DETAILED ACTION
In response to communication filed on 02 June 2026, claims 1, 4-5, 10, 13, 17 and 19-20 are amended. Claims 6-7, 18, 21-22 and 24-25 are canceled. Claims 26 and 27 are newly added claims. Claims 1-5, 8-17, 19-20, 23 and 26-27 are pending.
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, see “Rejections under 35 U.S.C. §103” filed 30 October 2025, have been carefully considered but are not persuasive. The arguments are related to newly amended limitations and are addressed in the 103 rejection below
Claim Interpretation
Claims 1, 13 and 20 recite “the first decision table uses the correspondence of the data item” and “the second decision table uses the multi-faceted trust score and at least one lineage”. These claim limitations appear to be citing intended use in terms of what the data items, multi-faceted trust score and lineage are used for. Examiner suggests amending the claim to recite the functionality performed by the claimed method, instead of reciting what the claim elements are used for.
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.
Claims 1-5, 8-17, 19-20, 23 and 26-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 2A: Prong One:
Claim 1 recites limitations:
selecting, from a group of data facets for a data item, a plurality of data facets, the selecting of the plurality of data facets comprising a first decision table applying a correspondence of the data item to a table or a column as criteria for selecting the plurality of data facets, wherein the selected data facets include a ratings average and a ratings count;
determining a plurality of parameters corresponding to the plurality of data facets associated with the data item;
assigning a weight to each of the plurality of data facets, wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight;
calculating a multi-faceted trust score for the data item based on the plurality of parameters and the assigned weights, the assigned weights indicating how much each data facet should contribute to the multi-faceted trust score; and
creating a report based on the data item using a second decision table and a policy engine, wherein the second decision table uses the multi-faceted trust score and at least one lineage associated with the data item as factors for determining whether the data item can be used to create the report.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to select from a group of data facets for a data item, a plurality of data facets, the selecting of the plurality of data facets comprising a first decision table applying a correspondence of the data item to a table or a column as criteria for selecting the plurality of data facets, wherein the selected data facets include a ratings average and a ratings count. A human being can evaluate mentally to determine a plurality of parameters corresponding to the plurality of data facets associated with the data item. A human being can perform evaluation to assign weights to each of the data facets based on decision table that uses data item to a table or a column as criteria for assigning weights. A human being can apply evaluation to calculate a multi-faceted trust score for the data item based on the plurality of parameters and the assigned weights, the assigned weights indicating how much each data facet should contribute to the multi-faceted trust score. A human mind can mentally evaluate to create a report based on the data item using a second decision table and a policy engine, wherein the second decision table uses the multi-faceted trust score and at least one lineage associated with the data item as factors for determining whether the data item can be used to create the report using a pen and a paper.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 1 further recites limitations:
A method for applying multi-faceted trust scores in data security, the method comprising:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 1 further recites limitations:
A method for applying multi-faceted trust scores in data security, the method comprising:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Regarding claim 13,
Step 2A: Prong One:
Claim 13 recites limitations:
select, from a group of data facets for a data item, a plurality of data facets, the selecting of the plurality of data facets comprising a first decision table applying a correspondence of the data item to a table or a column as criteria for selecting the plurality of data facets, wherein the selected data facets include a ratings average and a ratings count;
determine a plurality of parameters corresponding to the plurality of data facets associated with the data item;
assign a weight to each of the plurality of data facets, wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight;
calculate a multi-faceted trust score for the data item based on the plurality of parameters and the assigned weights, the assigned weights indicating how much each data facet should contribute to the multi-faceted trust score; and
create a report based on the data item using a second decision table and a policy engine, wherein the second decision table uses the multi-faceted trust score and at least one lineage associated with the data item as factors for determining whether the data item can be used to create the report.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to select from a group of data facets for a data item, a plurality of data facets, the selecting of the plurality of data facets comprising a first decision table applying a correspondence of the data item to a table or a column as criteria for selecting the plurality of data facets, wherein the selected data facets include a ratings average and a ratings count. A human being can evaluate mentally to determine a plurality of parameters corresponding to the plurality of data facets associated with the data item. A human being can perform evaluation to assign weights to each of the data facets based on decision table that uses data item to a table or a column as criteria for assigning weights. A human being can apply evaluation to calculate a multi-faceted trust score for the data item based on the plurality of parameters and the assigned weights, the assigned weights indicating how much each data facet should contribute to the multi-faceted trust score. A human mind can mentally evaluate to create a report based on the data item using a second decision table and a policy engine, wherein the second decision table uses the multi-faceted trust score and at least one lineage associated with the data item as factors for determining whether the data item can be used to create the report using a pen and a paper.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 13 further recites limitations:
A system for applying multi-faceted trust scores in data security, the system comprising: a data collection processor configured to:
a data analyzing processor configured to:
a processor configured to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 13 further recites limitations:
receive data, the data including a plurality of data items;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 13 further recites limitations:
A system for applying multi-faceted trust scores in data security, the system comprising: a data collection processor configured to:
a data analyzing processor configured to:
a processor configured to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 13 further recites limitations:
receive data, the data including a plurality of data items;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 20 incorporates substantively all the limitations of claim 13 in a system form (wherein claim limitations - A system for applying multi-faceted trust scores in data security, the system comprising: in Step 2A: Prong Two as these claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b). These claim limitations in Step 2B appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and is rejected under the same rationale.
