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
The action is in response to claims dated 5/26/2026.
Claims pending in the case: 1-13
Claims allowed: 13
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michaud (US 20230362091) in view of Guevara (US 20200150305) and Mathews (US 20180176197) in view of Jones (US 20150105878).
Jones not used in the prior office action.
Regarding Claim 1, Michaud teaches, A multi-layered data map in a data mesh, comprising:
a data mesh (Michaud: [37, 48]: data mesh);
a computer processor located in the data mesh (Michaud: Fig. 2 [33, 48]: data mesh process include processor);
one or more data storage units in electronic communication with the data mesh (Michaud: Fig. 1B, [33, 40]: data mesh architecture with storage units),
wherein one or more sets of data are stored in the one or more data storage units (Michaud: Fig. 1B, [25, 47, 51]: data mesh architecture with distributed storage units);
one or more user devices in electronic communication with the data mesh (Michaud: Fig. 1B, [25, 54]: data mesh architecture with end users);
… ;
wherein the computer processor is configured to implement one or more machine learning systems (Michaud: [33-35]: “data mesh process 248 may utilize machine learning” data mesh with machine learning models) to:
identify a content of the one or more sets of data distributed across the one or more data storage units (Michaud: [33]: “the model M can be used very easily to classify new data points” – identify content for the mesh);
for each set of data of the one or more sets of data, … a level of sensitivity, …(Michaud: [61]: “a data source for compliance with a data privacy policy or data sovereignty policy”; [80]: “data security role”);
identify one or more locations where the one or more sets of data are distributed across the one or more data storage units (Michaud: [52, 63, 71]: content locations; [78]: “mapping that indicates the physical locations of the data”);
identify one or more points of interest within the one or more sets of data, wherein the one or more points of interest are one or more pieces of data and have been identified by the one or more machine learning systems to have a higher probability of recognition by an accessor than data in the one or more data sets other than the one or more points of interest (Michaud: [78-79]: identify based on a query relevant data of interest (points of interest));
…;
…. a functional view and a storage view, the one or more machine learning systems being configured to present the functional view to the accessor when the one or more credentials identify the accessor as a consumer and to present the storage view to the accessor when the one or more credentials identify the accessor as a back-end operator (Michaud: Fig. 6, [64, 74-75]: tailor view based on user roles – based on user data (credentials));
in response to a request for data which meets pre-determined criteria from an accessor using a user device who presents one or more credentials (Michaud: [10, 61, 78-79]: user query for data as per user role), consult with the data map to:
identify data from the one or more sets of data distributed across the one or more data storage units which meet the criteria for the request for data (Michaud: [78-79]: identify user query data);
…;
identify one or more locations associated with the identified data which are permitted to be shared with the accessor (Michaud: [78-79]: locate data as per user query; [52]: use “mapping of data topics/data types and location information for each of those topics/data types”); and
identify one or more points of interest associated with the identified data which are permitted to be shared with the accessor (Michaud: [63-64, 71, 78-79]: identify content of interest based on query); and
provide the accessor with the identified data which are permitted to be shared with the accessor, the one or more locations associated with the data, and the one or more points of interest associated with the data (Michaud: [81-83]: the information related to the data of interest is provided which are aggregated for presentation), filtered according to the view of the multi-layered data map corresponding to the one or more credentials of the accessor (Michaud: Fig. 6, [64, 74-75]: tailor view (filtered) based on user roles);
However, Michaud does not specifically teach,
a data map in electronic communication with the data mesh;
wherein the data map is a multi-layered data map;
for each set of data of the one or more sets of data, determine a level of sensitivity, wherein to access a set of data with a pre-determined level of sensitivity, one or more credentials are required;
identify one or more points of interest within the one or more sets of data, wherein the one or more points of interest are one or more pieces of data and have been identified by the one or more machine learning systems to have a higher probability of recognition by an accessor than data in the one or more data sets other than the one or more points of interest;
populate the data map with the content of the one or more sets of data, the one or more locations of the one or more sets of data, the level of sensitivity associated with each of the one or more sets of data, and one or more points of interest within the one or more sets of data;
consult with the data map to: identify data
determine which of the identified data are at a level of sensitivity permitted to be shared with the accessor based on the presented one or more credentials;
Guevara teaches,
a data map in electronic communication with the data mesh (Guevara: Fig. 1 [21, 23]: generate a data map);
wherein the data map is a multi-layered data map (Guevara: Fig. 1 [21, 23]: generate a data map – data map is inherently multilayer);
