DETAILED ACTION
In response to communication filed on 30 October 2025, claims 1, 13 and 20 are amended. Claims 6-7, 18 and 21-22 are canceled. Claims 24 and 25 are new claims. Claims 1-5, 8-17, 19-20 and 23-25 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 October 2025 has been entered.
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”. These claim limitations appear to be citing intended use in terms of what the data items 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 Objections
Claims 1, 13 and 20 are objected to because of the following informalities:
Claims 1, 13 and 20 recite “wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight” should read as -- wherein the first decision table uses the correspondence of the data item to the table or the column as the criteria for assigning each weight-- as it appears to be a typographical error and may cause antecedent basis issue.
Appropriate corrections are required.
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, 5, 8-10, 13-15 and 19-20 and 23 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 van Wijk et al. (US 2009/0054091 A1, hereinafter “Van”).
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; (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; (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”).
combining the multi-faceted trust score of the data item with at least one lineage associated with the data item; and (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).
selectively taking a predetermined action based on the multi-faceted trust score, the predetermined action including (see Li, Fig. 14; [0435] “events and actions that are based on facts and metadata (both atomic and composite) are established… When a trust data item reaches the threshold, a predefined action takes place, such as notifying an information consumer… analyze the underlying facts and metadata corresponding to a particular trust score”; [0450] “steps taken to establish events and actions that are based on facts and metadata… the event is associated with other trust index repository objects, such as a composite metadata score stored in data store 1185”) integrating the multi-faceted trust score into a data store… (Li, [0424] “results with calculated atomic trust scores stored in calculated atomic metadata 1175”) and further including one or more of notifying at least one user, (see Li, Fig. 14; [0435] “events and actions that are based on facts and metadata (both atomic and composite) are established… When a trust data item reaches the threshold, a predefined action takes place, such as notifying an information consumer”).
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; wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight; the predetermined action including integrating the multi-faceted trust score into a second decision table.
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”).
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 the predetermined action including integrating the multi-faceted trust score into a second decision table.
However, Van discloses event decision table and teaches
a second decision table that includes an action and a score (see van, [0129] “accesses a decision table to identify an action to take…. the decision table includes an association between a score and an action… the decision table identifies an action--such as sending a notification of received event data to a user--to take based upon a particular score… the decision table identifies an action--such as sending a notification of received event data to a user--to take based upon a pattern of scores… the decision table may indicate that if the initial score was 1 and the delta scores include a -1 followed by a 1, then the notification service 404 should provide a notification to the user of the identification of the received event data associated with the delta value of 1”).
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 table as being disclosed and taught by Van, in the system taught by the proposed combination of Li and Wang to yield the predictable results of effectively making decisions based on identifying appropriate actions (see Van, [0118] “the notification service 404 accesses an event correlation table to identify a score assigned to a type of event data for use in ranking a plurality of identifications of received event data. In still another embodiment, the notification service 404 accesses an event decision table to identify an action to take responsive to a score assigned to one or more identifications of received event data”).
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; (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; (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; (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”).
combine the at least one multi-faceted trust score of the data item with at least one lineage associated with the data item; and (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).
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).
selectively take a predetermined action based on the at least one multi-faceted trust score, the predetermined action including (see Li, Fig. 14; [0435] “events and actions that are based on facts and metadata (both atomic and composite) are established… When a trust data item reaches the threshold, a predefined action takes place, such as notifying an information consumer… analyze the underlying facts and metadata corresponding to a particular trust score”; [0450] “steps taken to establish events and actions that are based on facts and metadata… the event is associated with other trust index repository objects, such as a composite metadata score stored in data store 1185”) integrating the at least one multi-faceted trust score into a data store… (Li, [0424] “results with calculated atomic trust scores stored in calculated atomic metadata 1175”) and further including one or more of notifying at least one user, (see Li, Fig. 14; [0435] “events and actions that are based on facts and metadata (both atomic and composite) are established… When a trust data item reaches the threshold, a predefined action takes place, such as notifying an information consumer”).
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; wherein the first decision table uses the correspondence of the data item to a table or a column as criteria for assigning each weight; the predetermined action including integrating the at least one multi-faceted trust score into a second decision table.
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”).
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 the predetermined action including integrating the at least one multi-faceted trust score into a second decision table.
However, Van discloses event decision table and teaches
a second decision table that includes an action and a score (see van, [0129] “accesses a decision table to identify an action to take…. the decision table includes an association between a score and an action… the decision table identifies an action--such as sending a notification of received event data to a user--to take based upon a particular score… the decision table identifies an action--such as sending a notification of received event data to a user--to take based upon a pattern of scores… the decision table may indicate that if the initial score was 1 and the delta scores include a -1 followed by a 1, then the notification service 404 should provide a notification to the user of the identification of the received event data associated with the delta value of 1”).
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 table as being disclosed and taught by Van, in the system taught by the proposed combination of Li and Wang to yield the predictable results of effectively making decisions based on identifying appropriate actions (see Van, [0118] “the notification service 404 accesses an event correlation table to identify a score assigned to a type of event data for use in ranking a plurality of identifications of received event data. In still another embodiment, the notification service 404 accesses an event decision table to identify an action to take responsive to a score assigned to one or more identifications of received event data”).
Claims 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 Van 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 5, the proposed combination of Li, Wang and Van teaches
wherein the selectively taking the predetermined action is further based on (see Li, Fig. 14; [0435] “events and actions that are based on facts and metadata (both atomic and composite) are established… When a trust data item reaches the threshold, a predefined action takes place, such as notifying an information consumer… analyze the underlying facts and metadata corresponding to a particular trust score”; [0450] “steps taken to establish events and actions that are based on facts and metadata… the event is associated with other trust index repository objects, such as a composite metadata score stored in data store 1185”) information external to the data item (see Li, [0435] “Information consumers can analyze trust data using a "drill-down" approach provided by predefined process 1460 (see FIG. 20 and corresponding text for processing details). This drill-down approach allows the user to analyze the underlying facts and metadata corresponding to a particular trust score. Using the drill down approach provided in predefined process 1460, the user can better understand why a trust index is a particular value and therefore make a better determination as to whether the facts and metadata can be trusted” – provides assistance with analyzing additional information such as facts and metadata).
Regarding claim 8, the proposed combination of Li, Wang and Van 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 Van 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 Van teaches
wherein the plurality of data facets include one of the following: (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) a null value, and (see Li, [0237] “number of null values in the column”).
Claim 19 incorporates substantively all the limitations of claim 10 in a system form and is rejected under the same rationale.
Regarding claim 14, the proposed combination of Li, Wang and Van 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 Van 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”).
Claims 3-4, 11 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Li, Wang and Van further in view of Eshwar et al. (US 2018/0060370 A1, hereinafter “Eshwar”).
Regarding claim 3, the proposed combination of Li, Wang and Van 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 Van 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 Van 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 4, the proposed combination of Li, Wang and Van teaches
wherein the predetermined action further includes one or more following: (see Li, Fig. 14; [0435] “events and actions that are based on facts and metadata (both atomic and composite) are established… When a trust data item reaches the threshold, a predefined action takes place, such as notifying an information consumer… analyze the underlying facts and metadata corresponding to a particular trust score”; [0450] “steps taken to establish events and actions that are based on facts and metadata… the event is associated with other trust index repository objects, such as a composite metadata score stored in data store 1185”).
determining that 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”).
The proposed combination of Li, Wang and Van does not explicitly teach the multi-faceted trust score is lower than the predetermined threshold.
However, Eshwar discloses thresholds and also teaches
records where trust factor is lower than the predetermined