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 .
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 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Independent claims 1-18 are directed to a device, claim 19 is directed to a method, and claim 20 is directed to a system. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 19, and 20 recite determining an association between an entity and an individual, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including social activities. Independent claim 1 recites limitations, similarly recited in claims 19 and 20, including “determine an entity, determine, by scanning a plurality of media articles to extract references to the entity, an individual and a position associated with the entity, determine, by text link analysis to the references, a confirmation of the association between the entity and the individual; and in response to a query comprising the individual, an indication of the entity.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 3, 5-8, 10-13, and 16-18 further narrow the abstract idea identified in the independent claim and do not introduce further additional elements for consideration.
Dependent claims 2, 4, 9, and 14-15 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 19, and 20 do not integrate the judicial exception into a practical application. Independent claim 20 recites the additional elements of “one or more processors; and memory coupled with the one or more processors, the memory storing executable instructions that when executed by the one or more processors cause the one or more processors to effectuate operations comprising.” The independent claims recite the additional element of “accessing a database of known state-owned enterprises (SOEs) and non-governmental organizations (NGOs)” and “transmit, in response to a query comprising the individual, an indication of the entity.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
The independent claims further recite the additional element of “determine, by applying natural language processing (NLP) and text link analysis to the references, a confirmation of the association between the entity and the individual.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3, 5-8, 10-13, and 16-18 further narrow the abstract idea identified in the independent claim and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 2 introduces the additional element of “further configured to update, based on the confirmation, a profiled database with the association between the entity and the individual.” Dependent claim 4 introduces the additional element of “wherein the association between the entity and the individual are tagged in the profiled database.” Dependent claim 14 introduces the additional element of “wherein the media articles include comprise digital media.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 9 introduces the additional element of “further configured to determine, based on applying a machine learning model to historical data patterns, a likelihood of an ongoing association of the individual with the entity.” Dependent claim 15 introduces the additional element of “wherein the individual and the position associated with the entity are determined, at least in part, using a machine learning model.” The limitations regarding a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 19, and 20 do not comprise anything significantly more than the judicial exception. Independent claim 20 recites the additional elements of “one or more processors; and memory coupled with the one or more processors, the memory storing executable instructions that when executed by the one or more processors cause the one or more processors to effectuate operations comprising.” The independent claims recite the additional element of “accessing a database of known state-owned enterprises (SOEs) and non-governmental organizations (NGOs)” and “transmit, in response to a query comprising the individual, an indication of the entity.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The independent claims further recite the additional element of “determine, by applying natural language processing (NLP) and text link analysis to the references, a confirmation of the association between the entity and the individual.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 3, 5-8, 10-13, and 16-18 further narrow the abstract idea identified in the independent claim and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 2 introduces the additional element of “further configured to update, based on the confirmation, a profiled database with the association between the entity and the individual.” Dependent claim 4 introduces the additional element of “wherein the association between the entity and the individual are tagged in the profiled database.” Dependent claim 14 introduces the additional element of “wherein the media articles include comprise digital media.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 9 introduces the additional element of “further configured to determine, based on applying a machine learning model to historical data patterns, a likelihood of an ongoing association of the individual with the entity.” Dependent claim 15 introduces the additional element of “wherein the individual and the position associated with the entity are determined, at least in part, using a machine learning model.” The limitations regarding a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 8, 13-15, 17, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wales et al. (US 20250278792 A1).
