Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This action is in response to application amendments filed on 9-22-2025.
2. Claims 1 - 13, 15 - 20 are pending. Claims 1, 15 have been amended. Claim 14 has been canceled. Claims 1, 15 are independent. This application was filed on 12-24-2022.
Response to Arguments
3. Applicant's arguments have been fully considered, however upon further consideration of the prior art and the claimed limitation, they were not persuasive.
A. Applicant argues on page 9 of Remarks: ... Zang does not disclose "selecting a subset of the suppressed dataset from the suppressed dataset, one or more data records in the selected subset of the suppressed dataset each has a corresponding anonymity value lower than the k-value" ... .
The Examiner respectfully disagrees. Zang discloses determining an anonymity parameter value. And, then Zang discloses select data (portion of data) that falls below a threshold parameter. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.; col 5: User data filter module 208 may then abstract the user mobility data that falls below an anonymity threshold by changing the resolution of location data and/or shortening the time frame for each anonymous data set, among other ways to abstract the data. User interface module 209 interacts with users to obtain user data and to transfer anonymity factors and recommendations, as well as receive selections and/or other input from the user.; (update anonymous data and then select a set of data); (anonymity below a threshold))
B. Applicant argues on page 9 of Remarks: ... "in response to checking the anonymity value of each data record of a plurality of data records in the suppressed dataset, selecting a subset of the suppressed dataset from the suppressed dataset, one or more data records in the selected subset of the suppressed dataset each has a corresponding anonymity value lower than the k-value" ... .
The Examiner respectfully disagrees. Zang discloses determining an anonymity parameter value. And, then Zang discloses select data (portion of data) that falls below a threshold parameter. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.; col 5: User data filter module 208 may then abstract the user mobility data that falls below an anonymity threshold by changing the resolution of location data and/or shortening the time frame for each anonymous data set, among other ways to abstract the data. User interface module 209 interacts with users to obtain user data and to transfer anonymity factors and recommendations, as well as receive selections and/or other input from the user.; (update anonymous data and then select a set of data); (anonymity below a threshold))
C. Applicant argues on page 9 of Remarks: ... Independent claim 15 recites similar features as claim 1 and Applicant respectfully submits that claim 15 is patentable for at least the foregoing reasons ...
Responses to arguments against independent claim 1 also answer arguments against independent claim 15, which has similar limitations as independent claim 1.
D. Applicant argues on page 9 of Remarks: ... Claims 1-13 and 16-20 each adds additional features to its respective base claim and Applicant respectfully submits that these claims are allowable for at least the foregoing reasons, ... .
Responses to arguments against the independent claims also answer arguments against the associated dependent claims.
Claim Rejections - 35 USC § 103
4. 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.
5. Claims 1 - 6, 11 - 13, 15 - 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mian et al. (US PGPUB No. 20220129485) in view of Zang et al. (US Patent No. 8,639,221).
Regarding Claim 1, Mian discloses a method for k-anonymization, the method comprising:
a) receiving an input dataset; (see Mian paragraph [0041]: the user device 102 may be an electronic device that may enable the user to receive and/or transmit data associated with the intermediary mapping and de-identification system 100. According to embodiments of the present invention, the user device 102 may be, but not limited to, a mobile device, a smart phone, a tablet computer, a portable computer, a laptop computer, a desktop computer, a smart device, a smart watch, a smart glass, a Personal Digital Assistant (PDA), paragraph [0039]: an intermediary mapping and de-identification system 100, according to an embodiment of the present invention. The intermediary mapping and de-identification system 100 may be configured for de-identification of one or more datasets using an intermediary mapping technique, according to embodiments of the present invention. According to embodiments of the present invention, the datasets may be, but not limited to, one or more non-standard datasets, one or more standard datasets, one or more synthetic datasets, partial or subset datasets, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the datasets that may require de-identification.)