identify one or more points of interest within the one or more sets of data, wherein the one or more points of interest are one or more pieces of data and have been identified by the one or more machine learning systems to have a higher probability of recognition by an accessor than data in the one or more data sets other than the one or more points of interest (Guevara: Fig. 1 [21-23]: identify specific data and data attributes to generate a relevant (probability of recognition for a purpose) data map);
populate the data map with the content of the one or more sets of data, the one or more locations of the one or more sets of data, the level of sensitivity associated with each of the one or more sets of data, and one or more points of interest within the one or more sets of data (Guevara: Fig. 1 [21-23]: identify content data and data attributes to generate a relevant data map; level of sensitivity is a data attribute); It is obvious that a data map includes level of sensitivity of data in the map for data processing;
consult with the data map to: identify data (Guevara: Fig. 1 [24, 35]: use data map to produce content for user);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Michaud and Guevara because the combination would enable using a data map to retrieve data from data mesh by aggregating content of specific purpose for specific user role in the data map to be used for a user query. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve the system’s the ability to use data captured from different sources, involve different purposes, and be stored in different databases in a comprehensive manner to predict outcomes (see Guevara [2]);
Mathews further teaches,
for each set of data of the one or more sets of data, determine a level of sensitivity, wherein to access a set of data with a pre-determined level of sensitivity, one or more credentials are required (Mathews: [18]: request access to data; determines that security key is needed for some data portions (sensitive); “determine an access tier associated with the security key”; [36]: access level for sensitive data);
determine which of the identified data are at a level of sensitivity permitted to be shared with the accessor based on the presented one or more credentials (Mathews: [18]: determine access to sensitive content based on security key of proper authorized individual);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Michaud, Guevara, Mathews and Jones because the combination would enable using data map to generate role-dependent views using multiple layers. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve the process of generating role based views (see Jones [6]);
Jones further teaches,
wherein the data map is a multi-layered data map (Jones: [9]: mapping role-dependent view by using multiple layers);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Michaud, Guevara and Mathews because the combination would enable a verification process for access to secure content. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would “improve data security systems and methods utilized by a business organization to protect their data from exposure to unauthorized individuals” (see Mathews [2]);
Regarding claim 2, Michaud, Guevara, Mathews and Jones teach the invention as claimed in claim 1 above and, wherein the one or more machine learning systems are deep learning systems (Michaud: [35]: machine learning techniques for data mesh processing – multi layered neural networks (deep learning)) (Guevara: [43]: multi-layer neural networks).
Regarding claim 3, Michaud, Guevara, Mathews and Jones teach the invention as claimed in claim 1 above and, wherein the one or more machine learning systems are artificial intelligence systems (Michaud: [35]: “artificial neural networks”).
Regarding claim 4, Michaud, Guevara, Mathews and Jones teach the invention as claimed in claim 1 above and, wherein the one or more machine learning systems present data from a viewpoint of a consumer (Michaud: [64]: data for different user roles – consumer);
The Examiner further notes that the fact that the user is a consumer is not functionally involved in the steps recited. Thus, this will not distinguish the claimed invention from the prior art in terms of patentability.
Regarding claim 5, Michaud, Guevara, Mathews and Jones teach the invention as claimed in claim 1 above and, wherein the one or more machine learning systems present data from a viewpoint of a back-end operator (Michaud: [64]: data for different user roles – user may be back-end operator);
The Examiner further notes that the fact that the user is a back-end operator is not functionally involved in the steps recited. Thus, this will not distinguish the claimed invention from the prior art in terms of patentability.
Regarding claim 6, Michaud, Guevara, Mathews and Jones teach the invention as claimed in claim 1 above and, wherein the one or more machine learning systems present data from a viewpoint of a sales representative (Michaud: [64]: data for different user roles – user may be a sales representative);
The Examiner further notes that the fact that the user is a sales representative is not functionally involved in the steps recited. Thus, this will not distinguish the claimed invention from the prior art in terms of patentability.
Regarding Claim(s) 7-12, this/these claim(s) is/are similar in scope as claim(s) 1-6. Therefore, this/these claim(s) is/are rejected under the same rationale.
Response to Arguments
Applicants’ prior art arguments have been fully considered and moot in view of the current office action. Since the arguments pertain to the amended sections of the claim, the applicant is requested to refer to the cited sections and explanations in the action presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the attached 892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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