interactive user interface 400 have been renamed according to the transform scripts 402 (e.g., column 0003 is now named date time 02, column 0007 is no named “url”, etc.) [0108]);
determining a candidate set of hubs and datasets for mapping, responsive to a search query (e.g. Stojanovic, see paragraphs [0062, 0098, 0091], which discloses the recommendation engine can identify, repair, transform and data enrichment recommendations for the data processed by profile engine. The metadata generated by profile engine can be used to determine recommendations for data based on the statistical analysis and/or classifications indicated by the metadata. Figure 5B, which shows a user interface that can include a profile metric panel. Panel show a summary of metrics associated with the selected data source, as further discloses in paragraph [0117].);
comparing pairs of source and target datasets associated with the candidate set, based on profiling the data therein (e.g. Stojanovic, see paragraphs [0092, 0100, 0103-0105], which discloses knowledge service can implement a matching method to compare the data to reference data available through knowledge service, where the knowledge service can implement a method to determine the semantic similarity between two or more data sets. The scoring formula may determine semantic similarity between two data sets and terms in the domain to obtain from a knowledge source. The domain for which the matching score indicates the best match (e.g. highest matching score) may be chosen as the domain having the greatest similarity with the input data set.); and
providing one or more recommended mappings of hubs and datasets to the user interface, reflective of a stage associated with the design or management of the application pipeline (e.g. Stojanovic, see paragraphs [0108, 0090, 0061-0064], which discloses a as shown in Figure 4A, an example interactive user interface can display transform scripts, recommend transforms and at least a portion of the data being analyzed/transformed. Transform scripts listed in panel can include indicate transforms that have been applied to the data and are visible in panel. Each transform script can be written in a simple declarative language intelligible to a business user. Transform scripts listed in panel may be automatically applied to the data and reflected in the portion of the data displayed in the interactive user interface, as described in paragraph [0108, 0090]. Additionally, in some embodiments, transform engine can present the user with the sampled data for each column, or sample rows from the input dataset through user interface. Through user interface, data enrichment service may present a user with recommended transformations. The transformations may be associated with transformation instructions, which may include and/or function calls to perform transformation actions. The transformation instructions may be invoked by a user based on selection at user interface, such as by selecting a recommendation for transformation or by receiving input indicating an operation.).
As per claim 19, Stojanovic teaches a non-transitory computer readable storage medium, including instructions stored thereon which when read and executed by one or more computers cause the one or more computers to perform a method comprising:
providing, at a system including one or more processors, a computational environment, including a user interface, for design and management of software application pipelines (e.g. Stojanovic, see paragraphs [0039, 0043, 0055, and 0089], which discloses prior to loading data into a warehouse the data is processed through a pipeline which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare stage, profile stage, transform stage, and publish stage. A user can specify via a user interface input for identifying data sources from which to obtain input data.),
wherein an application pipeline defines a data flow having a plurality of semantic actions that operate on an input data for preparation as an output data (e.g. Stojanovic, see paragraph [0041], which discloses the data may be processed semantically by a number of processing stages (e.g. semantic pipeline). These processing stages can include preparation stages, enrichment stages, and publishing stages, where the processed data is output data.);
wherein data is received from a data source operating as an input hub and comprising a source dataset (e.g. Stojanovic, see paragraph [0039], which discloses prior to loading data into a data warehouse the data is processed through a pipeline which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare state, profile stage, transform stage, and publish stage.), and provided to a target dataset at a same or other hub, according to the defined data flow (e.g. Stojanovic, see paragraphs [0039, 0043, 0050-0055, and 0089], which discloses when a data source is added, the data source and/or the data stored thereon can be processed through a pipeline prior to loading the data source. The pipeline can include one or more processing engines that are configured to process the data and/or data source before publishing the processed data to one or more data targets. The processing engines can include an ingest engine that extracts raw data from the new data source and provides the raw data to a prepare engine. The prepare engine can identify a format associated with the raw data and can convert the raw data into a format (e.g. normalize the raw data) that can be processed by the data enrichment service. A profile engine can extract and/or generate metadata associated with the normalized data and a transform engine can transform (e.g. repair and/or enrich) the normalized data based on the metadata. The resulting enriched data can be provided to the publish engine to be sent to one or more data targets.); and
