DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The instant application having Application No. 19/094,057 filed on 3/28/2025 is presented for examination by the Examiner. Claims 1-20 are currently pending in the present application.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of Applicant's claim for priority based on a Provisional Application 63/570,943 filed on 3/28/2024.
Drawings Objection
A descriptive textual label for each numbered element in these figures would be needed to fully and better understand these figures without substantial analysis of the detailed specification. Any structural detail that is of sufficient importance to be described should be shown in the drawing. Optionally, Applicant may wish to include a table next to the present figure to fulfill this requirement. See 37 CFR 1.83. 37 CFR 1.84(n)(o) is recited below:
(n) Symbols. Graphical drawing symbols may be used for conventional elements when appropriate. The elements for which such symbols and labeled representations are used must be adequately identified in the specification. Known devices should be illustrated by symbols which have a universally recognized conventional meaning and are generally accepted in the art. Other symbols which are not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.
(o) Legends. Suitable descriptive legends may be used, or may be required by the Examiner, where necessary for understanding of the drawing, subject to approval by the Office. They should contain as few words as possible."
The drawings are objected to because some elements or boxes (i.e., items in Figures 1 and 2) have no suitable descriptive legends, which should contain as few words as possible, where necessary for understanding of the drawing.
Claim Objections
Claim 8 is objected to because of the following informalities:
As per claim 8, the claim recites “wherein the updating comprises updating in in real-time” which should be written or amended as “wherein the updating comprises updating in ”.
Appropriate correction is respectfully required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1, the claim recites “A method for building a high trust dataset, comprising: repeatedly,
retrieving data from a plurality of data sources, wherein one or more of the plurality of data sources comprises structured and/or unstructured data and each one of the plurality of data sources has an associated trust score,
identifying at least one subset of the retrieved data which conflicts with at least one of another subset of the retrieved data and the high trust dataset,
for each identified subset of the retrieved data, selecting one of the plurality of data sources from which the subset will be included in the high trust dataset based on the associated trust score,
based on the trust score, updating the high trust dataset to comprise the subset from the selected one of the plurality of data sources, and
updating the associated trust score of at least one of the plurality of data sources”.
Step 1: Statutory Category
Claim 1 discloses a method which is a process within the meaning of the section.
Step 2A - Prong One: Judicial Exception Recited
The claim recites the limitations “identifying” and “selecting” which specifically recite “identifying at least one subset of the retrieved data which conflicts with at least one of another subset of the retrieved data and the high trust dataset” and “selecting one of the plurality of data sources from which the subset will be included in the high trust dataset based on the associated trust score”. These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, nothing in the claim element precludes the steps from practically being performed in a human mind or with the aid of pen or paper. For example, “identifying” and “selecting” in the context of this claim encompass a user mentally, and with the aid of pen and paper looking at information and/or characteristics of data to identify and select the desired or relevant data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong Two: Integrated into a Practical Application
The claim recites the additional elements “retrieving data from a plurality of data sources…”, “updating the high trust dataset…” and “updating the associated trust score…”. The judicial exception is not integrated into a practical application. In particular, the additional steps: the “receiving” and “updating” steps mount to data gathering which is considered to be insignificant extra-solution activity (see MPEP 2106.05(g)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the steps of “updating” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of performing the “updating” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim as a whole, does not amount to significantly more than the abstract idea itself. This is because the claim does not affect an improvement to the functioning of a computer itself; and the claim does not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, claim 1 is directed to an abstract idea.
As per claim 2, the claim recites “The method of claim 1, wherein the identifying comprises: identifying the subset and the another subset as analogous; comparing the subset with the another subset of retrieved data; and determining at least one conflict between the subset and the another subset”. The judicial exception is not integrated into a practical application. In particular, these additional limitations have been discussed above with respect to the abstract idea (i.e., “Mental Processes”) and do not amount to significantly more than the above-identified judicial exception.
As per claim 3, the claim recites “The method of claim 1, wherein the identifying comprises identifying each of the subset, the another subset, and a subset of the high trust dataset as conflicting”. The judicial exception is not integrated into a practical application. In particular, this additional limitation has been discussed above with respect to the abstract idea (i.e., “Mental Processes”) and does not amount to significantly more than the above-identified judicial exception.