b) receiving a k-value, the k-value being a positive integer; c) receiving one or more quasi-identifiers corresponding to one or more data fields in the input dataset; (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
d) receiving a data suppression strategy including one or more transformation steps, at least one transformation step of the one or more transformation steps associated with at least one quasi-identifier of one or more quasi-identifiers; (see Mian paragraph [0019]: correlations between fields are used to inform how to apply a de-identification strategy for the de-identification of the full dataset. The application of a de-identification may be performed manually or using custom scripting. In an embodiment of the present invention, correlations are akin to groupings of variables, which serve a dual purpose; in a more accurate disclosure-risk calculation, groupings may manifest as measurement groups, and in a more refined, automated de-identification process, groupings may serve the role of propagation de-id groups.; paragraph [0051]: the schema mapping module 204 may be configured to retrieve a schema mapping of the incoming dataset using a table name, a metadata, an introspection, or other sources and/or means, in an embodiment of the present invention. Furthermore, the schema mapping module 204 may be configured to interpret the schema mapping of the incoming dataset using the table name, the metadata, the introspection, or other sources and/or means, in an embodiment of the present invention.; the schema mapping module 204 may be configured to use a rules-based approach to perform the schema mapping based on a retrieved non-standard dataset and/or a synthetic dataset.)
e) applying a first transformation step of the one or more transformation steps to at least one data field of the one or more data fields in the input dataset to generate a suppressed dataset including at least one suppressed data field corresponding to the at least one data field; (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.)
h) applying a second transformation step of the one or more transformation steps to at least the subset of the suppressed dataset to generate an output, the second transformation step being different from the first transformation step; (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.) and
i) wherein the method is performed using one or more processors. (see Mian paragraph [0046]: The mapping platform 116 may be one or more computer readable instructions that may be stored onto the database 114 and configured to control operations of the mapping application 112 installed on the user device 102 when executed by the central processor 118.)
Mian does not specifically disclose for f) checking an anonymity value of each data record, and for g) in response to checking anonymity value of each record of a plurality of data records, one or more data records in the selected dataset each has a corresponding anonymity value.
However, Zang discloses:
f) checking an anonymity value of each data record of a plurality of data records in the suppressed dataset; g) in response to checking the anonymity value of each record of a plurality of data records in the suppressed data, selecting a subset of the suppressed dataset from the suppressed dataset, one or more data records in the selected subset of the suppressed dataset each has a corresponding anonymity value lower than the k-value; (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.; col 5: User data filter module 208 may then abstract the user mobility data that falls below an anonymity threshold by changing the resolution of location data and/or shortening the time frame for each anonymous data set, among other ways to abstract the data. User interface module 209 interacts with users to obtain user data and to transfer anonymity factors and recommendations, as well as receive selections and/or other input from the user.; (update anonymous data and then select a set of data); (anonymity below a threshold))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for f) checking an anonymity value of each data record, and for g) in response to checking anonymity value of each record of a plurality of data records, one or more data records in the selected dataset each has a corresponding anonymity value as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 2, Mian-Zang discloses the method of claim 1.
Mian does not specifically disclose checking an anonymity value of the dataset comprises determining a record anonymity value for each data record in the dataset.
However, Zang discloses wherein the checking an anonymity value of the suppressed dataset comprises determining a record anonymity value for each data record of a plurality of data records in the suppressed dataset. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for checking an anonymity value of the dataset comprises determining a record anonymity value for each data record in the dataset. as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 3 Mian-Zang discloses the method of claim 1, wherein the anonymity value is a first anonymity value and the suppressed dataset is a first suppressed dataset, wherein the applying a second transformation step comprises generating a second suppressed dataset by applying the second transformation step to at least the subset of the first suppressed dataset, wherein the method further comprises:
c) applying a third transformation step of the one or more transformation steps to at least the subset of the second suppressed dataset to generate the output, the third transformation step being different from the second transformation step, the third transformation step being different from the first transformation step. (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.)
Mian does not specifically disclose for a) checking a second anonymity value of the second suppressed dataset, and for b) selecting one or more data records in the selected subset of the dataset, each has a corresponding anonymity value.
