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 .
In response to Applicant’s claims filed on April 08, 2026, claims 1-20 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 10/30/2025. In this action Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wellmann et al. (US Pub. No. 20220335518) and Argyros et al. (US Pub. No. 20190156256) in further view of Thakur et al. (US Pub. No. 20220038490). The Wellmann et al. reference has been added to address the amendment of running the data subject project on a collection of documents to assess the data subject, where the running the data subject comprises applying one or more of the plurality of AI models to the collection of documents to assess the data subject.
Applicant’s arguments:
In regards to claim 1 on Pages 10, applicant argues the cited art fails to clearly and unequivocally disclose a “As submitted on page 8 of the October 2025 Reply, human minds do not use AI models. Nor can human minds, even with the aid of pen and paper, solve a problem that is particular to AI platforms operating in cloud computing environments, i.e., in the realm of computer networks. Since this is a problem specifically arising in the realm of computer networks and since this problem is solved by the claimed invention, the claimed invention amounts to significantly more than any abstract idea and are patentable subject matter, according to the holding of DDR Holdings, LLC V. Hotels.com L.P., 773 F.3d 1245, 1257 (Fed. Cir. 2014).” as alleged.
Examiner’s Reply:
AI models are being used by a human in coordination with a computer as a tool. Creating, defining, determining, searching, and generating steps are part of limitations that recites a mental process capable of being performed by the human mind by using data along with a computer being used as a generic tool. The AI models themselves are just additional elements.
Applicant’s arguments:
In regards to claim 1 on Pages 11, applicant argues the cited art fails to clearly and unequivocally disclose a “By solving a technical problem that specifically arising in the realm of an AI platform operating in a cloud environment, the claimed invention is necessarily rooted in computer technology. This is exactly the reason that the claims in DDR Holdings were found to be eligible for patent, as submitted in the October 2025 Reply. Further, the invention operates in the same way as the invention at issue in DDR Holdings, namely, entirely in a computing environment, no brick and mortar involved..” as alleged.
Examiner’s Reply:
The examiner notes that the computer as recited in the claims are being used for natural language understanding using a computer (being used a generic tools) within a computing environment. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Document processing and Risk analysis does not improve the functioning of a computer.
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 non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-7), system(s) (claims 8-14), and program product 15-20 is/are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 8, and 15 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 1 recites the following limitations directed towards a Mental Processes:
defining a data subject, the defining performed by a data subject assessment service responsive to an instruction from a user, the instruction received through a user interface of the data subject assessment service, the data subject assessment service hosted on an artificial intelligence (AI) platform, the AI platform operating in a cloud computing environment and having a plurality of AI models (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by defining subject data);
creating a data subject project, wherein the creating the data subject project comprises associating the data subject project with the plurality of AI models, each of the plurality of AI models modeling a risk (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by creating subject data);
configuring the data subject project, the configuring the data subject project comprising setting the risk at a risk level responsive to a setting received through the user interface of the data subject assessment service (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by configuring subject data);
adding the data subject to the data subject project (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by adding subject data);
wherein accessing the data subject comprises (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to assess subject data);
determining how sets of metadata associated with the collection of documents relate to one another (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine metadata); and
based on relationships thus determined, generating data subject assessment results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generating results);
searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by searching subject data).
generating a report on the subset of the data subject assessment results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a report).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 8, and 15:
a processor (i.e., as a generic processor/component performing a generic computer function);
a non-transitory computer-readable medium (i.e., as a generic processor/component performing a generic computer function); and
instructions (i.e., as a generic processor/component performing a generic computer function) stored on the non-transitory computer-readable medium and translatable by the processor;
running the data subject project on a collection of documents to assess the data subject, where the running the data subject comprises applying one or more of the plurality of AI models to the collection of documents to assess the data subject (recites insignificant extrasolution activity for AI modeling).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 8, and 15 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2, 9, 16:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
importing a file containing data subject information (recites insignificant extrasolution activity for importing subject data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3 and 10:
Step 2A, prong one of the 2019 PEG:
wherein the configuring the data subject project comprises selecting an instance of an analytic engine operating on the AI platform (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by selecting an instance).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4, 11, and 17:
Step 2A, prong one of the 2019 PEG:
performing data subject assessment operations on the document (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to assessing a document).