Regarding claim 1, Wales anticipates one or more computing devices (Fig. 24), comprising one or more processors, configured to:
determine an entity by accessing a database of known state-owned enterprises (SOEs) and non-governmental organizations (NGOs) ([0076] teaches a service provider and broker database are created that are configured to store received data, wherein the received data includes documents from web sources including social media platforms, wherein the received data includes Form 5500 and Form 900 documents, wherein the data is merged in the database in order to remove the duplicated data, wherein the provider and broker identities are stored and corrected using NLP, wherein the system is then configured to store the information in a provider database and a broker database, wherein [0077] teaches the database includes information stored from web crawling including data points regarding a company via social media links, wherein [0061-0062] teach the remote server is configured to retrieve data from at least one government website, wherein data associated with contacts can be retrieved from government websites related to benefits broker data, employer data, service provider data, and more, wherein the GUI provides search functionality of the database on the remote server for employer data and service provider data; see also: [0016-0017, 0079]);
determine, by scanning a plurality of media articles to extract references to the entity, an individual and a position associated with the entity ([0061-0062] teach the remote server can retrieve data from at least one government website in order to analyze the benefits disclosure documents and normalize extracted data from the benefits disclosure documents using a natural language processing algorithm, wherein the server is configured to receive social media data from a web crawler that is configured to retrieve contact information from at least one social media profile for a contact and an URL for the contact based on the government website, wherein the GUI provides search functionality of the database on the remote server for employer data and service provider data, wherein the GUI is configured to display the contact information for the contact and a hyperlink to the URL for the contact, wherein the contact includes a contact for a broker, a contact for a broker, or a contact for a service provider, as well as in [0074] teaches extracting data of gatekeepers from the Form 550 and Form 990 documents and searching for the desired contacts at the company, wherein the retrieved and extracted data can be processed using natural language processing and storing the data in a database, wherein natural language processing is used to parse text in the documents in order to identify relevant information for plans, information about service providers associated with those plans, company names, brokers, administrators, and more, wherein [0098] teaches utilizing natural language processing algorithms to organize data collected by the web crawler in order to label information including people, organization, location, and other similar descriptors, as well as in [0103] teaches the software platform can combine the name and contact information of the gatekeepers and display the information via the GUI, wherein the platform can identify relevant individuals within a selected brokerage firm based on the prospective client and brokerage office location, wherein the search can search across contacts including a job role, job title, location, years at the company, and more; see also: [0079-0080]);
determine, by applying natural language processing (NLP) and text link analysis to the references, a confirmation of the association between the entity and the individual ([0061-0062] teach the remote server can retrieve data from at least one government website in order to analyze the benefits disclosure documents and normalize extracted data from the benefits disclosure documents using a natural language processing algorithm, wherein the server is configured to receive social media data from a web crawler that is configured to retrieve contact information from at least one social media profile for a contact and an URL for the contact based on the government website, wherein the GUI provides search functionality of the database on the remote server for employer data and service provider data, wherein the GUI is configured to display the contact information for the contact and a hyperlink to the URL for the contact, wherein the contact includes a contact for a broker, a contact for a broker, or a contact for a service provider, as well as in [0074] teaches extracting data of gatekeepers from the Form 550 and Form 990 documents and searching for the desired contacts at the company, wherein the retrieved and extracted data can be processed using natural language processing and storing the data in a database, wherein natural language processing is used to parse text in the documents in order to identify relevant information for plans, information about service providers associated with those plans, company names, brokers, administrators, and more, wherein [0076] teaches a service provider and broker database are created that are configured to store received data, wherein the received data includes documents from web sources including social media platforms, wherein the received data includes Form 5500 and Form 900 documents, wherein the data is merged in the database in order to remove the duplicated data, wherein the provider and broker identities are stored and corrected using NLP, wherein the system is then configured to store the information in a provider database and a broker database, wherein [0098] teaches utilizing natural language processing algorithms to organize data collected by the web crawler in order to label information including people, organization, location, and other similar descriptors, as well as in [0103] teaches the software platform can combine the name and contact information of the gatekeepers and display the information via the GUI, wherein the platform can identify relevant individuals within a selected brokerage firm based on the prospective client and brokerage office location, wherein the search can search across contacts including a job role, job title, location, years at the company, and more; see also: [0079-0080]); and
transmit, in response to a query comprising the individual, an indication of the entity ([0016] teaches displaying search results for an employer including a contact for the employer, as well as in [0061-0062] teach the remote server is configured to retrieve data from at least one government website, wherein data associated with contacts can be retrieved from government websites related to benefits broker data, employer data, service provider data, and more, wherein the GUI provides search functionality of the database on the remote server for employer data and service provider data, wherein the GUI is configured to display the contact information for the contact and a hyperlink to the URL for the contact, wherein the contact includes a contact for a broker, a contact for a broker, or a contact for a service provider, wherein [0074] teaches the system is configured to receive a selection of a broker, wherein the system is configured to display a broker’s contact information, such as phone number, email, linked in, and office address, wherein the system is configured to display the broker’s entire client list and filter the broker’s client list by various factors, wherein the system is configured to construct and display client lists for specific service providers using machine learning, as well as in [0103] teaches the software platform can combine the name and contact information of the gatekeepers and display the information via the GUI, wherein the platform can identify relevant individuals within a selected brokerage firm based on the prospective client and brokerage office location, wherein the search can search across contacts including a job role, job title, location, years at the company, and more; see also: [0017, 0079]).