However, Zang discloses:
a) checking a second anonymity value of the second suppressed dataset; b) selecting a subset of the second suppressed dataset from the second suppressed dataset, one or more data records in the selected subset of the second suppressed dataset each has a corresponding anonymity value lower than the k-value. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for a) checking a second anonymity value of the second suppressed dataset, and for b) selecting one or more data records in the selected subset of the dataset, each has a corresponding anonymity value as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 4, Mian-Zang discloses the method of claim 1, wherein the first transformation step applies to a first quasi-identifier and the second transformation step applies to a second quasi-identifier, wherein the first quasi-identifier is different from the second quasi-identifier. (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
Regarding Claim 5, Mian-Zang discloses the method of claim 1, wherein the one or more transformation steps includes at least one selected from a group consisting of masking, bucketing, and replacing. (see Mian paragraph [0072]: the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth. Further, the mapping may be used to ensure the de-identification that may further ensure that a synthesized information is correctly imputed and performed at, but not limited to, a cell-level, a column-level, a greater level, and so forth.)
Regarding Claim 6, Mian-Zang discloses the method of claim 1, wherein the receiving a data suppression strategy comprises:
a) presenting the one or more quasi-identifiers on a user interface; (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.; paragraph [0042]: The user interface 108 may be configured to enable the user to input data into the intermediary mapping and de-identification system 100, ... . The data may be the datasets; The user interface 108 may be further configured to display an output data associated with the intermediary mapping and de-identification system 100, according to an embodiment of the present invention. Further, the user interface 108 may be, but is not limited to, a digital display, a touch screen display, a graphical user interface)
b) receiving one or more suppression inputs associated with the one or more quasi- identifiers; (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
c) compiling the one or more transformation steps based on the one or more data suppression inputs and the one or more quasi-identifier; (see Mian paragraph [0019]: correlations between fields are used to inform how to apply a de-identification strategy for the de-identification of the full dataset. The application of a de-identification may be performed manually or using custom scripting. In an embodiment of the present invention, correlations are akin to groupings of variables, which serve a dual purpose; in a more accurate disclosure-risk calculation, groupings may manifest as measurement groups, and in a more refined, automated de-identification process, groupings may serve the role of propagation de-id groups.; paragraph [0051]: the schema mapping module 204 may be configured to retrieve a schema mapping of the incoming dataset using a table name, a metadata, an introspection, or other sources and/or means, in an embodiment of the present invention. Furthermore, the schema mapping module 204 may be configured to interpret the schema mapping of the incoming dataset using the table name, the metadata, the introspection, or other sources and/or means, in an embodiment of the present invention.; the schema mapping module 204 may be configured to use a rules-based approach to perform the schema mapping based on a retrieved non-standard dataset and/or a synthetic dataset.) and
d) generating the data suppression strategy using the one or more transformation steps. (see Mian paragraph [0019]: correlations between fields are used to inform how to apply a de-identification strategy for the de-identification of the full dataset. The application of a de-identification may be performed manually or using custom scripting. In an embodiment of the present invention, correlations are akin to groupings of variables, which serve a dual purpose; in a more accurate disclosure-risk calculation, groupings may manifest as measurement groups, and in a more refined, automated de-identification process, groupings may serve the role of propagation de-id groups.; paragraph [0051]: the schema mapping module 204 may be configured to retrieve a schema mapping of the incoming dataset using a table name, a metadata, an introspection, or other sources and/or means, in an embodiment of the present invention. Furthermore, the schema mapping module 204 may be configured to interpret the schema mapping of the incoming dataset using the table name, the metadata, the introspection, or other sources and/or means, in an embodiment of the present invention.; the schema mapping module 204 may be configured to use a rules-based approach to perform the schema mapping based on a retrieved non-standard dataset and/or a synthetic dataset.)