Step 2A Prong Two Analysis:
accessing a data source (recites insignificant extrasolution activity for assessing subject data);
retrieving a document from the collection (recites insignificant extrasolution activity for retrieving subject data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5, 12, and 18:
Step 2A, prong one of the 2019 PEG:
performing data subject assessment operations on the document (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to assessing a document).
Step 2A Prong Two Analysis:
accessing a data source (recites insignificant extrasolution activity for assessing subject data);
retrieving a document from the collection (recites insignificant extrasolution activity for retrieving subject data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6, 13, and 19:
Step 2A, prong one of the 2019 PEG:
wherein the configuring the data subject project comprises selecting a processing language (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by selecting a processing language).
Step 2A Prong Two Analysis:
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7, 14, and 20:
Step 2A, prong one of the 2019 PEG:
customizing the data subject project, wherein the customizing the data subject project comprises adding another data subject to the data subject project (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to customizing subject data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wellmann et al. (US Pub. No. 20220335518) and Argyros et al. (US Pub. No. 20190156256) in further view of Thakur et al. (US Pub. No. 20220038490).
.
With respect to claim 1, Wellmann et al. discloses a data subject assessment method, comprising:
defining a data subject, the defining performed by a data subject assessment service
responsive to an instruction from a user, the instruction received through a user interface of the
data subject assessment service, the data subject assessment service hosted on an artificial
intelligence (AI) platform, the AI platform operating in a cloud computing environment and
having a plurality of AI models (Paragraph 76 discloses processing device 104 may apply a document analysis model to the document data (e.g., classified document data). In some embodiments, processing device 104 may select a machine learning model from among a plurality of candidate machine learning models based on the classified document data);
creating a data subject project, wherein the creating the data subject project comprises
associating the data subject project with the plurality of AI models, each of the plurality of AI
models modeling a risk (Paragraph 91 discloses user device 300 may be a smartphone associated with a user, and any of interfaces 700A-700D may be displayed on user interface 340 (e.g., a display panel or a touchscreen). Any or all of these user interfaces may include data included in process 400 and/or 500 (e.g., a risk level, a model input value, etc.));
configuring the data subject project, the configuring the data subject project comprising
setting the risk at a risk level responsive to a setting received through the user interface of the
data subject assessment service (Paragraph 30 discloses examination assistant module 118 may be configured to carry out all or part of process 400, described below. In some embodiments, examination assistant module 118 may provide particularized analysis information and/or recommendations, which may be based on user input);
adding the data subject to the data subject project (Paragraph 72 discloses processing device 104 may have access to multiple extraction models that have particularized parameters for different types of documents or different entities (e.g., financial institutions), and may select an extraction model designated (e.g., in a look-up table) for a particular document type (e.g., a loan closing document) and/or entity (e.g., bank), which may have been identified through the document data classification (e.g., at step 504));
running the data subject project on a collection of documents to assess the data subject, where the running the data subject comprises applying one or more of the plurality of AI models to the collection of documents to assess the data subject (Paragraph 57 discloses statistical and/or machine learning models to process, scaling numerical values, normalizing data coming from different sources, creating new dynamic feature sets such as time lags or delta shifts between periods, determining simple moving averages or exponential moving averages, determining volatility or ranges in an input variable to describe time series data, and/or another data refinement operation);
generating a report on the subset of the data subject assessment results (Paragraph 83 discloses the API request may be a request for normalized data as a service, which may involve a request to APIs that provide processes and services for generating normalized and high-quality data originating from banking cores and document repository in a format for further analysis or modeling in a client application or platform (e.g., modeling, visualization, reporting of normalized, granular data, etc.)). Wellmann et al. does not disclose wherein accessing assessing the data subject comprises: determining how sets of metadata associated with the collection of documents relate to one another.