As per claim 4, the claim recites “The method of claim 1, wherein the trust scores of the plurality of data sources comprises at least one high trust score and at least one low trust score”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 5, the claim recites “The method of claim 1, wherein the plurality of data sources comprises one or more of a database, a data feed and a data structure”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 6, the claim recites “The method of claim 1, wherein each one of the plurality of data sources is updated at different frequencies”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 7, the claim recites “The method of claim 1, wherein at least one of the plurality of data sources is associated with a healthcare entity”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 8, the claim recites “The method of claim 1, wherein the updating comprises updating in in real-time”. The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
As per claim 9, the claim recites “The method of claim 1, wherein the updating comprises use of at least one of artificial intelligence and data analytics”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 10, the claim recites “The method of claim 9, wherein at least one of the artificial intelligence and the data analytic is based on one or more of historical data, contextual data and data type”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 11, the claim recites “The method of claim 1, further comprising predicting a trust score of a data source using artificial intelligence”. The judicial exception is not integrated into a practical application. In particular, this additional limitation has been discussed above with respect to the abstract idea (i.e., “Mental Processes”) and does not amount to significantly more than the above-identified judicial exception.
As per claim 12, the claim recites “The method of claim 9, wherein the artificial intelligence comprises one or more of machine learning and artificial generative intelligence”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 13, the claim recites “The method of claim 12, wherein the machine learning comprises one or more artificial neural networks”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 14, the claim recites “The method of claim 1, wherein a frequency of the repeating is in accordance with the results of applied artificial intelligence”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 15, the claim recites “The method of claim 1, further comprising providing the high trust dataset to at least one downstream system”. The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
As per claim 16, the claim recites “A system for building a high trust dataset, comprising:” to include similar limitations as recited in the claim 1.
Step 1: Statutory Category
Claim 16 discloses a system which is a machine within the meaning of the section.
Step 2A – Prong One: Judicial Exception Recited
The claim recites the limitations as same as claim 1, and therefore are interpreted as an abstract idea under the same premise as claim 1.
Step 2A – Prong Two: Integrated into a Practical Application
The claim recites additional elements as same as claim 1, and therefore are interpreted as an abstract idea under the same premise as claim 1.
Step 2B: Claim provides an Inventive Concept
The claim recites the limitations as same as claim 1, and therefore is considered under the same premise as claim 1 as no inventive concept in the claim, and thus it is ineligible.
As per claim 17, the claim recites “The system of claim 16, wherein the retrieved data is encrypted and the computer-executable instructions when executed by the processing device further causes the processing device to decrypt the retrieved data”. The judicial exception is not integrated into a practical application. In particular, these additional limitations amount to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 18, the claim recites “The system of claim 16, further comprising an application interface for the data handling engine”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 19, the claim recites “The system of claim 16, further comprising a plurality of downstream systems having access to the high trust dataset”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 20, the claim recites “A non-transitory computer readable medium for building a high trust dataset, comprising computer-executable instructions for:” similar limitations as recited in the claim 1.
Step 1: Statutory Category
Claim 20 discloses a non-transitory computer readable medium which is a manufacture within the meaning of the section.
Step 2A – Prong One: Judicial Exception Recited
The claim recites the limitations as same as claim 1, and therefore are interpreted as an abstract idea under the same premise as claim 1.
Step 2A – Prong Two: Integrated into a Practical Application
The claim recites additional elements as same as claim 1, and therefore are interpreted as an abstract idea under the same premise as claim 1.
Step 2B: Claim provides an Inventive Concept
The claim recites the limitations as same as claim 1, and therefore is considered under the same premise as claim 1 as no inventive concept in the claim, and thus it is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8, 15, 16 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fuchs et al. (US 2014/0289187 A1).
As per claim 1, Fuchs et al. discloses A method for building a high trust dataset, comprising: repeatedly, as (see e.g., ¶ 0050 and Figs. 6-8).
retrieving data from a plurality of data sources, wherein one or more of the plurality of data sources comprises structured and/or unstructured data, as (see e.g., ¶¶ 0041 – 0042: as receiving claims from other sources) and each one of the plurality of data sources has an associated trust score, as (see e.g., ¶ 0050: as trust scores of sources).
identifying at least one subset of the retrieved data which conflicts with at least one of another subset of the retrieved data and the high trust dataset, as (see e.g., ¶ 0053: as two conflicting claims are identified based on sources’ ratings).
for each identified subset of the retrieved data, selecting one of the plurality of data sources from which the subset will be included in the high trust dataset based on the associated trust score, as (see e.g., ¶¶ 0052 and 0053: as selecting claim’s source having high trust score).
based on the trust score, updating the high trust dataset to comprise the subset from the selected one of the plurality of data sources, and as (see e.g., ¶¶ 0050 and 0053 – 0055: as updating claim(s) from the source having high trust score).
updating the associated trust score of at least one of the plurality of data sources, as (see e.g., ¶¶ 0050 and 0053 – 0055: as updating the source having high trust score).