Regarding Claim 11, Mian-Zang discloses the method of claim 1, wherein the data suppression strategy includes a process of bucketing to group data into a plurality of first buckets associated with a first bucket size, wherein the method further comprises: modifying the data suppression strategy by changing the process of bucketing to group data into a plurality of second buckets associated with a second bucket size smaller than the first bucket size. (see Mian paragraph [0061]: The ruleset engine 384 may access the generated variable mapping and further infer the variable mapping with, but not limited to, the appropriate variable classification (such as, a type of identifier), the one or more groupings, the one or more disclosure risk settings, and the one or more de-identification settings, and so forth. Classifications, connections/groupings, and/or de-identification settings for mapped variables can be inferred 386. Further, the ruleset engine 384 may be configured to retrieve 388 the one or more variable classification, the one or more groupings, the one or more disclosure risk settings, the one or more de-identification settings, and so forth from a rules storage 390.; (bucket analogous to a grouping; groupings of data based upon variable such as a bucket and its size))
Regarding Claim 12, Mian-Zang discloses the method of claim 1, wherein the data suppression strategy is a first data expression strategy, wherein the method further comprises:
a) determining a first suppression metric associated with the first data expression strategy; b) modifying a parameter associated with one transformation step of the one or more transformation steps of the first data suppression strategy to generate a second data suppression strategy; c) determining a second suppression metric associated with the second data expression strategy; (see Mian paragraph [0061]: The ruleset engine 384 may access the generated variable mapping and further infer the variable mapping with, but not limited to, the appropriate variable classification (such as, a type of identifier), the one or more groupings, the one or more disclosure risk settings, and the one or more de-identification settings, and so forth. Classifications, connections/groupings, and/or de-identification settings for mapped variables can be inferred 386. Further, the ruleset engine 384 may be configured to retrieve 388 the one or more variable classification, the one or more groupings, the one or more disclosure risk settings, the one or more de-identification settings, and so forth from a rules storage 390.; (groupings of data based upon variable such as a bucket and its size)) and
d) selecting a data suppression strategy from the first data suppression strategy and the second data suppression strategy based on the first suppression metric and the second suppression metric. (see Mian paragraph [0019]: correlations between fields are used to inform how to apply a de-identification strategy for the de-identification of the full dataset. The application of a de-identification may be performed manually or using custom scripting. In an embodiment of the present invention, correlations are akin to groupings of variables, which serve a dual purpose; in a more accurate disclosure-risk calculation, groupings may manifest as measurement groups, and in a more refined, automated de-identification process, groupings may serve the role of propagation de-id groups.; paragraph [0051]: the schema mapping module 204 may be configured to retrieve a schema mapping of the incoming dataset using a table name, a metadata, an introspection, or other sources and/or means, in an embodiment of the present invention. Furthermore, the schema mapping module 204 may be configured to interpret the schema mapping of the incoming dataset using the table name, the metadata, the introspection, or other sources and/or means, in an embodiment of the present invention.; the schema mapping module 204 may be configured to use a rules-based approach to perform the schema mapping based on a retrieved non-standard dataset and/or a synthetic dataset.)
Regarding Claim 13, Mian-Zang discloses the method of claim 1, wherein the output includes an output dataset, wherein the output dataset includes data from the input dataset and data from the suppressed dataset. (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.)
Regarding Claim 15, Mian discloses a system for k-anonymization, the system comprising: one or more memories comprising instructions stored thereon; and
one or more processors configured to execute the instructions and perform operations (Mian paragraph [0046]: The mapping platform 116 may be one or more computer readable instructions that may be stored onto the database 114 and configured to control operations of the mapping application 112 installed on the user device 102 when executed by the central processor 118.) comprising:
a) receiving an input dataset; (see Mian paragraph [0041]: the user device 102 may be an electronic device that may enable the user to receive and/or transmit data associated with the intermediary mapping and de-identification system 100. According to embodiments of the present invention, the user device 102 may be, but not limited to, a mobile device, a smart phone, a tablet computer, a portable computer, a laptop computer, a desktop computer, a smart device, a smart watch, a smart glass, a Personal Digital Assistant (PDA),)
b) receiving a k-value, the k-value being a positive integer; c) receiving one or more quasi-identifiers corresponding to one or more data fields in the input dataset; (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
d) receiving a data suppression strategy including one or more transformation steps, at least one transformation step of the one or more transformation steps associated with at least one quasi-identifier of one or more one or more quasi-identifiers; (see Mian paragraph [0019]: correlations between fields are used to inform how to apply a de-identification strategy for the de-identification of the full dataset. The application of a de-identification may be performed manually or using custom scripting. In an embodiment of the present invention, correlations are akin to groupings of variables, which serve a dual purpose; in a more accurate disclosure-risk calculation, groupings may manifest as measurement groups, and in a more refined, automated de-identification process, groupings may serve the role of propagation de-id groups.; paragraph [0051]: the schema mapping module 204 may be configured to retrieve a schema mapping of the incoming dataset using a table name, a metadata, an introspection, or other sources and/or means, in an embodiment of the present invention. Furthermore, the schema mapping module 204 may be configured to interpret the schema mapping of the incoming dataset using the table name, the metadata, the introspection, or other sources and/or means, in an embodiment of the present invention.; the schema mapping module 204 may be configured to use a rules-based approach to perform the schema mapping based on a retrieved non-standard dataset and/or a synthetic dataset.) and
e) applying a first transformation step of the one or more transformation steps to at least one data field of the one or more data fields in the input dataset to generate a suppressed dataset including at least one suppressed data field corresponding to the at least one data field; (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.) and
h) applying a second transformation step of the one or more transformation steps to at least the subset of the suppressed dataset to generate an output, the second transformation step being different from the first transformation step. (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.)