However, Argyros et al. teaches wherein accessing assessing the data subject comprises: determining how sets of metadata associated with the collection of documents relate to one another (Paragraph 79 discloses generate a set of metadata tags which are assigned to the risk identifiers that correspond to operational risk categories); and
based on relationships thus determined, generating data subject assessment results (Paragraph 87 discloses application parses the set of risk assessment documents to identify one or more portions of text corpus indicative of operational risk (block 504). In one embodiment, parsing of the set of risk assessment documents may be performed by an NLP algorithm. The application determines contextual features from the identified one or more portions of the text corpus (block 506). In one embodiment, the contextual features may refer to a regulation, an obligation, a topic, a subject, or a subject-matter domain).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wellmann et al. with Argyros et al. This would have facilitated risk assessment. See Argyros et al. Paragraph(s) 5-7.
Krishna et al. as modified by Thakur et al. does not searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results.
However, teaches searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results (Paragraph 48 discloses cybersecurity threats 902 associated with the extracted features and their interrelationships with system elements 54a).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wellmann et al. and Argyros et al. with Thakur et al. This would have facilitated risk assessment. See Thakur et al. Paragraph(s) 3-6.
The Wellmann et al. reference as modified by Argyros et al. and Thakur et al. teaches all the limitations of claim 1. With respect to claim 2, Thakur et al. discloses the data subject assessment method according to claim 1, further comprising:
importing a file containing data subject information (Paragraph 36 discloses the first text miner 46 mines unstructured data from unstructured text sources 906 including system configuration files and security policies, extracts features of the target computer system 900 from these text sources 906, and feeds extracted features of the target computer system 900 into the artificial intelligence system modeling assistant 52a). The motivation to combine statement previously provided in the rejection of dependent claim 2 provided above, combining the Wellmann et al. reference and the Thakur et al. reference is applicable to independent claim 1.
The Wellmann et al. reference as modified by Argyros et al. and Thakur et al. teaches all the limitations of claim 1. With respect to claim 3, Thakur et al. discloses the data subject assessment method according to claim 1, wherein the configuring the data subject project comprises selecting an instance of an analytic engine operating on the AI platform (Paragraph 56 discloses threat analyzer 88 includes a third artificial intelligence model 38 including a third neural network 90 that has been trained on a third training data set including classified system elements 43, classified threats 65, and user inputted security recommendations, and is configured to, at run-time, receive run-time input of classified system elements 54a for a target computer system 900 and classified cybersecurity threats for a target cybersecurity threat, and in response output a predicted security recommendation 92 for each classified system element and classified threat pair of the target computer system 900). The motivation to combine statement previously provided in the rejection of dependent claim 3 provided above, combining the Wellmann et al. reference and the Thakur et al. reference is applicable to independent claim 1.
The Wellmann et al. reference as modified by Argyros et al. and Thakur et al. teaches all the limitations of claim 1. With respect to claim 4, Thakur et al. discloses the data subject assessment method according to claim 1, wherein the running the data subject project comprises:
accessing a data source (Paragraph 36 discloses the first text miner 46 mines unstructured data from unstructured text source);
retrieving a document from the collection (Paragraph 46 discloses the second text miner 68 mines, crawls, and/or scrapes various documents and IT security policy documents 910a from the internet or intranet for known cybersecurity threats); and
performing data subject assessment operations on the document (Paragraph 46 discloses the second text miner 68 mines, crawls, and/or scrapes various documents and IT security policy documents 910a from the internet or intranet for known cybersecurity threats. The motivation to combine statement previously provided in the rejection of dependent claim 4 provided above, combining the Wellmann et al. reference and the Thakur et al. reference is applicable to independent claim 1.