Regarding claim 2,
Step 2A: Prong One:
Claim 2 further recites limitations:
further comprising calculating a plurality of multi-faceted trust scores associated with each data facet of the plurality of data facets to obtain the plurality of multi-faceted trust scores for the plurality of data facets, the calculation being based on the plurality of parameters and weights associated with each data facet of the plurality of data facets.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to calculate a plurality of multi-faceted trust scores associated with each data facet of the plurality of data facets to obtain the plurality of multi-faceted trust scores for the plurality of data facets, the calculation being based on the plurality of parameters and weights associated with each data facet of the plurality of data facets.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Claim 15 incorporates substantively all the limitations of claim 2 in a system form and is rejected under the same rationale.
Regarding claim 3,
Step 2A: Prong One:
Claim 3 further recites limitations:
wherein the multi-faceted trust score of the data item is a sum of the plurality of multi-faceted trust scores associated with each data facet of the plurality of data facets.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine the multi-faceted trust score of the data item is a sum of the plurality of multi-faceted trust scores associated with each data facet of the plurality of data facets.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Claim 16 incorporates substantively all the limitations of claim 3 in a system form and is rejected under the same rationale
Regarding claim 4,
Step 2A: Prong One:
Claim 4 further recites limitations:
wherein creating the report is based on the multi-faceted trust score exceeding a predetermined threshold.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to create the report based on the multi-faceted trust score exceeding a predetermined threshold.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Claim 17 incorporates substantively all the limitations of claim 4 in a system form and is rejected under the same rationale.
Regarding claim 5,
Step 2A: Prong One:
Claim 5 further recites limitations:
wherein creating the report is further based on information external to the data item.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to create the report based on external information.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Regarding claim 8,
Step 2A: Prong One:
Claim 8 further recites limitations:
wherein the at least one lineage is based on evaluation of data provenance of the data item.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine one lineage based on evaluation of data provenance of the data.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Regarding claim 9,
Step 2A: Prong One:
Claim 9 further recites limitations:
wherein each of the plurality of data facets includes a characteristic of the data item.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine that each of the data facets include a characteristic of the data item.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Regarding claim 10,
Step 2A: Prong One:
Claim 10 further recites limitations:
wherein the plurality of data facets include at least one of the following: data quality dimensions, criticality of the data item, governance of the data item, an issue, a proximity of the data item to a source, an existence of a data lineage, a fact of scanning the data item from an active source or a spreadsheet, a tag associated with the data item, a frequency of update, a frequency of use, or usefulness of the data item for an intended purpose.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine that each of the data facets include at least one of data quality dimensions, criticality of the data item, governance of the data item, an issue, a proximity of the data item to a source, an existence of a data lineage, a fact of scanning the data item from an active source or a spreadsheet, a tag associated with the data item, a frequency of update, a frequency of use, or usefulness of the data item for an intended purpose.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Claim 19 incorporates substantively all the limitations of claim 10 in a system form and is rejected under the same rationale.
Regarding claim 11,
Step 2A: Prong One:
Claim 11 further recites limitations:
determining that the data item has been changed; and
based on the determination, recalculating the multi-faceted trust score for the data item.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine that data item has changed and recalculating the multi-faceted trust score based on the determination.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Regarding claim 12,
Step 2A: Prong One:
Claim 12 further recites limitations:
calculating a multi-faceted trust score for each of the plurality of data items; and
calculating a data multi-faceted trust score by summarizing the multi-faceted trust score for each of the plurality of data items.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to calculate a multi-faceted trust score for each of the plurality of data items by summarizing the multi-faceted trust score for each of the plurality of data items.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Regarding claim 14,
Step 2A: Prong One:
Claim 14 further recites limitations:
the data including a plurality of data items, the plurality of data items including at least the data item..
These claim limitations appear to be reciting a “Mental Process” including observation.
A human mind can mentally evaluate to observe that data includes plurality of data items and plurality of data items include the data item.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 14 further recites limitations:
further comprising a data collection processor configured to
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 14 further recites limitations:
receive data…
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 14 further recites limitations:
further comprising a data collection processor configured to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 14 further recites limitations:
receive data…
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Regarding claim 23,
Step 2A: Prong One:
Claim 23 further recites limitations:
assigning a weight to the multi-faceted trust score based on the at least one lineage associated with the data item; and
determining a composite trust score based on the multi-faceted trust score and the weight.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to assign a weight to the multi-faceted trust score based on the at least one lineage associated with the data item; and determining a composite trust score based on the multi-faceted trust score and the weight.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Regarding claim 26,
Step 2A: Prong One:
Claim 26 further recites limitations:
determining a confidence level that the data item contains personally identifying information based on the multi-faceted trust score and the at least one lineage associated with the data item.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine a confidence level that the data item contains personally identifying information based on the multi-faceted trust score and the at least one lineage associated with the data item.
There are no other claim limitations that can be integrated into a practical application or amount to significantly more.
Claim 27 incorporates substantively all the limitations of claim 26 in a system form and is rejected under the same rationale
Claim Rejections - 35 USC § 103
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 1-2, 4-5, 8-10, 12-15, 17 and 19-20, 23 and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2010/0106560 A1, hereinafter “Li”) in view of Wang et al. (US 10,140,666 B1, hereinafter “Wang”) further in view of Stibel et al. (US 2012/0260209 A1, hereinafter “Stibel”).