Mian does not specifically disclose for f) checking an anonymity value of each data record in dataset; g) in response to checking anonymity value of each record of a plurality of data records, selecting one or more data records in the dataset each has a corresponding anonymity value.
However, Zang discloses:
f) checking an anonymity value of each data record of a plurality of data records in the suppressed dataset; g) in response to checking the anonymity value of each record of a plurality of data records in the suppressed data, selecting a subset of the suppressed dataset from the suppressed dataset, one or more data records in the selected subset of the suppressed dataset each has a corresponding anonymity value lower than the k-value. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.; col 5: User data filter module 208 may then abstract the user mobility data that falls below an anonymity threshold by changing the resolution of location data and/or shortening the time frame for each anonymous data set, among other ways to abstract the data. User interface module 209 interacts with users to obtain user data and to transfer anonymity factors and recommendations, as well as receive selections and/or other input from the user.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for f) checking an anonymity value of each data record in dataset; g) in response to checking anonymity value of each record of a plurality of data records, selecting one or more data records in the dataset each has a corresponding anonymity value as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 16, Mian-Zang discloses the system of claim 15.
Mian does not specifically disclose checking an anonymity value of dataset comprises determining a record anonymity value for each data record.
However, Zang discloses wherein the checking an anonymity value of the suppressed dataset comprises determining a record anonymity value for each data record of a plurality of data records in the suppressed dataset. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for checking an anonymity value of dataset comprises determining a record anonymity value for each data record as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 17, Mian-Zang discloses the system of claim 15, wherein the anonymity value is a first anonymity value and the suppressed dataset is a first suppressed dataset, wherein the applying a second transformation step comprises generating a second suppressed dataset by applying the second transformation step to at least the subset of the first suppressed dataset, wherein the operations further comprise:
c) applying a third transformation step of the one or more transformation steps to at least the subset of the second suppressed dataset to generate the output, the third transformation step being different from the second transformation step, the third transformation step being different from the first transformation step. (see Mian paragraph [0009]: this workflow, as shown in the FIG. 1B, also requires specialized ETL processes to ingest data for disclosure risk estimation, and post-processing to ensure the derived de-identification strategy (including de-identification transformations or replacement through data synthesis) is fully applied to the entire non-standard dataset.; paragraph [0072]: the intermediary mapping and de-identification system may perform a de-identification and a de-identification propagation using data such as, but not limited to, the fully mapped dataset, the mapped one or more metadata, the inferred one or more variable classifications, the inferred one or more variable connections, the inferred one or more groupings, the inferred one or more disclosure risk settings, the inferred one or more de-identification settings, and so forth. Further, the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth.)
Mian does not specifically disclose for a) checking an anonymity value of the dataset; b) selecting a subset of a dataset from the dataset, one or more data records in the dataset has a corresponding anonymity value.