providing, for use with the application pipeline, a recommended mapping of semantic actions between the source dataset and the target dataset, based on a profiling of the datasets (e.g. Stojanovic, see paragraphs [0019, 0117, 0055, 0060-0064], which discloses a user can interact with the data enrichment service through user interface. Clients can render graphical user interface to display the user’s data, recommendations for transforming the user’s data, and to send and/or receive instructions (‘transformation instructions’) to the data enrichment service through user interface. The output, or results, of profile engine may be metadata (e.g. source profile) indicating profile information about the data from a source. The metadata may indicate one or more patterns about the data and/or a classification of the data. As further described below, the metadata may include statistical information based on analysis of the data. For example, profile engine can 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.), including:
receiving a request for mapping associated with an application, together with information identifying one or more input hub, dataset, entity, or attribute, to be used in searching to find a potential candidate set for mapping (e.g. Stojanovic discloses a client can submit a data enrichment request to data enrichment service 102 identifies one or more of the data sources 104 (or portions thereof, e.g., particular tables, datasets, etc.). The data enrichment service 102 may then request data to be processed from the identified data sources 104 [0041]. Recommendation engine 308 can request (e.g., query) knowledge service 310 for data that can be recommended to a user for the data obtained for a source [0062]. Transform scripts listed in panel 402 may be automatically applied to the data and reflected in the portion of the data 406 displayed in the interactive user interface 400. For example, the transform scripts listed in patent 402 include renaming columns to be descriptive of their content. Columns 408 shown in interactive user interface 400 have been renamed according to the transform scripts 402 (e.g., column 0003 is now named date time 02, column 0007 is no named “url”, etc.) [0108]);
determining a candidate set of hubs and datasets for mapping, responsive to a search query (e.g. Stojanovic, see paragraphs [0062, 0098, 0091], which discloses the recommendation engine can identify, repair, transform and data enrichment recommendations for the data processed by profile engine. The metadata generated by profile engine can be used to determine recommendations for data based on the statistical analysis and/or classifications indicated by the metadata. Figure 5B, which shows a user interface that can include a profile metric panel. Panel show a summary of metrics associated with the selected data source, as further discloses in paragraph [0117].);
comparing pairs of source and target datasets associated with the candidate set, based on profiling the data therein (e.g. Stojanovic, see paragraphs [0092, 0100, 0103-0105], which discloses knowledge service can implement a matching method to compare the data to reference data available through knowledge service, where the knowledge service can implement a method to determine the semantic similarity between two or more data sets. The scoring formula may determine semantic similarity between two data sets and terms in the domain to obtain from a knowledge source. The domain for which the matching score indicates the best match (e.g. highest matching score) may be chosen as the domain having the greatest similarity with the input data set.); and
providing one or more recommended mappings of hubs and datasets to the user interface, reflective of a stage associated with the design or management of the application pipeline (e.g. Stojanovic, see paragraphs [0108, 0090, 0061-0064], which discloses a as shown in Figure 4A, an example interactive user interface can display transform scripts, recommend transforms and at least a portion of the data being analyzed/transformed. Transform scripts listed in panel can include indicate transforms that have been applied to the data and are visible in panel. Each transform script can be written in a simple declarative language intelligible to a business user. Transform scripts listed in panel may be automatically applied to the data and reflected in the portion of the data displayed in the interactive user interface, as described in paragraph [0108, 0090]. Additionally, in some embodiments, transform engine can present the user with the sampled data for each column, or sample rows from the input dataset through user interface. Through user interface, data enrichment service may present a user with recommended transformations. The transformations may be associated with transformation instructions, which may include and/or function calls to perform transformation actions. The transformation instructions may be invoked by a user based on selection at user interface, such as by selecting a recommendation for transformation or by receiving input indicating an operation.).