Regarding claim 1, Li teaches
A method for (see Li, [0041] “illustrate that the methods described herein can be performed on a wide variety of information handling systems which operate in a networked environment”) applying multi-faceted trust scores in data security, (see Li, [0422] “showing factors included in security trust factors… Within each of these types of security factors, a number of example security factors are provided to provide context and detail for the type of security factor... Certification of the Network as Secure, and Certification of the Hardware as Secure”; [claim 4] “computing the atomic trust scores, wherein one or more of the atomic trust scores are term related scores, one or more of the atomic trust scores are data profiling scores, one or more of the atomic trust scores are data lineage scores, and one or more of the atomic trust scores are security scores”) the method comprising: (see Li, [0041] “illustrate that the methods described herein can be performed on a wide variety of information handling systems which operate in a networked environment”).
selecting, from a group of data facets for a data item, a plurality of data facets, the selecting of the plurality of data facets comprising… (see Li, [0451] “the associated fact(s) and/or metadata that correspond to the selected event are selected and the current values of these facts/metadata are retrieved… the retrieved facts/metadata is/are compared with the thresholds retrieved from the selected event from data store 1925”; [0454] “facts and other metadata that correspond to the metadata selected by the user are retrieved (e.g., the metadata/facts that were used to compute a composite metadata score, etc.)”; [0423] “metadata is gathered regarding the selected scope item (e.g., type of scope item (database table, flat file, etc.), location of the scope item, access method(s) used to access the scope item, etc.). This gathered data is stored in selected scope items data store 1120”) a correspondence of the data item to a table or a column as criteria for selecting the plurality of data facets, wherein the selected data facets… (see Li, [0439] “to gather column-based metadata… a first column of data is selected from fact data 1620. Fact data 1620 includes selected scope items 1120 and selected facts 1525. So, for example, at step 1610, a particular column could be selected from a database table that was included in the selected scope items and identified as a selected fact. At step 1620, the first column-based trust factor that applies to the selected column of data is selected from column-based trust factors 1630 that were included in selected atomic trust factors 1150”).
determining a plurality of parameters (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0429] “the first threshold that is needed to calculate the selected atomic metadata is selected from available thresholds data store 1170… elects the threshold(s) most appropriate for the organization… A determination is made as to whether additional thresholds are needed to calculate the selected atomic metadata” – thresholds are interpreted as parameters) corresponding to the plurality of data facets associated with the data item; (see Li, [0423] “Organizational data 1110 includes the data stores maintained or available to an organization (e.g., databases, flat files, tables, etc.). For example, a customer table might be identified in step 1105. At step 1115, metadata is gathered regarding the selected scope item (e.g., type of scope item (database table, flat file, etc.), location of the scope item, access method(s) used to access the scope item, etc.). This gathered data is stored in selected scope items data store 1120” – metadata has been interpreted as data facets).
assigning a weight to each of the plurality of data facets,… (see Li, [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) the correspondence of the data item to a table or a column as criteria for assigning each weight; (see Li, [0439] “to gather column-based metadata… a first column of data is selected from fact data 1620. Fact data 1620 includes selected scope items 1120 and selected facts 1525. So, for example, at step 1610, a particular column could be selected from a database table that was included in the selected scope items and identified as a selected fact. At step 1620, the first column-based trust factor that applies to the selected column of data is selected from column-based trust factors 1630 that were included in selected atomic trust factors 1150”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”).
calculating a multi-faceted trust score for the data item (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) based on the plurality of parameters and the assigned weights, (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) the assigned weights indicating how much each data facet should contribute to the multi-faceted trust score; and (see Li, [0437] “atomic trust factor data is gathered based on the selected fact and the selected atomic trust factor. This data includes the name of the atomic trust factor, the score/value (or algorithm), and the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata (data store 1560)”; [0057] “a weighting factor might be applied so that if a particular atomic factor is not resolved or is resolved poorly, the factor is still used in the aggregation hierarchy. One example could be multiplying a particular atomic trust factor by a weighting factor so that the lack of reliability (trust) in the atomic trust factor does not overly reduce the resulting composite score 570. However, when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed”).
creating a report based on the data item… (see Li, [0049] “Definition, configuration, and collection of facts: determine what input data---called facts-are needed to calculate the trust factors (e.g. data quality analysis result/report, etc .), possibly adjust existing definitions and specify how to collect them (e.g. from existing metadata repositories, etc.)”; [0450] “Examples of actions would include notifying a user of the system with an email message, running a particular process or report, etc”) and a policy engine, (see Li, [0420] “data entered as provided or also validated against rules”) the multi-faceted trust score (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) and at least one lineage associated with the data item as factors… (see Li, [0057] “The various atomic trust factors (factors 510 to 540) are fed into aggregation hierarchy process 550 which generates composite scores using one or more of the atomic trust factors as inputs (e.g., term related factors, data profiling factors, data lineage factors, and security factors, etc.)”) the data item (see Li, [0451] “the associated fact(s) and/or metadata that correspond to the selected event are selected and the current values of these facts/metadata are retrieved”).
Li does not explicitly teach selecting of the plurality of data facets comprising a first decision table applying a correspondence of the data item to a table or a column, include a ratings average and a ratings count; wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight; using a second decision table, wherein the second table uses the multi-faceted trust score for determining whether the data item can be used to create the report.