As per claim 2, Fuchs et al. discloses The method of claim 1, wherein the identifying comprises: identifying the subset and the another subset as analogous; comparing the subset with the another subset of retrieved data; and determining at least one conflict between the subset and the another subset, as (see e.g., ¶ 0053: as “if there are two conflicting claims, one from a well-rated source but presented one year ago, and the other from two sources with slightly lower ratings but both presented within the last week, then the criteria may be defined to accept the more recent claim”).
As per claim 3, Fuchs et al. discloses The method of claim 1, wherein the identifying comprises identifying each of the subset, the another subset, and a subset of the high trust dataset as conflicting, as (see e.g., ¶ 0053: as “if there are two conflicting claims, one from a well-rated source but presented one year ago, and the other from two sources with slightly lower ratings but both presented within the last week, then the criteria may be defined to accept the more recent claim”).
As per claim 4, Fuchs et al. discloses The method of claim 1, wherein the trust scores of the plurality of data sources comprises at least one high trust score and at least one low trust score, as (see e.g., ¶ 0053: as “if there are two conflicting claims, one from a well-rated source but presented one year ago, and the other from two sources with slightly lower ratings but both presented within the last week, then the criteria may be defined to accept the more recent claim”).
As per claim 5, Fuchs et al. discloses The method of claim 1, wherein the plurality of data sources comprises one or more of a database, a data feed and a data structure, as (see e.g., ¶ 0022: as “receiving a request from a source to update a database record, including creating or deleting the record. The request or "claim" can be accepted solely based on the trust level associated with the source of the claim, or in combination with other relevant factors.”).
As per claim 6, Fuchs et al. discloses The method of claim 1, wherein each one of the plurality of data sources is updated at different frequencies, as (see e.g., ¶¶ 0050 and 0053 – 0055: as updating the source having high trust score).
As per claim 8, Fuchs et al. discloses The method of claim 1, wherein the updating comprises updating in in real-time, as (see e.g., ¶ 0042: as “the accuracy of a source's claims can be tracked over time to provide one indication of the reliability and trustworthiness of the source”).
As per claim 15, Fuchs et al. discloses The method of claim 1, further comprising providing the high trust dataset to at least one downstream system, as (see e.g., ¶ 0022: as “systems and methods for receiving a request from a source to update a database record, including creating or deleting the record. The request or "claim" can be accepted solely based on the trust level associated with the source of the claim, or in combination with other relevant factors. For example, updates affecting a record are only made if the trust score of the source exceeds a threshold. Further, if multiple claims are made against the same record, the claim of the source with the highest trust score will be accepted so long as the trust score of the source exceeds a threshold, or multiple sources make the same claim”).
As per claim 16, the claim is rejected under the same premise as claim 1.
As per claim 18, Fuchs et al. discloses The system of claim 16, further comprising an application interface for the data handling engine, as (see e.g., ¶ 0037; and Fig. 5).
As per claim 19, Fuchs et al. discloses The system of claim 16, further comprising a plurality of downstream systems having access to the high trust dataset, as (see e.g., ¶ 0022: as “systems and methods for receiving a request from a source to update a database record, including creating or deleting the record. The request or "claim" can be accepted solely based on the trust level associated with the source of the claim, or in combination with other relevant factors. For example, updates affecting a record are only made if the trust score of the source exceeds a threshold. Further, if multiple claims are made against the same record, the claim of the source with the highest trust score will be accepted so long as the trust score of the source exceeds a threshold, or multiple sources make the same claim”).
As per claim 20, the claim is rejected under the same premise as claim 1.
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 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Fuchs et al., and further in view of Subramanian et al. (US 2017/0373935 A1).
As per claim 9, Subramanian et al. discloses The method of claim 1, wherein the updating comprises use of at least one of artificial intelligence and data analytics, which is not explicitly disclosed by Fuchs et al., as (Subramanian et al., see e.g., ¶¶ 0035 and 0077: as machine learning techniques may be employed to generate and update trust scores over time).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include using machine learning techniques in updating trust score, as taught by Subramanian et al., for the benefit of generating relevance scores, trust scores and the like.
As per claim 10, Subramanian et al. discloses The method of claim 9, wherein at least one of the artificial intelligence and the data analytic is based on one or more of historical data, contextual data and data type, which is not explicitly disclosed by Fuchs et al., as (Subramanian et al., see e.g., ¶¶ 0035 and 0077: as machine learning techniques may be employed using historical data and data type).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include using machine learning techniques in updating trust score, as taught by Subramanian et al., for the benefit of generating relevance scores, trust scores and the like.