As per claims 2 and 11, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, wherein a machine learning model that compares pairs of source and targets and scores a similarity of entities based on extracted features, wherein the feature extraction includes metadata, data type and statistical profiles of sampled data for each attributes (e.g. Stojanovic discloses Knowledge service 310 can perform operations to implement automated data analyses. In some embodiments, knowledge service 310 can use an unsupervised machine learning tool, such as Word2Vec, to analyze an input data set ([0093 and 0092]). The output, or results, of profile engine 326 may be metadata (source profile) indicating profile information about the data from a source. The metadata may indicate one or more patterns about the data and/or a classification of the data. As further described below, the metadata may include statistical information based on analysis of the data. For example, profile engine 326 can 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 [0060]. By computing a similarity metric based on a cosine Similarity, each term in the input data set may be considered as a faction of a whole-value integer, such as a value indicating a percentage of similarity between the term and candidate category. For example, computing a similarity metric between a tire manufacturer and a surname might result in a similarity metric of 0.3, while the similarity metric between a tire manufacturer and a company name might results in a similarity metric of be 0.5. Non-whole integer values representing similarity metrics can be close compared to provide greater accuracy for a closely matching category name. The closely matching category name may be chosen as the most applicable category name based on the similarity metric closest to a value of 1. In the example, above, based on the similarity metric, company name is more likely the correct category. As such, knowledge service 310 can associated “company” instead of “surname” with a user-supplied column of data containing tire manufactures [0102]).
As per claims 3 and 12, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, wherein the request for mapping can include a file specified for the application for which an auto-map service is to be performed, together with information identifying an input hub, a dataset or entity, and one or more attributes, which is used in searching to find a potential candidate set for mapping (e.g. Stojanovic discloses a client can submit a data enrichment request to data enrichment service 102 identifies one or more of the data sources 104 (or portions thereof, e.g., particular tables, datasets, etc.). The data enrichment service 102 may then request data to be processed from the identified data sources 104 [0041]. Recommendation engine 308 can request (e.g., query) knowledge service 310 for data that can be recommended to a user for the data obtained for a source [0062]. Transform scripts listed in panel 402 may be automatically applied to the data and reflected in the portion of the data 406 displayed in the interactive user interface 400. For example, the transform scripts listed in patent 402 include renaming columns to be descriptive of their content. Columns 408 shown in interactive user interface 400 have been renamed according to the transform scripts 402 (e.g., column 0003 is now named date time 02, column 0007 is no named “url”, etc.) [0108]).
As per claims 5 and 14, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, wherein based on a metadata analysis of the data, one or more samples of data are identified, and a machine learning process applied to the sampled data, to determine a category of data in the accessed data, and update a model (e.g. Stojanovic discloses The metadata itself may indicate information about the data. The metadata may be compared to identify similarities and/or to determine a type of the information. The information identified based on the data may be compared to know types of data (e.g., business information, personal identification information, or address information) to identify the data that corresponds to a pattern [0074]. Transform engine 322 can present the user with the sampled data for each column, or sample rows from the input dataset through user interface 306. Through user interface 306, data enrichment service 302 may present a user with recommended transformations [0063]. Profile engine 326 may perform the statistical analysis to disambiguate between patterns identified in the data. For example, data analyzed by profile engine 326, may be evaluated to 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. Each of the set of pattern metrics is computed for a different pattern of the patterns that are identified [0076]).
As per claims 6 and 15, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, further comprising displaying the profile of the datasets in a graphical user interface, for use in creating a dataflow application (e.g. Stojanovic discloses Profiles pane 904 may provide information indicating profile information determined for data ingested by data enrichment service 302. Profiles pane 904 may provide graphical visualizations that indicate a profile of data that has been profiled by data enrichment service 302 [0153]).