However, Wang discloses decision tables and teaches
a first decision table applying data analysis for each topic or sub-topic (see Wang, [col 17 lines 16-46] “uses the decision tables 30 to analyze the run time data 62… Each decision table 30 created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic… The TLA 60 identifies a decision table 30 corresponding to one of the non-complete topics or sub-topics and, using the rule engine 64, identifies one or more non-binding suggestions 66 to present to the UI control 80. The non-binding suggestions 66 may include a listing of compilation of one or more questions (e.g., Q.sub.1-Q.sub.5 as seen in FIG. 7) from the decision table 30… the listing or compilation of questions may be ranked in order by rank… those questions that resolve data fields associated with low confidence values”; [col 9 lines 29-30] “the decision table 30 is used to select a question or questions”).
wherein the first decision table uses data and their respective weights (see Wang, [col 17 lines 27-39] “The TLA 60 identifies a decision table 30 corresponding to one of the non-complete topics or sub-topics and, using the rule engine 64, identifies one or more non-binding suggestions 66 to present… The ranking or listing may be weighted in order of importance, relevancy, confidence level, or the like. For example, a top ranked question may be a question that… a decision will most likely lead to a path to completion”).
using a second decision table… wherein the second decision table uses for each topic (see Wang, [col 16 lines 16-17] “uses the decision tables 30 to analyze the run time data 62 and determine whether… Each decision table 30 created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic” – there are plurality of decision tables).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of decision tables being disclosed and taught by Wang, in the system taught by Li to yield the predictable results of effectively analyzing data based on decision table (see Wang, [col 17 lines 16-21] “uses the decision tables 30 to analyze the run time data 62 and determine whether a tax return is complete. Each decision table 30 created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic”).
The proposed combination of Li and Wang does not explicitly teach include a ratings average and a ratings count; for determining whether the data item can be used to create the report.
However, Stibel discloses presenting data in a report and teaches
include a ratings average and a ratings count; (see Stibel, [0187] “An overall rating displays the average rating from the aggregated ratings, a first count specifies the number of ratings used to derive the reviews sentiment, a second count specifies the number of data sources from which the ratings were aggregated, and a chart displays the distribution of the ratings”).
for determining whether data can be used to create the report (see Stibel, [0113] “incorporate different pattern matching criteria to identify which quantitative measures or which credibility data to filter based on what conditions. Each scoring filter may be specific to one or more types of credibility data... the scoring filters are selectively applied to the credibility data based on the type of credibility data”; [0117] “the process directly passes the filtered quantitative measures to the credibility scoring aggregator 640 of the reporting engine 230”; [0091] “The reporting engine 230 includes data analyzer 610, natural language processing (NLP) engine 620, scoring engine 625, scoring filters 630, credibility scoring aggregator 640, and report generator 650”; [0123] “the report generator 650 is tasked with (1) producing reports that detail how the credibility of different entities is derived” – data is filtered and passed on to the reporting engine and reporting engine includes a report generator that can produce reports).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of ratings average, ratings count, determine whether data can be used to create the report and summarizing being disclosed and taught by Stibel, in the system taught by the proposed combination of Li and Wang to yield the predictable results of effectively analyzing data to present it in a report (see Stibel, [0093] “The data analyzer 610 interfaces with the database 220 in order to obtain aggregated credibility data for one or more entities. As noted above, credibility data for a particular entity is stored to the database 220 using a unique identifier. Accordingly, the data analyzer 610 is provided with one or a list of unique identifiers for which credibility scores and reports are to be generated. The list of unique identifiers may be provided by a system administrator or may be generated on-the-fly based on requests that are submitted through the interface portal. The data analyzer 610 uses the unique identifiers to retrieve the associated data from the database 220”).
Regarding claim 13, Li teaches
A system for (see Li, [0035] “information handling system 100 which is a simplified example of a computer system capable of performing the computing operations described herein”) applying multi-faceted trust scores in data security, (see Li, [0422] “showing factors included in security trust factors… Within each of these types of security factors, a number of example security factors are provided to provide context and detail for the type of security factor... Certification of the Network as Secure, and Certification of the Hardware as Secure”; [claim 4] “computing the atomic trust scores, wherein one or more of the atomic trust scores are term related scores, one or more of the atomic trust scores are data profiling scores, one or more of the atomic trust scores are data lineage scores, and one or more of the atomic trust scores are security scores”) the system comprising: (see Li, [0035] “information handling system 100 which is a simplified example of a computer system capable of performing the computing operations described herein”).
a data collection processor configured to: (see Li, [0035] “Information handling system 100 includes one or more processors 110 which are coupled to processor interface bus 112” – there are plurality of processors).
receive data, the data including a plurality of data items; (see Li, [0423] “Organizational data 1110 includes the data stores maintained or available to an organization (e.g., databases, flat files, tables, etc.). For example, a customer table might be identified in step 1105”).
a data analyzing processor configured to: (see Li, [0035] “Information handling system 100 includes one or more processors 110 which are coupled to processor interface bus 112” – there are plurality of processors).
