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 .
Response to Amendment
This is the initial office action that has been issued in response to patent application, 18/856,559, filed on 07/20/2021. Claims 1-12 are currently pending and have been considered below. Claims 1 and 10-11 are an independent claim.
Response to Arguments
Applicants’ arguments have been considered and are persuasive. The rejections of claim 10 under 35 U.S.C. 112f is withdrawn
Applicants’ arguments have been considered and are persuasive. The rejections of claims 3 and 10 under 35 U.S.C. 112b is withdrawn
Claims 10-12 have been amended and rejection under 35 U.S.C. 112d is withdrawn.
Applicant’s arguments with respect to claim 1-12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Padgett(US Publication No. 20240160902 A1) in view of Hassanzadeh(US Publication No. 20200183995 A1) in view of Bloom (US Publication No. 20240160902 A1)
Regarding Claim 1:
Padgett discloses:
A computer implemented method for providing data in accordance with an access restriction, the method comprising: (Padgett, [0118], the prompts are received from one or more user devices as the first generative AI model is made available and accessible over one or more computing networks. Access to the first generative AI model may be open or may be restricted to authorized users in some cases.);
determining first data characteristics associated with the first data(Padgett, [0022], and comparing and finding may include comparing the product or service data with goods and services data associated with trademarks in the one or more trademark databases.);
determining second data characteristics associated with second data not being subject to the access restriction(Padgett, [0108], the training data set may include a large number of existing corporate logos. The training data set may include items protected by one or more forms of intellectual property and/or PII, and unavailable for free and unrestricted use, or available only under the terms of one or more user licenses);
Padgett does not disclose:
determining whether a similarity of the first and second data characteristics meets a predetermined threshold
Hassanzadeh discloses:
determining whether a similarity of the first and second data characteristics meets a predetermined threshold(Hassanzadeh, [0011], uses one or more of the similarity functions contained in that library to identify pairs of token sets that meet a predetermined similarity threshold based on a comparison of the tokens in those token sets.);
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to determine whether a similarity between first data characteristics to automatically provide, output, or indicate the second data to the user when the similarity meets the predetermined threshold, as taught by Hassanzadeh, in order to enable similarity-based substitution of alternative data when access to requested data is restricted.
The motivation is to ensure that users are automatically presented with relevant alternative data that is sufficiently similar to the requested restricted data, while maintaining access restrictions and preventing unauthorized disclosure, thereby improving system usability, efficiency, and security.
Padgett in view of Hassanzadeh do not disclose:
receiving a request of a user to access first data,
the first data being subject to the access restriction, wherein the access restriction applies to the user
and automatically outputting or indicating, the second data to the user in response to the receipt of the user request if the similarity meets the predetermined threshold
Bloom discloses:
receiving a request of a user to access first data, (Bloom, [0014], The service provider 110 may make a request to a data holding entity 115 for data about the person 105. [0020], The service provider 210 may make a request to a data holding entity 215 which provides authenticating services or provides personal data. [0037], receive a request at a first entity from a second entity for secure data of a user)
the first data being subject to the access restriction, wherein the access restriction applies to the user(Bloom, [0009], These entities may store private and sensitive data about a person. [0010], These entities secure sensitive user data, but also release the data under the proper authorization. [0011], Before releasing this data to a new entity, the data holding entity may request authorization from the person to release their personal information. [0025], he data available from the adaptive authentication interface system 315 may be secured and may require that the person 305 be authenticated before the secure data is provided. [0042], Based on the adaptive authentication methods performed, the requested secure data may be transmitted to the requesting service provider.)
and automatically outputting or indicating, the second data to the user in response to the receipt of the user request if the similarity meets the predetermined threshold(Bloom, [0042], Based on the adaptive authentication methods performed, the requested secure data may be transmitted to the requesting service provider. [0045], Based on the type of verified authentications and any risk calculations may alter the type or amount of secure data that is released. For example, when the person verifies their location through an application on their mobile device).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett in view of Hassaanzadeh’s similarity-based generative AI output filtering system by enhancing Padgett in view of Hassaanzadeh’s systems to determine whether a similarity between first data characteristics to automatically provide, output, or indicate the second data to the user when the similarity meets the predetermined threshold, as taught by Bloom, in order to provide relevant substitute information while maintaining access restrictions on protected data.
The motivation is to ensure that a user requesting restricted data is still provided with useful and relevant information through alternative data having sufficiently similar characteristics, while preserving security controls, preventing unauthorized disclosure of protected data, and improving the efficiency and usability of the data access system.