The Wellmann et al. reference as modified by Argyros et al. and Thakur et al. teaches all the limitations of claim 1. With respect to claim 5, Thakur et al. discloses the data subject assessment method according to claim 4, wherein the data subject assessment operations comprise text mining operations and application of rules, wherein the text mining operations produce metadata about the document, and wherein the application of rules leverages the metadata and applies an action to the document when a condition is met (Paragraph 36 discloses the first text miner 46 mines unstructured data from unstructured text source and 41 discloses a graphical user interface 56 to present metadata for each system element 54a as suggestions 58, and allow for a user such as human operator 904 to provide system property adoption user input 22 including an indication of whether to accept or reject each of the system-related candidate properties). The motivation to combine statement previously provided in the rejection of dependent claim 5 provided above, combining the Wellmann et al. reference and the Thakur et al. reference is applicable to independent claim 1.
The Wellmann et al. reference as modified by Argyros et al. and Thakur et al. teaches all the limitations of claim 1. With respect to claim 6, Thakur et al. discloses the data subject assessment method according to claim 1, wherein the configuring the data subject project comprises selecting a processing language (Paragraph 44 discloses A threat template editor 70 is configured to process the extracted features, identify cybersecurity threats 902 associated with the extracted features and their interrelationships with system elements 54a, and populate a threat template 72 for each identified cybersecurity threat 902 with threat-specific candidate properties for that threat 902). The motivation to combine statement previously provided in the rejection of dependent claim 6 provided above, combining the Wellmann et al. reference and the Thakur et al. reference is applicable to independent claim 1.
The Wellmann et al. reference as modified by Argyros et al. and Thakur et al. teaches all the limitations of claim 1. With respect to claim 7, Thakur et al. discloses the data subject assessment method according to claim 1, further comprising: customizing the data subject project, wherein the customizing the data subject project comprises adding another data subject to the data subject project (Paragraph 49 discloses the user, i.e., human operator 904, can accept, reject, or modify the candidate properties 86 for each cybersecurity threat 902, and can add, delete, or modify the cybersecurity threats 902 themselves and their interrelationships with system elements 54a, so that the user can provide feedback to improve the accuracy of the identification of cybersecurity threats 902 and the populating of the threat templates 72). The motivation to combine statement previously provided in the rejection of dependent claim 7 provided above, combining the Wellmann et al. reference and the Thakur et al. reference is applicable to independent claim 1.
With respect to claim 8, Wellmann et al. discloses a data subject assessment system, comprising:
a processor (See Fig. 3);
a non-transitory computer-readable medium (See Fig. 3); and
instructions stored on the non-transitory computer-readable medium and translatable by the processor for:
defining a data subject, the defining performed by a data subject assessment service
responsive to an instruction from a user, the instruction received through a user interface of the
data subject assessment service, the data subject assessment service hosted on an artificial
intelligence (AI) platform, the AI platform operating in a cloud computing environment and
having a plurality of AI models (Paragraph 76 discloses processing device 104 may apply a document analysis model to the document data (e.g., classified document data). In some embodiments, processing device 104 may select a machine learning model from among a plurality of candidate machine learning models based on the classified document data);
creating a data subject project, wherein the creating the data subject project comprises
associating the data subject project with the plurality of AI models, each of the plurality of AI
models modeling a risk (Paragraph 91 discloses user device 300 may be a smartphone associated with a user, and any of interfaces 700A-700D may be displayed on user interface 340 (e.g., a display panel or a touchscreen). Any or all of these user interfaces may include data included in process 400 and/or 500 (e.g., a risk level, a model input value, etc.));
configuring the data subject project, the configuring the data subject project comprising
setting the risk at a risk level responsive to a setting received through the user interface of the
data subject assessment service (Paragraph 30 discloses examination assistant module 118 may be configured to carry out all or part of process 400, described below. In some embodiments, examination assistant module 118 may provide particularized analysis information and/or recommendations, which may be based on user input);
adding the data subject to the data subject project (Paragraph 72 discloses processing device 104 may have access to multiple extraction models that have particularized parameters for different types of documents or different entities (e.g., financial institutions), and may select an extraction model designated (e.g., in a look-up table) for a particular document type (e.g., a loan closing document) and/or entity (e.g., bank), which may have been identified through the document data classification (e.g., at step 504));
running the data subject project on a collection of documents to assess the data subject,
where the running the data subject comprises applying one or more of the plurality of AI models
to the collection of documents to assess the data subject (Paragraph 57 discloses statistical and/or machine learning models to process, scaling numerical values, normalizing data coming from different sources, creating new dynamic feature sets such as time lags or delta shifts between periods, determining simple moving averages or exponential moving averages, determining volatility or ranges in an input variable to describe time series data, and/or another data refinement operation);
generating a report on the subset of the data subject assessment results (Paragraph 83 discloses the API request may be a request for normalized data as a service, which may involve a request to APIs that provide processes and services for generating normalized and high-quality data originating from banking cores and document repository in a format for further analysis or modeling in a client application or platform (e.g., modeling, visualization, reporting of normalized, granular data, etc.)). Wellmann et al. does not disclose wherein accessing assessing the data subject comprises: determining how sets of metadata associated with the collection of documents relate to one another.