select, from a group of data facets for a data item of the plurality of data items, a plurality of data facets, the selecting of the plurality of data facets comprising… (see Li, [0451] “the associated fact(s) and/or metadata that correspond to the selected event are selected and the current values of these facts/metadata are retrieved… the retrieved facts/metadata is/are compared with the thresholds retrieved from the selected event from data store 1925”; [0454] “facts and other metadata that correspond to the metadata selected by the user are retrieved (e.g., the metadata/facts that were used to compute a composite metadata score, etc.)”; [0423] “metadata is gathered regarding the selected scope item (e.g., type of scope item (database table, flat file, etc.), location of the scope item, access method(s) used to access the scope item, etc.). This gathered data is stored in selected scope items data store 1120”) a correspondence of the data item to a table or a column as criteria for selecting the plurality of data facets, wherein the selected data facets include… (see Li, [0439] “to gather column-based metadata… a first column of data is selected from fact data 1620. Fact data 1620 includes selected scope items 1120 and selected facts 1525. So, for example, at step 1610, a particular column could be selected from a database table that was included in the selected scope items and identified as a selected fact. At step 1620, the first column-based trust factor that applies to the selected column of data is selected from column-based trust factors 1630 that were included in selected atomic trust factors 1150”).
determine a plurality of parameters (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0429] “the first threshold that is needed to calculate the selected atomic metadata is selected from available thresholds data store 1170… elects the threshold(s) most appropriate for the organization… A determination is made as to whether additional thresholds are needed to calculate the selected atomic metadata” – thresholds are interpreted as parameters) corresponding to the plurality of data facets associated with the data item; (see Li, [0423] “Organizational data 1110 includes the data stores maintained or available to an organization (e.g., databases, flat files, tables, etc.). For example, a customer table might be identified in step 1105. At step 1115, metadata is gathered regarding the selected scope item (e.g., type of scope item (database table, flat file, etc.), location of the scope item, access method(s) used to access the scope item, etc.). This gathered data is stored in selected scope items data store 1120” – metadata has been interpreted as data facets).
assign a weight to each of the plurality of data facets,… (see Li, [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) the correspondence of the data item to a table or a column as criteria for assigning each weight; and (see Li, [0439] “to gather column-based metadata… a first column of data is selected from fact data 1620. Fact data 1620 includes selected scope items 1120 and selected facts 1525. So, for example, at step 1610, a particular column could be selected from a database table that was included in the selected scope items and identified as a selected fact. At step 1620, the first column-based trust factor that applies to the selected column of data is selected from column-based trust factors 1630 that were included in selected atomic trust factors 1150”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”).
calculate at least one multi-faceted trust score for the data item based on the plurality of parameters and the assigned weights, (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) the assigned weights indicating how much each data facet should contribute to the multi-faceted trust score; and (see Li, [0437] “atomic trust factor data is gathered based on the selected fact and the selected atomic trust factor. This data includes the name of the atomic trust factor, the score/value (or algorithm), and the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata (data store 1560)”; [0057] “a weighting factor might be applied so that if a particular atomic factor is not resolved or is resolved poorly, the factor is still used in the aggregation hierarchy. One example could be multiplying a particular atomic trust factor by a weighting factor so that the lack of reliability (trust) in the atomic trust factor does not overly reduce the resulting composite score 570. However, when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed”).
a processor configured to: (see Li, [0035] “Information handling system 100 includes one or more processors 110 which are coupled to processor interface bus 112” – there are plurality of processors).
create a report based on the data item… (see Li, [0049] “Definition, configuration, and collection of facts: determine what input data---called facts-are needed to calculate the trust factors (e.g. data quality analysis result/report, etc .), possibly adjust existing definitions and specify how to collect them (e.g. from existing metadata repositories, etc.)”; [0450] “Examples of actions would include notifying a user of the system with an email message, running a particular process or report, etc”) and a policy engine,… (see Li, [0420] “data entered as provided or also validated against rules”) the multi-faceted trust score (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) and at least one lineage associated with the data item as factors… (see Li, [0057] “The various atomic trust factors (factors 510 to 540) are fed into aggregation hierarchy process 550 which generates composite scores using one or more of the atomic trust factors as inputs (e.g., term related factors, data profiling factors, data lineage factors, and security factors, etc.)”) the data item (see Li, [0451] “the associated fact(s) and/or metadata that correspond to the selected event are selected and the current values of these facts/metadata are retrieved”).
Li does not explicitly teach selecting of the plurality of data facets comprising a first decision table applying a correspondence of the data item to a table or a column, include a ratings average and a ratings count; wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight; using a second decision table, wherein the second table uses the multi-faceted trust score for determining whether the data item can be used to create the report.
However, Wang discloses decision tables and teaches
a first decision table applying data analysis for each topic or sub-topic (see Wang, [col 17 lines 16-46] “uses the decision tables 30 to analyze the run time data 62… Each decision table 30 created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic… The TLA 60 identifies a decision table 30 corresponding to one of the non-complete topics or sub-topics and, using the rule engine 64, identifies one or more non-binding suggestions 66 to present to the UI control 80. The non-binding suggestions 66 may include a listing of compilation of one or more questions (e.g., Q.sub.1-Q.sub.5 as seen in FIG. 7) from the decision table 30… the listing or compilation of questions may be ranked in order by rank… those questions that resolve data fields associated with low confidence values”; [col 9 lines 29-30] “the decision table 30 is used to select a question or questions”).