Regarding Claim 2:
The method according to claim 1, Padgett in view of Hassanzadeh in further view of Bloom disclose wherein the first and/or second data characteristics are determined based on first and second previously stored metadata associated with the first and second data, respectively(Hassanzadeh, [0012], All of these modules can run on one or more computing systems, e.g., a distributed or cloud based computing system, and can include other components 112 to support the running of the linkage point identification modules. These other components include one or more data bases to store data required or generated during the linkage point identification process, a task scheduler and an information retrieval index. An interface 111 such as a visual interface, a graphical user interface or a Web-based interface is provided in communication with the linkage point identification system to communicate and to visualize the results of the data records linkage evaluation and linkage point identification.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to determine first and second data characteristics based on previously stored metadata associated with first data subject to an access restriction and second data not subject to the access restriction, as taught by Hassanzadeh, in order to enable efficient similarity determination between restricted and unrestricted data using precomputed metadata representations.
The motivation is to ensure that similarity comparisons can be performed efficiently and consistently using stored metadata rather than raw data, thereby reducing computational overhead, improving response time to user requests, and enabling scalable provision of relevant alternative data while maintaining access restrictions and preventing unauthorized disclosure.
Regarding Claim 3:
The method according to claim 1, Padgett in view of Hassanzadeh in further view of Bloom disclose further comprising: prior to the determination of the second data, adding the second data to a plurality of stored data(Hassanzadeh, [0008], The datasets can be of different types and formats, e.g., comma separated value datasets, relational databases stored in relational database management systems,);
in response to the addition of the second data, automatically determining and/or storing second metadata indicative of the second data characteristics(Hassanzadeh, [0019], data record in each pair of data records is contained in one of a given pair of datasets, i.e., in different datasets. In addition, each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. These common entities can be the same entity or related entities.);
and determining the second data characteristics based on the second metadata(Hassanzadeh, [0011], A linkage point discovery module 108 is provided that can identify linkage points between data records across datasets. Each linkage point can be used to link data records in each of two data sets that are associated with the same real-world entity or related real-world entities as determined through a comparison of the token sets associated with attributes in each one of the two data records.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to add second data to a plurality of stored data and, in response to the addition of the second data, automatically determine and store metadata indicative of second data characteristics, and to determine the second data characteristics based on the stored metadata, as taught by Hassanzadeh, in order to enable automated characterization of newly ingested data for subsequent similarity-based comparison. The motivation is to ensure that newly added data can be efficiently and consistently analyzed without manual intervention, allowing similarity determinations to be performed using automatically generated metadata, thereby improving scalability, processing efficiency, and responsiveness of the system while maintaining controlled access to data.
Regarding Claim 4:
The method according to claim 3, Padgett in view of Hassanzadeh in further view of Bloom disclose wherein the automatic determination of the second metadata is performed by a machine learning model(Padgett, [0062], The system 100 includes a generative AI model 102, a similarity-assessment layer 104, and a repository of pre-existing content 106. The generative AI model 102 may be an unsupervised or semi-supervised machine learning algorithm that has been trained using a set of training data content).
Regarding Claim 5:
The method according to claim 1, Padgett in view of Hassanzadeh in further view of Bloom disclose wherein the first and second data characteristics comprises syntax characteristics and/or semantic characteristics of the first and second datasets, respectively(Hassanzadeh, [0004], the attributes in the data sources that can be used to link their records or entities. Traditionally, this is performed by schema matching, where the goal is to identify the schema elements of the input data sources that are semantically related.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to determine first and second data characteristics that comprise syntactic characteristics and/or semantic characteristics of the respective datasets, as taught by Hassanzadeh, in order to enable more accurate similarity determination based on both structural and meaning-based features of the data. The motivation is to ensure that similarity comparisons account for both syntactic structure and semantic content of the data, thereby improving the relevance and accuracy of alternative data provided to users while maintaining access restrictions and preventing disclosure of restricted information.
Regarding Claim 6:
The method according to claim 1, Padgett in view of Hassanzadeh in further view of Bloom disclose further comprising: performing a first process on a third data using the second data; and subsequent to performing the first process, performing a second process on the third data using the first data(Hassanzadeh, [0004], the attributes in the data sources that can be used to link their records or entities. Traditionally, this is performed by schema matching, where the goal is to identify the schema elements of the input data sources that are semantically related. However, the massive growth in the amount of unstructured and semi-structured data in data warehouses and on the Web has created new challenges for this task. With the increasing size and heterogeneity of data sources, the task can no longer be performed manually using simple user interfaces or with specific heuristics that work well only for a certain type of data or domain.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to perform a first process on third data using second data not subject to an access restriction, and, subsequent to performing the first process, to perform a second process on the third data using first data subject to the access restriction, as taught by Hassanzadeh, in order to enable staged data processing that respects access restrictions while still leveraging unrestricted data. The motivation is to ensure that initial processing of data can be performed using unrestricted information to reduce exposure of restricted data, while still allowing restricted data to be applied at a later stage in a controlled manner, thereby improving data security, compliance with access controls, and overall system efficiency.