Regarding claims 19 and 20, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 19, Wales anticipates a method performed by one or more computing devices, the method comprising ([0126-0127] teach a memory and processor that perform the methodologies described; see also: Fig. 24). Regarding claim 20, Wales anticipates a system comprising: one or more processors (Fig. 24 and [0123-0127] teach a processor); and memory coupled with the one or more processors (Fig. 24 and [0123-0127] teach a processor coupled to a memory that stores sets of instructions that can be executed by the processor), the memory storing executable instructions that when executed by the one or more processors cause the one or more processors to effectuate operations comprising. Accordingly, claims 19 and 20 are rejected as being anticipated by Wales.
Regarding claim 2, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates further configured to update, based on the confirmation, a profiled database with the association between the entity and the individual ([0074] teaches the system is configured to receive a selection of a broker, wherein the system is configured to display a broker’s contact information, such as phone number, email, linked in, and office address, wherein the system is configured to display the broker’s entire client list and filter the broker’s client list by various factors, wherein the system is configured to construct and display client lists for specific service providers using machine learning, wherein the system can perform real-time data extraction with third-party data sources in order to update the data displayed via the GUI in real time, wherein the system can utilize a checker in order to determine valid email addresses for the desired contacts at the company, as well as in [0077] teaches automatically matching a web profile to a company listed in the database of the software platform, wherein the machine learning module matches information gathered from the web crawler retrieval with information already entered in the database for the company, confirming that the information gathered concerns the correct company, wherein the data is then able to be added to the gathered information for the company from the web crawler to the database, as well as in [0078] teaches when a job change is detected by the web crawler, a notification is automatically transmitted to one or more profiles monitoring the change, wherein the platform automatically utilizes the APIs to retrieve the new contact information for the new or changed contact, wherein [0087] teaches enrichments, or updates, are automatically pushed to the CRM profile associated with the user profile when the profile changes or information connected to the CRM profile changes; see also: [0097, 0116]).
Regarding claim 3, Wales anticipates all the limitations of claim 2 above.
Wales further anticipates further configured to trigger an alert based on a change in a status of the association between the entity and the individual ([0077] teaches automatically matching a web profile to a company listed in the database of the software platform, wherein the machine learning module matches information gathered from the web crawler retrieval with information already entered in the database for the company, confirming that the information gathered concerns the correct company, wherein the data is then able to be added to the gathered information for the company from the web crawler to the database, as well as in [0078] teaches when a job change is detected by the web crawler, a notification is automatically transmitted to one or more profiles monitoring the change, wherein the platform automatically utilizes the APIs to retrieve the new contact information for the new or changed contact, wherein [0087] teaches enrichments, or updates, are automatically pushed to the CRM profile associated with the user profile when the profile changes or information connected to the CRM profile changes; see also: [0074, 0097, 0116]).
Regarding claim 4, Wales anticipates all the limitations of claim 2 above.