However, Zang discloses:
a) checking a second anonymity value of the second suppressed dataset; b) selecting a subset of the second suppressed dataset from the second suppressed dataset, one or more data records in the selected subset of the second suppressed dataset each has a corresponding anonymity value lower than the k-value; (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for a) checking an anonymity value of the dataset; b) selecting a subset of a dataset from the dataset, one or more data records in the dataset has a corresponding anonymity value as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 18, Mian-Zang discloses the system of claim 15, wherein the first transformation step applies to a first quasi-identifier and the second transformation step applies to a second quasi-identifier, wherein the first quasi-identifier is different from the second quasi-identifier. (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
Regarding Claim 19, Mian-Zang discloses the system of claim 15, wherein the one or more transformation steps includes at least one selected from a group consisting of masking, bucketing, and replacing. (see Mian paragraph [0072]: the de-identification may be, but not limited to, a data transformation, a data masking, a cell-based and/or column-based suppression, a data synthesis, and so forth. Further, the mapping may be used to ensure the de-identification that may further ensure that a synthesized information is correctly imputed and performed at, but not limited to, a cell-level, a column-level, a greater level, and so forth.)
6. Claims 7 - 10, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mian in view of Zang and further in view of Shlens et al. (US PGPUB No. 20210097348).
Regarding Claim 7, Mian-Zang discloses the method of claim 6.
Mian does not specifically disclose at least one suppression input includes a selection of a transformation type and a value associated with the selected transformation type.
However, Shlens discloses wherein at least one suppression input of the one or more suppression inputs includes a selection of a transformation type and a value associated with the selected transformation type. (see Shlens paragraph [0083]: Each training input can each be processed with a different sequence of transformation operations, but each sequence of transformation operations has the same number of transformation operations, and each transformation operation in all of the sequences has a magnitude that is determined according to the same magnitude schedule. The procedure for transforming a given training input can include randomly selecting a transformation operation for each position in the sequence, and then transforming the training input using the selected sequence of transformation operations in the order defined by the sequence.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for at least one suppression input includes a selection of a transformation type and a value associated with the selected transformation type as taught by Shlens. One of ordinary skill in the art would have been motivated to employ the teachings of Shlens for the benefits achieved from the flexibility of a system that enables processing transformation steps in a selected order. (see Shlens paragraph [0083])
Regarding Claim 8, Mian-Zang discloses the method of claim 1.
Mian does not specifically disclose data suppression strategy includes an order of the transformation steps, wherein a first transformation step is applied before a second transformation step according to the order.
However, Shlens discloses wherein the data suppression strategy includes an order of the one or more transformation steps, wherein a first transformation step of the one or more transformation steps is applied before a second transformation step of the one or more transformation steps according to the order. (see Shlens paragraph [0083]: Each training input can each be processed with a different sequence of transformation operations, but each sequence of transformation operations has the same number of transformation operations, and each transformation operation in all of the sequences has a magnitude that is determined according to the same magnitude schedule. The procedure for transforming a given training input can include randomly selecting a transformation operation for each position in the sequence, and then transforming the training input using the selected sequence of transformation operations in the order defined by the sequence.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for data suppression strategy includes an order of the transformation steps, wherein a first transformation step is applied before a second transformation step according to the order as taught by Shlens. One of ordinary skill in the art would have been motivated to employ the teachings of Shlens for the benefits achieved from the flexibility of a system that enables processing transformation steps in a selected order. (see Shlens paragraph [0083])
Regarding Claim 9, Mian-Zang-Shlens discloses the method of claim 8, wherein the data suppression strategy is applied to a first subset of the one or more quasi-identifiers.
Mian does not specifically disclose for a) modifying the data suppression strategy by changing the order of the transformation steps; b) wherein the first transformation step is applied after the second transformation step.
However, Shlens discloses further comprises:
a) modifying the data suppression strategy by changing the order of the one or more transformation steps; b) wherein the first transformation step of the one or more transformation steps is applied after the second transformation step. (see Shlens paragraph [0083]: Each training input can each be processed with a different sequence of transformation operations, but each sequence of transformation operations has the same number of transformation operations, and each transformation operation in all of the sequences has a magnitude that is determined according to the same magnitude schedule. The procedure for transforming a given training input can include randomly selecting a transformation operation for each position in the sequence, and then transforming the training input using the selected sequence of transformation operations in the order defined by the sequence.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for a) modifying the data suppression strategy by changing the order of the transformation steps; b) wherein the first transformation step is applied after the second transformation step as taught by Shlens. One of ordinary skill in the art would have been motivated to employ the teachings of Shlens for the benefits achieved from the flexibility of a system that enables processing transformation steps in a selected order. (see Shlens paragraph [0083])
Regarding Claim 10, Mian-Zang-Shlens discloses the method of claim 9.