Regarding Claim 7:
The method according to claim 6, Padgett in view of Hassanzadeh in further view of Bloom disclose further comprising: subsequent to performing the second process, restricting access to the third data and/or enabling access to, or providing, metadata of the third data(Padgett, [0118], the prompts are received from one or more user devices as the first generative AI model is made available and accessible over one or more computing networks. Access to the first generative AI model may be open or may be restricted to authorized users in some cases.).
Regarding Claim 8:
The method of claim 1, Padgett in view of Hassanzadeh in further view of Bloom disclose further comprising: developing, creating or training a machine learning, ML, model using the second data; in response to an input of the user, training the ML model using the first data after the ML model has been developed, created or trained using the second data(Padgett, [0104], The similarity-assessment layer in such a system may include an infringement-assessment MLM that determines the likelihood that an output from the generative AI model would be considered trademark infringement. The MLM may be trained on a training data set that includes examples of trademark infringement and non-infringement, including the allegedly infringing mark and the registered (or unregistered) mark.).
Regarding Claim 9:
The method of claim 8, Padgett in view of Hassanzadeh in further view of Bloom disclose further comprising: subsequent to training the ML model using the first data, applying the access restriction to the ML model(Padgett, [0118], Access to the first generative AI model may be open or may be restricted to authorized users in some cases.).
Regarding Claim 10:
Padgett discloses:
A data processing apparatus comprising means determining first data characteristics associated with the first data; (Padgett, [0022], and comparing and finding may include comparing the product or service data with goods and services data associated with trademarks in the one or more trademark databases.);
determining second data characteristics associated with second data not being subject to the access restriction (Padgett, [0108], the training data set may include a large number of existing corporate logos. The training data set may include items protected by one or more forms of intellectual property and/or PII, and unavailable for free and unrestricted use, or available only under the terms of one or more user licenses);
determining whether a similarity of the first and second data characteristics meets a predetermined threshold (Hassanzadeh, [0011], uses one or more of the similarity functions contained in that library to identify pairs of token sets that meet a predetermined similarity threshold based on a comparison of the tokens in those token sets.);
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to determine whether a similarity between first data characteristics to automatically provide, output, or indicate the second data to the user when the similarity meets the predetermined threshold, as taught by Hassanzadeh, in order to enable similarity-based substitution of alternative data when access to requested data is restricted.
The motivation is to ensure that users are automatically presented with relevant alternative data that is sufficiently similar to the requested restricted data, while maintaining access restrictions and preventing unauthorized disclosure, thereby improving system usability, efficiency, and security.
Padjett in view of Hassanzadeh:
receiving a request of a user to access first data
the first data being subject to the access restriction,
wherein the access restriction applies to the user
and automatically outputting or indicating the second data to the user in response to the receipt of the user request if the similarity meets the predetermined threshold
Bloom discloses:
receiving a request of a user to access first data (Bloom, [0014], The service provider 110 may make a request to a data holding entity 115 for data about the person 105. [0020], The service provider 210 may make a request to a data holding entity 215 which provides authenticating services or provides personal data. [0037], receive a request at a first entity from a second entity for secure data of a user),
the first data being subject to the access restriction, wherein the access restriction applies to the user (Bloom, [0009], These entities may store private and sensitive data about a person. [0010], These entities secure sensitive user data, but also release the data under the proper authorization. [0011], Before releasing this data to a new entity, the data holding entity may request authorization from the person to release their personal information. [0025], he data available from the adaptive authentication interface system 315 may be secured and may require that the person 305 be authenticated before the secure data is provided. [0042], Based on the adaptive authentication methods performed, the requested secure data may be transmitted to the requesting service provider.);
and automatically outputting or indicating the second data to the user in response to the receipt of the user request if the similarity meets the predetermined threshold (Bloom, [0042], Based on the adaptive authentication methods performed, the requested secure data may be transmitted to the requesting service provider. [0045], Based on the type of verified authentications and any risk calculations may alter the type or amount of secure data that is released. For example, when the person verifies their location through an application on their mobile device).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett in view of Hassaanzadeh’s similarity-based generative AI output filtering system by enhancing Padgett in view of Hassaanzadeh’s systems to determine whether a similarity between first data characteristics to automatically provide, output, or indicate the second data to the user when the similarity meets the predetermined threshold, as taught by Bloom, in order to provide relevant substitute information while maintaining access restrictions on protected data.