wherein the first decision table uses data and their respective weights (see Wang, [col 17 lines 27-39] “The TLA 60 identifies a decision table 30 corresponding to one of the non-complete topics or sub-topics and, using the rule engine 64, identifies one or more non-binding suggestions 66 to present… The ranking or listing may be weighted in order of importance, relevancy, confidence level, or the like. For example, a top ranked question may be a question that… a decision will most likely lead to a path to completion”).
using a second decision table… wherein the second decision table uses each topic (see Wang, [col 16 lines 16-17] “uses the decision tables 30 to analyze the run time data 62 and determine whether… Each decision table 30 created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic” – there are plurality of decision tables).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of decision tables being disclosed and taught by Wang, in the system taught by Li to yield the predictable results of effectively analyzing data based on decision table (see Wang, [col 17 lines 16-21] “uses the decision tables 30 to analyze the run time data 62 and determine whether a tax return is complete. Each decision table 30 created for each topic or sub-topic is scanned or otherwise analyzed to determine completeness for each particular topic or sub-topic”).
The proposed combination of Li and Wang does not explicitly teach include a ratings average and a ratings count; for determining whether the data item can be used to create the report.
However, Stibel discloses presenting data in a report and teaches
include a ratings average and a ratings count; (see Stibel, [0187] “An overall rating displays the average rating from the aggregated ratings, a first count specifies the number of ratings used to derive the reviews sentiment, a second count specifies the number of data sources from which the ratings were aggregated, and a chart displays the distribution of the ratings”).
for determining whether data can be used to create the report (see Stibel, [0113] “incorporate different pattern matching criteria to identify which quantitative measures or which credibility data to filter based on what conditions. Each scoring filter may be specific to one or more types of credibility data... the scoring filters are selectively applied to the credibility data based on the type of credibility data”; [0117] “the process directly passes the filtered quantitative measures to the credibility scoring aggregator 640 of the reporting engine 230”; [0091] “The reporting engine 230 includes data analyzer 610, natural language processing (NLP) engine 620, scoring engine 625, scoring filters 630, credibility scoring aggregator 640, and report generator 650”; [0123] “the report generator 650 is tasked with (1) producing reports that detail how the credibility of different entities is derived” – data is filtered and passed on to the reporting engine and reporting engine includes a report generator that can produce reports).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of ratings average, ratings count, determine whether data can be used to create the report and summarizing being disclosed and taught by Stibel, in the system taught by the proposed combination of Li and Wang to yield the predictable results of effectively analyzing data to present it in a report (see Stibel, [0093] “The data analyzer 610 interfaces with the database 220 in order to obtain aggregated credibility data for one or more entities. As noted above, credibility data for a particular entity is stored to the database 220 using a unique identifier. Accordingly, the data analyzer 610 is provided with one or a list of unique identifiers for which credibility scores and reports are to be generated. The list of unique identifiers may be provided by a system administrator or may be generated on-the-fly based on requests that are submitted through the interface portal. The data analyzer 610 uses the unique identifiers to retrieve the associated data from the database 220”).
Claim 20 incorporates substantively all the limitations of claim 13 in a system form (see Li, [0035] “information handling system 100 which is a simplified example of a computer system capable of performing the computing operations… Information handling system 100 includes one or more processors 110 which are coupled to processor interface bus 112” – there are plurality of processors) and is rejected under the same rationale.
Regarding claim 2, the proposed combination of Li, Wang and Stibel teaches
further comprising calculating a plurality of multi-faceted trust scores associated with each data facet of the plurality of data facets to obtain the plurality of multi-faceted trust scores for the plurality of data facets, the calculation being based on the plurality of parameters and weights associated with each data facet of the plurality of data facets (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata” – there are plurality of atomic trust scores being stored; [0005] “computing atomic trust scores using a atomic trust factors that are applied to a plurality of metadata. A first set of composite trust scores are computed using some of the atomic trust scores. The composite trust scores are computed using a first set of algorithms. Some of the algorithms use a factor weighting value as input to the algorithm”).
Claim 15 incorporates substantively all the limitations of claim 2 in a system form and is rejected under the same rationale.
Regarding claim 4, the proposed combination of Li, Wang and Stibel teaches
wherein creating the report is based on (see Li, [0049] “Definition, configuration, and collection of facts: determine what input data---called facts-are needed to calculate the trust factors (e.g. data quality analysis result/report, etc .), possibly adjust existing definitions and specify how to collect them (e.g. from existing metadata repositories, etc.)”; [0450] “Examples of actions would include notifying a user of the system with an email message, running a particular process or report, etc”) the multi-faceted trust score (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) exceeding a predetermined threshold (see Wang, [col 22 lines 63-65] “When a data field exhibits statistical deviation beyond a threshold level”; [col 39 lines 17-19] “determined to have a probability of being relevant to the user greater than a predetermined threshold”). The motivation for proposed combination is maintained.
Claim 17 incorporates substantively all the limitations of claim 4 in a system form and is rejected under the same rationale.
Regarding claim 5, the proposed combination of Li, Wang and Stibel teaches
wherein creating the report is further based on (see Li, [0049] “Definition, configuration, and collection of facts: determine what input data---called facts-are needed to calculate the trust factors (e.g. data quality analysis result/report, etc .), possibly adjust existing definitions and specify how to collect them (e.g. from existing metadata repositories, etc.)”; [0450] “Examples of actions would include notifying a user of the system with an email message, running a particular process or report, etc”) information external to the data item (see Wang, [col 19 lines 11-12] “may access external interaction configuration files”). The motivation for proposed combination is maintained.