As per claim 11, Subramanian et al. discloses The method of claim 1, further comprising predicting a trust score of a data source using artificial intelligence, which is not explicitly disclosed by Fuchs et al., as (Subramanian et al., see e.g., ¶¶ 0035 and 0045). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include using machine learning techniques in updating trust score, as taught by Subramanian et al., for the benefit of generating relevance scores, trust scores and the like.
As per claim 12, Subramanian et al. discloses The method of claim 9, wherein the artificial intelligence comprises one or more of machine learning and artificial generative intelligence, which is not explicitly disclosed by Fuchs et al., as (Subramanian et al., see e.g., ¶¶ 0035 and 0077). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include using machine learning techniques in updating trust score, as taught by Subramanian et al., for the benefit of generating relevance scores, trust scores and the like.
As per claim 13, Subramanian et al. discloses The method of claim 12, wherein the machine learning comprises one or more artificial neural networks, which is not explicitly disclosed by Fuchs et al., as (Subramanian et al., see e.g., ¶¶ 0035 and 0045). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include using machine learning techniques in updating trust score, as taught by Subramanian et al., for the benefit of generating relevance scores, trust scores and the like.
As per claim 14, Subramanian et al. discloses The method of claim 1, wherein a frequency of the repeating is in accordance with the results of applied artificial intelligence, which is not explicitly disclosed by Fuchs et al., as (Subramanian et al., see e.g., ¶¶ 0035 and 0077). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include using machine learning techniques in updating trust score, as taught by Subramanian et al., for the benefit of generating relevance scores, trust scores and the like.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fuchs et al., and further in view of Motgi et al. (US 2025/0077488 A1).
As per claim 7, Motgi et al. discloses The method of claim 1, wherein at least one of the plurality of data sources is associated with a healthcare entity, which is not explicitly disclosed by Fuchs et al., as (Motgi et al., see e.g., ¶¶ 0031 and 0032). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include healthcare provider as data source, as taught by Motgi et al., for the benefit of providing data with quality requirements.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Fuchs et al., and further in view of Zilberberg et al. (US 2015/0127660 A1).
As per claim 17, Zilberberg et al. discloses The system of claim 16, wherein the retrieved data is encrypted and the computer-executable instructions when executed by the processing device further causes the processing device to decrypt the retrieved data, which is not explicitly disclosed by Fuchs et al., as (Zilberberg et al., see e.g., ¶ 0089). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Fuchs et al. invention to include techniques of data/message encrypting and decrypting, as taught by Zilberberg et al., for the benefit of ensuring data confidentiality, integrity, and safety during transmission or storage.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0245197 A1 by Gout et al. teaches a Trust Score Engine are directed to providing a user interface for identifying datasets collected into a dataset inventory wherein the dataset inventory may be displayed within the user interface. The Trust Score Engine determines social curation activities by respective user accounts that have been applied the datasets in the dataset inventory and validates the datasets in the dataset inventory according to pre-defined attributes applied to any of the respective datasets. The Trust Score Engine generates a first trust score for a first dataset according to any determined social curation activities and any pre-defined attributes that correspond to the first dataset. The Trust Score Engine receives a selection of a trust score visualization functionality, via the user interface, with respect to the first dataset.
US 2013/0080197 A1 by Kung et al. teaches evaluating a trust value for a report are disclosed herein. The method includes obtaining one or more reports by the computer, where the reports are formed of one or more fields of data. An end-to-end lineage for the data is determined to trace the data back to the data source systems from which the data had originated initially. Further, the method includes validating each of the multiple data source systems including intermediate tables, and determining a data quality score for each of the multiple data source systems. A trust value for the report is calculated based on the data quality scores for the one or more data source systems and intermediate tables, and rendered along with the report.
US 9,727,591 B1 by Sharma et al. teaches one or more trust characteristics are obtained. The one or more trust characteristics are attributable to a storage infrastructure from which one or more data sets stored in one or more data repositories are obtained. The one or more trust characteristics attributable to the storage infrastructure are associated with the one or more data sets such that the one or more data sets are characterized as having a trustworthiness reflective of the one or more trust characteristics. The one or more trust characteristics and the association with the one or more data sets are stored as metadata in the one or more data repositories.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bai D. Vu whose telephone number is (571) 270-1751. The examiner can normally be reached 9:00 - 5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at (571) 272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BAI D VU/Primary Examiner, Art Unit 2163 4/2/2026