As per claims 7 and 16, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, wherein a list of selected semantic actions that are enabled for the accessed data are determined, during the processing of the accessed data (e.g. Stojanovic discloses the metadata produced by profile engine 326 can be provided to the recommendation engine 308 to generate one or more transform recommendations. The entities that match an identified pattern of the data can be used to enrich the data with those entities identified by classification determined using knowledge service 310 ... The information received from knowledge service 310 may include a list (e.g., canonical list) of entities that have properly spelled information (e.g., properly spelled cities and states) for the entities. Entity information corresponding to matching entities obtained from knowledge service 310 can be used to enrich data, e.g., normalize the data, repair the data, and/or augment the data [0085]).
As per claims 8 and 17, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, wherein one or more logistic regression or other models are calculated to represent an overall confidence indicative of a candidate dataset or entity similarly with an input dataset or entity (e.g. Stojanovic discloses knowledge service 310 can implement a method to determine the semantic Similarity between two or more datasets. This may also be used to match the user's data to reference data available through the knowledge service 310. Knowledge service 310 may perform Similarity metric analysis as described in this disclosure ([0092, 0077, 0100, and 0104-0105]).
As per claims 9 and 18, Stojanovic teaches the method of claim 1 and the system of claim 10, respectively, wherein the method is performed in a cloud-based computing environment (e.g. Stojanovic discloses Data targets 330 include a public cloud storage service 332, a private cloud storage service 334, various other cloud services 336, a URL or web-based data target 338, or any other accessible data target ([0090, 0017, and 0039]).
As per claim 20, Stojanovic teaches the method of claim 1, wherein the system operates with a data warehouse to receive data from the data source operating as an input hub (e.g. Stojanovic discloses in certain embodiments of the present invention, prior to loading data into a data warehouse (or other data target) the data is processed through a pipeline (also referred to herein as a semantic pipeline) which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare stage, profile stage, transform stage, and publish stage. During processing, the data can be analyzed, prepared, and enriched. The resulting data can then be published (e.g. provided to a downstream process) into one or more data targets (such as local storage systems, cloud-based storage services, web services, data warehouses, etc.) where various data analytics can be performed on the data [0039]).
As per claim 21, Stojanovic teaches the method of claim 1, wherein the system includes a knowledge source that stores metadata associated with processing data flows associated with input hubs and output hubs (e.g. Stojanovic discloses In certain embodiments of the present invention, prior to loading data into a data warehouse (or other data target) the data is processed through a pipeline (also referred to herein as a semantic pipeline) which includes various processing stages. In some embodiments, the pipeline can include an ingest stage, prepare stage, profile stage, transform stage, and publish stage. During processing, the data can be analyzed, prepared, and enriched. The resulting data can then be published (e.g. provided to a downstream process) into one or more data targets (such as local storage systems, cloud-based storage services, web services, data warehouses, etc.) where various data analytics can be performed on the data [0039]).
As per claim 22, Stojanovic teaches the method of claim 1, further comprising displaying with the user interface the one or more recommended mappings for inclusion within the defined data flow (e.g. Stojanovic, see paragraphs [0039, 0043, 0050-0055, and 0089], which discloses when a data source is added, the data source and/or the data stored thereon can be processed through a pipeline prior to loading the data source. The pipeline can include one or more processing engines that are configured to process the data and/or data source before publishing the processed data to one or more data targets. The processing engines can include an ingest engine that extracts raw data from the new data source and provides the raw data to a prepare engine. The prepare engine can identify a format associated with the raw data and can convert the raw data into a format (e.g. normalize the raw data) that can be processed by the data enrichment service. A profile engine can extract and/or generate metadata associated with the normalized data and a transform engine can transform (e.g. repair and/or enrich) the normalized data based on the metadata. The resulting enriched data can be provided to the publish engine to be sent to one or more data targets.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 March 25, 2026
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