b) wherein the second subset of the one or more quasi-identifiers includes a second number of quasi-identifiers less than a first number of quasi-identifiers in the first subset of the one or more quasi-identifiers. (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
Mian does not specifically disclose for a) wherein the modified data suppression strategy is applied to a second subset of quasi-identifiers to generate a second dataset such that the dataset has an anonymity value.
However, Zang discloses for a) wherein the modified data suppression strategy is applied to a second subset of the one or more quasi-identifiers to generate a second suppressed dataset such that the second suppressed dataset has an anonymity value not lower than the k-value. (see Zang col 1, lines 46-60: Systems, methods, and software for identifying the anonymity of data, anonymizing the data, and providing a user with information about the data of the user, are provided herein. In one example, a method of operating a communication system is provided. The method includes exchanging wireless communications with a plurality of wireless communication devices operated by a plurality of mobile users, determining mobility data for the plurality of mobile users based at least in part on the exchange of the wireless communications, for at least one mobile user of the plurality of mobile users, determining an anonymity factor from the mobility data indicative of a difficulty of identifying the one mobile user from at least the mobility data, and modifying at least a portion of the mobility data to effect a change in the anonymity factor.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for a) wherein the modified data suppression strategy is applied to a second subset of quasi-identifiers to generate a second dataset such that the dataset has an anonymity value as taught by Zang. One of ordinary skill in the art would have been motivated to employ the teachings of Zang for the benefits achieved from the utilization of multiple parameters such as anonymity values in the processing of data in a network environment. (see Zang col 1, lines 46-60)
Regarding Claim 20, Mian-Zang discloses the system of claim 15, wherein the receiving a data suppression strategy comprises:
presenting the one or more quasi-identifiers on a user interface; (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.; paragraph [0042]: The user interface 108 may be configured to enable the user to input data into the intermediary mapping and de-identification system 100, ... . The data may be the datasets; The user interface 108 may be further configured to display an output data associated with the intermediary mapping and de-identification system 100, according to an embodiment of the present invention. Further, the user interface 108 may be, but is not limited to, a digital display, a touch screen display, a graphical user interface)
b) receiving one or more suppression inputs associated with the one or more quasi- identifiers; (see Mian paragraph [0008]: de-identification solutions currently allow generic characterization of datasets and elements of the datasets. An example is that available de-identification software solutions currently allow a user to associate variables in the data to very generic variable types, such as public quasi-identifier or direct identifier. ... This can be akin to a data catalog process whereby an exhaustive list of variables and variable settings are stored for future retrieval; if an incoming data variable matches particulars of a variable already existing in the catalog, it is handled appropriately.)
Mian does not specifically disclose for c) compiling the transformation steps based on the one or more data inputs and the quasi-identifier; and for d) generating the data suppression strategy using transformation steps.
However, Shlens discloses:
c) compiling the one or more transformation steps based on the one or more data suppression inputs and the one or more quasi-identifier; and d) generating the data suppression strategy using the one or more transformation steps. (see Shlens paragraph [0083]: Each training input can each be processed with a different sequence of transformation operations, but each sequence of transformation operations has the same number of transformation operations, and each transformation operation in all of the sequences has a magnitude that is determined according to the same magnitude schedule. The procedure for transforming a given training input can include randomly selecting a transformation operation for each position in the sequence, and then transforming the training input using the selected sequence of transformation operations in the order defined by the sequence.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mian for c) compiling the transformation steps based on the one or more data inputs and the quasi-identifier; and for d) generating the data suppression strategy using transformation steps as taught by Shlens. One of ordinary skill in the art would have been motivated to employ the teachings of Shlens for the benefits achieved from the flexibility of a system that enables processing transformation steps in a selected order. (see Shlens paragraph [0083])
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CJ/
December 29, 2025
/SHEWAYE GELAGAY/Supervisory Patent Examiner, Art Unit 2436