Regarding claim 8, the proposed combination of Li, Wang and Stibel teaches
wherein the at least one lineage is based on evaluation of data provenance of the data item (see Li, [0048] “the provenance (also referred to as information lineage) of the information (i.e. who, what, how, when, where the information is being collected and processed from the very beginning”; [0419] “Three subsets of data lineage trust factors are shown: Identification of Data Origination and Associated Trust 910, Data Capture trust factors 920, and Lineage Path trust factors 930. Six examples of trust factors are shown within Identification of Data Origination and Associated Trust 910. These include actor (911)-who provides the data: is it the user or a 3rd party, etc”).
Regarding claim 9, the proposed combination of Li, Wang and Stibel teaches
wherein each of the plurality of data facets includes a characteristic of the data item (see Li, [0158]-[0159] “Date Types Validity… Indicates the confidence in the validity of the defined data type of a column based on the inferred data types from the column analysis”).
Regarding claim 10, the proposed combination of Li, Wang and Stibel teaches
wherein the plurality of data facets include at least one of the following:… (see Li, [0451] “the associated fact(s) and/or metadata that correspond to the selected event are selected and the current values of these facts/metadata are retrieved… the retrieved facts/metadata is/are compared with the thresholds retrieved from the selected event from data store 1925”; [0454] “facts and other metadata that correspond to the metadata selected by the user are retrieved (e.g., the metadata/facts that were used to compute a composite metadata score, etc.)”; [0423] “metadata is gathered regarding the selected scope item (e.g., type of scope item (database table, flat file, etc.), location of the scope item, access method(s) used to access the scope item, etc.). This gathered data is stored in selected scope items data store 1120”) an existence of a data lineage, (see Li, [0421] “Eight examples are included within Lineage Path trust factors 930… control of the data along the lineage path (938, governance and security of the data, etc. also related to security factors”).
Claim 19 incorporates substantively all the limitations of claim 10 in a system form and is rejected under the same rationale.
Regarding claim 12, the proposed combination of Li, Wang and Stibel teaches
further comprising receiving data, the data including a plurality of data items, the plurality of data items including at least the data item; (see Li, [0423] “Organizational data 1110 includes the data stores maintained or available to an organization (e.g., databases, flat files, tables, etc.). For example, a customer table might be identified in step 1105”).
calculating a multi-faceted trust score for each of the plurality of data items; and (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”).
calculating a data multi-faceted trust score (see Li, [0057] “The various atomic trust factors (factors 510 to 540) are fed into aggregation hierarchy process 550 which generates composite scores using one or more of the atomic trust factors as inputs (e.g., term related factors, data profiling factors, data lineage factors, and security factors, etc.)” – plurality of scores are aggregated) by summarizing the information (see Stibel, [0153] “These scores 1540 summarize the credibility of an entity”) the multi-faceted trust score (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) for each of the plurality of data items (see Li, [0444]-[0446] “trust index repository includes several data stores including selected facts data store 1525 (facts identified for the organization)… a first item is selected from trust index repository 320… includes a number of data stores with each data store including a number of items”).
Regarding claim 14, the proposed combination of Li, Wang and Stibel teaches
further comprising a data collection processor configured to (see Li, [0035] “Information handling system 100 includes one or more processors 110 which are coupled to processor interface bus 112” – there are plurality of processors) receive data, the data including a plurality of data items, the plurality of data items including at least the data item (see Li, [0423] “Organizational data 1110 includes the data stores maintained or available to an organization (e.g., databases, flat files, tables, etc.). For example, a customer table might be identified in step 1105”).
Regarding claim 23, the proposed combination of Li, Wang and Stibel teaches
further comprising: assigning a weight to the multi-faceted trust score based on the at least one lineage associated with the data item; and determining a composite trust score based on the multi-faceted trust score and the weight (see Li, [0057] “The various atomic trust factors (factors 510 to 540) are fed into aggregation hierarchy process 550 which generates composite scores using one or more of the atomic trust factors as inputs (e.g., term related factors, data profiling factors, data lineage factors, and security factors, etc.). In addition, weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… could be multiplying a particular atomic trust factor by a weighting factor so that the lack of reliability (trust) in the atomic trust factor does not overly reduce the resulting composite score 570. However, when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed”).
Regarding claim 26, the proposed combination of Li, Wang and Stibel teaches
further comprising determining a confidence level that the data item contains (see Li, [0159] “Description: Indicates the confidence in the validity of the defined data type of a column based on the inferred data types from the column analysis”) personally identifying information (see Wang, [col 28 lines 45-46] “collects the relevant financial and/or personal data for the user”) based on the multi-faceted trust score (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) and the at least one lineage associated with the data item (see Li, [0419] “Three subsets of data lineage trust factors are shown: Identification of Data Origination and Associated Trust 910, Data Capture trust factors 920, and Lineage Path trust factors 930. Six examples of trust factors are shown within Identification of Data Origination and Associated Trust 910. These include actor (911)-who provides the data: is it the user or a 3rd party, etc”). The motivation for the proposed combination is maintained.
Claim 27 incorporates substantively all the limitations of claim 26 in a system form and is rejected under the same rationale.