However, Argyros et al. teaches wherein accessing assessing the data subject comprises: determining how sets of metadata associated with the collection of documents relate to one another (Paragraph 79 discloses generate a set of metadata tags which are assigned to the risk identifiers that correspond to operational risk categories); and
based on relationships thus determined, generating data subject assessment results (Paragraph 87 discloses application parses the set of risk assessment documents to identify one or more portions of text corpus indicative of operational risk (block 504). In one embodiment, parsing of the set of risk assessment documents may be performed by an NLP algorithm. The application determines contextual features from the identified one or more portions of the text corpus (block 506). In one embodiment, the contextual features may refer to a regulation, an obligation, a topic, a subject, or a subject-matter domain).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wellmann et al. with Argyros et al. This would have facilitated risk assessment. See Argyros et al. Paragraph(s) 5-7.
Krishna et al. as modified by Thakur et al. does not searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results.
However, teaches searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results (Paragraph 48 discloses cybersecurity threats 902 associated with the extracted features and their interrelationships with system elements 54a).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wellmann et al. and Argyros et al. with Thakur et al. This would have facilitated risk assessment. See Thakur et al. Paragraph(s) 3-6.
With respect to claim 9, it is rejected on grounds corresponding to above rejected claim 2, because claim 9 is substantially equivalent to claim 2.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 3, because claim 10 is substantially equivalent to claim 3.
With respect to claim 11, it is rejected on grounds corresponding to above rejected claim 4, because claim 11 is substantially equivalent to claim 4.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 5, because claim 12 is substantially equivalent to claim 5.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 6, because claim 13 is substantially equivalent to claim 6.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 7, because claim 14 is substantially equivalent to claim 7.