Wales further anticipates wherein the association between the entity and the individual are tagged in the profiled database ([0079] teaches the web crawler is operable to transfer the data to the artificial intelligence engine, where the third party data is tagged and aggregated, wherein [0081] teaches generating the best name to tag the group being the group service provider that identifies that are the same but have minor differences, wherein [0085] teaches syncing information from a CRM profile associated with the user profile in the database, wherein [0093] teaches generating the best tag for the group and storing this information in the database; see also: [0080, 0083, 0098]).
Regarding claim 5, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates wherein the indication of the entity comprises the position associated with the entity ([0103] teaches the software platform can combine the name and contact information of the gatekeepers and display the information via the GUI, wherein the platform can identify relevant individuals within a selected brokerage firm based on the prospective client and brokerage office location, wherein the search can search across contacts including a job role, job title, location, years at the company, and more; see also: [0078-0080]).
Regarding claim 8, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates wherein the entity and the position are referenced by the plurality of media articles in a same context ([0074] teaches the system is configured to receive a selection of a broker, wherein the system is configured to display a broker’s contact information, such as phone number, email, linked in, and office address, wherein the system is configured to display the broker’s entire client list and filter the broker’s client list by various factors, wherein the system is configured to construct and display client lists for specific service providers using machine learning, wherein the system can perform real-time data extraction with third-party data sources in order to update the data displayed via the GUI in real time, wherein the system can utilize a checker in order to determine valid email addresses for the desired contacts at the company, as well as in [0077] teaches automatically matching a web profile to a company listed in the database of the software platform, wherein the machine learning module matches information gathered from the web crawler retrieval with information already entered in the database for the company, confirming that the information gathered concerns the correct company, wherein the data is then able to be added to the gathered information for the company from the web crawler to the database, as well as in [0078] teaches when a job change is detected by the web crawler, a notification is automatically transmitted to one or more profiles monitoring the change, wherein the platform automatically utilizes the APIs to retrieve the new contact information for the new or changed contact, wherein [0087] teaches enrichments, or updates, are automatically pushed to the CRM profile associated with the user profile when the profile changes or information connected to the CRM profile changes, wherein [0131] teaches a web browser extension that provides a contextual information about a contact, service provider, benefits broker, or company automatically upon recognition of a name of a contact, service provider, benefits broker, or company on a web page; see also: [0097, 0116]).
Regarding claim 13, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates further configured to determine a category for the association between the entity and the individual, wherein the indication of the entity comprises the category ([0103] teaches the software platform can combine the name and contact information of the gatekeepers and display the information via the GUI, wherein the platform can identify relevant individuals within a selected brokerage firm based on the prospective client and brokerage office location, wherein the search can search across contacts including a job role, job title, location, years at the company, and more, wherein the job role field of the search is highly customized and acts to group thousands of job title permutations into smaller, meaningful categories; see also: [0078-0080]).
Regarding claim 14, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates wherein the media articles include comprise digital media ([0076] teaches a service provider and broker database are created that are configured to store received data, wherein the received data includes documents from web sources including social media platforms, wherein the received data includes Form 5500 and Form 900 documents, as well as in [0061-0062] teach the remote server can retrieve data from at least one government website in order to analyze the benefits disclosure documents and normalize extracted data from the benefits disclosure documents using a natural language processing algorithm, wherein the server is configured to receive social media data from a web crawler that is configured to retrieve contact information from at least one social media profile for a contact and an URL for the contact based on the government website, and wherein [0143] teaches the computing devices are operable to communicate communication media through a network; see also: [0098, 0103, 0127]).