Claims 3, 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Li, Wang and Stibel further in view of Eshwar et al. (US 2018/0060370 A1, hereinafter “Eshwar”).
Regarding claim 3, the proposed combination of Li, Wang and Stibel teaches
wherein the multi-faceted trust score of the data item… (see Li, [0424] “Predefined process 1160 uses available algorithms from available algorithms data store 1165 and available thresholds from available thresholds data store 1170 as inputs and results with calculated atomic trust scores stored in calculated atomic metadata 1175”; [0057]-[0058] “weighting factors 560 can be applied to the composite factor and/or one or more of the underlying atomic trust factors… when the data is more settled, this weighting factor can be changed in order to highlight the issue regarding the particular atomic trust factor so that the underlying trustworthiness of the data is addressed… course-grained weighting, would be "high" (H), "medium" (M), "low" (L), and not applicable (NIA) with corresponding fine-grained weighting being 7 to 9 for "high," 4 to 6 for "medium," 1 to 3 for "low," and 0 for "not applicable”. A high weighting for a trust factor would imply that the associated trust factor (and its score) are of highest importance in the project… A weighing of "not applicable" (NIA or '0') implies that the trust factor (and the associated score) do not apply to the specific project”; [0437] “the priority (or weighting) to apply to the atomic trust factor. The gathered data is stored in atomic trust factors metadata”) the plurality of multi-faceted trust scores associated with (see Li, [0059] “one or more composite and/or atomic trust scores 570… to create additional levels of composite trust scores. A composite score is calculated from a set of elementary scores. The elementary scores can either be atomic, i.e. calculated from facts, or they can be composite scores themselves. Weights can be specified for scores which can be used by algorithms to calculate composite scores… Those trust factors could be aggregated into a single column score for a particular column. All the column scores for a particular table could then be aggregated into a table score for that table. And then all the table scores of a database could be aggregated into the score for the database) each data facet of the plurality of data facets (see Li, [0423] “Organizational data 1110 includes the data stores maintained or available to an organization (e.g., databases, flat files, tables, etc.). For example, a customer table might be identified in step 1105. At step 1115, metadata is gathered regarding the selected scope item (e.g., type of scope item (database table, flat file, etc.), location of the scope item, access method(s) used to access the scope item, etc.). This gathered data is stored in selected scope items data store 1120” – metadata has been interpreted as data facets).
The proposed combination of Li, Wang and Stibel does not explicitly teach the multi-faceted trust score is a sum of the plurality of multi-faceted trust scores.
However, Eshwar discloses scores and also teaches
trust factor is a sum of other scores (see Eshwar, [0029] “the trust factor may be the sum of those other scores”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of sum of scores, thresholds and recalculating scores as being disclosed and taught by Eshwar, in the system taught by the proposed combination of Li, Wang and Stibel to yield the predictable results of efficiently measuring trust in order to determine trust factor (see Eshwar, [0043] “Technical effects and benefits of some embodiments include the ability to measure trust and thereby determine a trust factor that effectively reflects the reliability of data in non-relational or relational databases 190. Specifically, in some embodiments, the trust system 100 may calculate a trust factor that is based on the age, lineage, and completeness of data, which are characteristics that may be known regardless of whether the data is stored in a relational database 190”).
Claim 16 incorporates substantively all the limitations of claim 3 in a system form and is rejected under the same rationale.
Regarding claim 11, the proposed combination of Li, Wang and Stibel teaches
further comprising: determining that the data item has been changed; and (see Li, [0045] “Security 350 is used to ensure that individuals and processes access data that is applicable to the particular Information Consumer as established by Administration process 340. In addition, security maintains audit trails to establish which user or process updated any particular facts, algorithms, or trust factors”).
based on the determination, to establish which processes are updated (see Li, [0045] “Security 350 is used to ensure that individuals and processes access data that is applicable to the particular Information Consumer as established by Administration process 340. In addition, security maintains audit trails to establish which user or process updated any particular facts, algorithms, or trust factors”).
The proposed combination of Li, Wang and Stibel does not explicitly teach recalculating the at least one multi-faceted trust score for the data item.
However, Eshwar discloses calculating scores and also teaches
recalculating the at least one multi-faceted trust score for the data item (see Eshwar, [0030] “the trust factor or one or more scores on which the trust factor is based may be updated by recalculation when data in a record is changed… may detect an update to a row and, responsive to that update, may recalculate the one or more scores and the trust factor”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of recalculating scores as being disclosed and taught by Eshwar, in the system taught by the proposed combination of Li, Wang and Stibel to yield the predictable results of efficiently measuring trust in order to determine trust factor (see Eshwar, [0043] “Technical effects and benefits of some embodiments include the ability to measure trust and thereby determine a trust factor that effectively reflects the reliability of data in non-relational or relational databases 190. Specifically, in some embodiments, the trust system 100 may calculate a trust factor that is based on the age, lineage, and completeness of data, which are characteristics that may be known regardless of whether the data is stored in a relational database 190”).
Citation of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Publication No. US US-20100205550 A1 (Chen et al.) teaches average rating over time and count of ratings specific to products, retailer, etc. It also obtains a variety of statistical data associated with the user generated content, including an importance metric. This importance metric may be utilized to rank the products of the manufacturer such that statistical, or other, data related to the manufacturer's products may be presented to a user in the order of product importance.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VAISHALI SHAH/Primary Examiner, Art Unit 2156
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