With respect to claim 15, Wellman et al. discloses a computer program product for data subject assessment, the compute program product comprising a non-transitory computer-readable medium storing instructions translatable by a processor for:
defining a data subject, the defining performed by a data subject assessment service
responsive to an instruction from a user, the instruction received through a user interface of the
data subject assessment service, the data subject assessment service hosted on an artificial
intelligence (AI) platform, the AI platform operating in a cloud computing environment and
having a plurality of AI models (Paragraph 76 discloses processing device 104 may apply a document analysis model to the document data (e.g., classified document data). In some embodiments, processing device 104 may select a machine learning model from among a plurality of candidate machine learning models based on the classified document data);
creating a data subject project, wherein the creating the data subject project comprises
associating the data subject project with the plurality of AI models, each of the plurality of AI
models modeling a risk (Paragraph 91 discloses user device 300 may be a smartphone associated with a user, and any of interfaces 700A-700D may be displayed on user interface 340 (e.g., a display panel or a touchscreen). Any or all of these user interfaces may include data included in process 400 and/or 500 (e.g., a risk level, a model input value, etc.));
configuring the data subject project, the configuring the data subject project comprising
setting the risk at a risk level responsive to a setting received through the user interface of the
data subject assessment service (Paragraph 30 discloses examination assistant module 118 may be configured to carry out all or part of process 400, described below. In some embodiments, examination assistant module 118 may provide particularized analysis information and/or recommendations, which may be based on user input);
adding the data subject to the data subject project (Paragraph 72 discloses processing device 104 may have access to multiple extraction models that have particularized parameters for different types of documents or different entities (e.g., financial institutions), and may select an extraction model designated (e.g., in a look-up table) for a particular document type (e.g., a loan closing document) and/or entity (e.g., bank), which may have been identified through the document data classification (e.g., at step 504));
running the data subject project on a collection of documents to assess the data subject,
where the running the data subject comprises applying one or more of the plurality of AI models
to the collection of documents to assess the data subject (Paragraph 57 discloses statistical and/or machine learning models to process, scaling numerical values, normalizing data coming from different sources, creating new dynamic feature sets such as time lags or delta shifts between periods, determining simple moving averages or exponential moving averages, determining volatility or ranges in an input variable to describe time series data, and/or another data refinement operation);
generating a report on the subset of the data subject assessment results (Paragraph 83 discloses the API request may be a request for normalized data as a service, which may involve a request to APIs that provide processes and services for generating normalized and high-quality data originating from banking cores and document repository in a format for further analysis or modeling in a client application or platform (e.g., modeling, visualization, reporting of normalized, granular data, etc.)). Wellmann et al. does not disclose wherein accessing assessing the data subject comprises: determining how sets of metadata associated with the collection of documents relate to one another.
However, Argyros et al. teaches wherein accessing assessing the data subject comprises: determining how sets of metadata associated with the collection of documents relate to one another (Paragraph 79 discloses generate a set of metadata tags which are assigned to the risk identifiers that correspond to operational risk categories); and
based on relationships thus determined, generating data subject assessment results (Paragraph 87 discloses application parses the set of risk assessment documents to identify one or more portions of text corpus indicative of operational risk (block 504). In one embodiment, parsing of the set of risk assessment documents may be performed by an NLP algorithm. The application determines contextual features from the identified one or more portions of the text corpus (block 506). In one embodiment, the contextual features may refer to a regulation, an obligation, a topic, a subject, or a subject-matter domain).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wellmann et al. with Argyros et al. This would have facilitated risk assessment. See Argyros et al. Paragraph(s) 5-7.
Krishna et al. as modified by Thakur et al. does not searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results.
However, teaches searching for data subject relationships in the data subject assessment results, wherein the searching produces a subset of the data subject assessment results (Paragraph 48 discloses cybersecurity threats 902 associated with the extracted features and their interrelationships with system elements 54a).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Wellmann et al. and Argyros et al. with Thakur et al. This would have facilitated risk assessment. See Thakur et al. Paragraph(s) 3-6.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 2, because claim 16 is substantially equivalent to claim 2.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 4, because claim 17 is substantially equivalent to claim 4.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 5, because claim 18 is substantially equivalent to claim 5.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 6, because claim 19 is substantially equivalent to claim 6.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 7, because claim 20 is substantially equivalent to claim 7.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20220261711 is directed to SYSTEM AND METHOD FOR INTELLIGENT CONTRACT GUIDANCE: [0004] determining risk categories and calculating risk levels for clauses in contracts is disclosed. The system and method solve the problems discussed above by providing a comprehensive, autonomous contract risk analysis and assessment platform. The system applies artificial intelligence models that can autonomously generate insights, recommendations, summaries, and alerts for key contract clauses and milestones to users via a virtual assistant-based chatbot interface. The system provides highly accurate risk levels for various types of contract clauses, as well as identification of potentially problematic clause types, thereby minimizing the likelihood of a contract failing to serve the needs of a signing party.
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 NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.E.A/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154
Prosecution Insights