The motivation is to ensure that a user requesting restricted data is still provided with useful and relevant information through alternative data having sufficiently similar characteristics, while preserving security controls, preventing unauthorized disclosure of protected data, and improving the efficiency and usability of the data access system.
Regarding Claim 11:
Padgett discloses:
An apparatus comprising : a memory configured to store a computer program comprising instructions; and a processor in communication with the memory, wherein the processor, upon executing the instructions(Padgett, [0024], The computing system includes a processor and a memory coupled to the processor. The memory stores computer-executable instructions that, when executed by a processor,), is configured to cause the apparatus
determine first data characteristics associated with the first data (Padgett, [0022], and comparing and finding may include comparing the product or service data with goods and services data associated with trademarks in the one or more trademark databases.);
determine second data characteristics associated with second data not being subject to the access restriction(Padgett, [0108], the training data set may include a large number of existing corporate logos. The training data set may include items protected by one or more forms of intellectual property and/or PII, and unavailable for free and unrestricted use, or available only under the terms of one or more user licenses);
determine whether a similarity of the first and second data characteristics meets a predetermined threshold (Hassanzadeh, [0011], uses one or more of the similarity functions contained in that library to identify pairs of token sets that meet a predetermined similarity threshold based on a comparison of the tokens in those token sets.);
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett’s similarity-based generative AI output filtering system by enhancing Padgett’s systems to determine whether a similarity between first data characteristics to automatically provide, output, or indicate the second data to the user when the similarity meets the predetermined threshold, as taught by Hassanzadeh, in order to enable similarity-based substitution of alternative data when access to requested data is restricted.
The motivation is to ensure that users are automatically presented with relevant alternative data that is sufficiently similar to the requested restricted data, while maintaining access restrictions and preventing unauthorized disclosure, thereby improving system usability, efficiency, and security.
Padgett in view of Hassanzadeh do not disclosereceive a request of a user to access first data
the first data being subject to the access restriction, wherein the access restriction applies to the user
and automatically output or indicate the second data to the user in response to the receipt of the user request if the similarity meets the predetermined threshold
Bloom discloses:
receive a request of a user to access first data (Bloom, [0014], The service provider 110 may make a request to a data holding entity 115 for data about the person 105. [0020], The service provider 210 may make a request to a data holding entity 215 which provides authenticating services or provides personal data. [0037], receive a request at a first entity from a second entity for secure data of a user),
the first data being subject to the access restriction, wherein the access restriction applies to the user (Bloom, [0009], These entities may store private and sensitive data about a person. [0010], These entities secure sensitive user data, but also release the data under the proper authorization. [0011], Before releasing this data to a new entity, the data holding entity may request authorization from the person to release their personal information. [0025], he data available from the adaptive authentication interface system 315 may be secured and may require that the person 305 be authenticated before the secure data is provided. [0042], Based on the adaptive authentication methods performed, the requested secure data may be transmitted to the requesting service provider.)
and automatically output or indicate the second data to the user in response to the receipt of the user request if the similarity meets the predetermined threshold (Bloom, [0042], Based on the adaptive authentication methods performed, the requested secure data may be transmitted to the requesting service provider. [0045], Based on the type of verified authentications and any risk calculations may alter the type or amount of secure data that is released. For example, when the person verifies their location through an application on their mobile device).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Padgett in view of Hassaanzadeh’s similarity-based generative AI output filtering system by enhancing Padgett in view of Hassaanzadeh’s systems to determine whether a similarity between first data characteristics to automatically provide, output, or indicate the second data to the user when the similarity meets the predetermined threshold, as taught by Bloom, in order to provide relevant substitute information while maintaining access restrictions on protected data.
The motivation is to ensure that a user requesting restricted data is still provided with useful and relevant information through alternative data having sufficiently similar characteristics, while preserving security controls, preventing unauthorized disclosure of protected data, and improving the efficiency and usability of the data access system.
Regarding Claim 12:
The method of claim, Padgett in view of Hassanzadeh in further view of Bloom disclose further comprising a non-transitory computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1
(Same rational applied to claim 1 above)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAYASA SHAAWAT whose telephone number is (571)272-3939. The examiner can normally be reached on M-F, 8 AM TO 5 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JEFFREY PWU can be reached on (571)272-6789. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAYASA A. SHAAWAT/Examiner, Art Unit 2433
/JEFFREY C PWU/Supervisory Patent Examiner, Art Unit 2433
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