Regarding claim 15, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates wherein the individual and the position associated with the entity are determined, at least in part, using a machine learning model ([0074] teaches the system is configured to receive a selection of a broker, wherein the system is configured to display a broker’s contact information, such as phone number, email, linked in, and office address, wherein the system is configured to display the broker’s entire client list and filter the broker’s client list by various factors, wherein the system is configured to construct and display client lists for specific service providers using machine learning, wherein the system can perform real-time data extraction with third-party data sources in order to update the data displayed via the GUI in real time, wherein the system can utilize a checker in order to determine valid email addresses for the desired contacts at the company, as well as in [0077] teaches automatically matching a web profile to a company listed in the database of the software platform, wherein the machine learning module matches information gathered from the web crawler retrieval with information already entered in the database for the company, confirming that the information gathered concerns the correct company, wherein the data is then able to be added to the gathered information for the company from the web crawler to the database, as well as in [0078] teaches when a job change is detected by the web crawler, a notification is automatically transmitted to one or more profiles monitoring the change, wherein the platform automatically utilizes the APIs to retrieve the new contact information for the new or changed contact; see also: [0081, 0084, 0103]).
Regarding claim 17, Wales anticipates all the limitations of claim 1 above.
Wales further anticipates wherein updates to the database are automatically performed at in real-time as new information becomes available ([0074] teaches the system is configured to receive a selection of a broker, wherein the system is configured to display a broker’s contact information, such as phone number, email, linked in, and office address, wherein the system is configured to display the broker’s entire client list and filter the broker’s client list by various factors, wherein the system is configured to construct and display client lists for specific service providers using machine learning, wherein the system can perform real-time data extraction with third-party data sources in order to update the data displayed via the GUI in real time, wherein the system can utilize a checker in order to determine valid email addresses for the desired contacts at the company, as well as in [0077] teaches automatically matching a web profile to a company listed in the database of the software platform, wherein the machine learning module matches information gathered from the web crawler retrieval with information already entered in the database for the company, confirming that the information gathered concerns the correct company, wherein the data is then able to be added to the gathered information for the company from the web crawler to the database, as well as in [0078] teaches when a job change is detected by the web crawler, a notification is automatically transmitted to one or more profiles monitoring the change, wherein the platform automatically utilizes the APIs to retrieve the new contact information for the new or changed contact, wherein [0087] teaches enrichments, or updates, are automatically pushed to the CRM profile associated with the user profile when the profile changes or information connected to the CRM profile changes; see also: [0097, 0116]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wales et al. (US 20250278792 A1) in view of Pasumarthi et al. (US 20250077742 A1).
Regarding claim 6, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach wherein the query is associated with measuring compliance and risk.
From the same or similar field of endeavor, Pasumarthi teaches wherein the query is associated with measuring compliance and risk ([0036] teaches the model is built upon entity characteristic data including internal and external data related to regulatory information, wherein the entity risk data may be periodically provided, wherein the entity risk data includes publicly available regulatory and legal data, wherein the risk data API can screen for information available for legal and regulatory risk category information, wherein the legal and regulatory data may provide information that describes entity activity that relates to the regulatory and compliance obligations that help to uphold and relate to lawful legal requirements, wherein the risk data may be segmented and classified for a given entity as unknown, low, medium, and high risks, as well as in [0037] teaches retrieving details about a particular entity and generating a risk classification for that entity, and wherein [0075] teaches the historical and current data from the risk API can be extracted utilizing NLP; see also: [0035, 0045]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Pasumarthi to include wherein the query is associated with measuring compliance and risk. One would have been motivated to do so in order to provide improvements to facilitate entity provisioning, thus avoiding the increasingly difficult selection for organizations (Pasumarthi, [0001, 0027]). By incorporating the teachings of Pasumarthi, one would have been able to predict desired entity characteristics based on a risk classification for the entity (Pasumarthi, [0037]).
Regarding claim 16, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach further configured to integrate with external compliance systems for collaborative risk management.
From the same or similar field of endeavor, Pasumarthi teaches further configured to integrate with external compliance systems for collaborative risk management ([0036] teaches the AI/ML model is built upon the entity characteristic data that may include internal data and external data related to regulatory information, wherein the model includes entity risk data in their analysis of an entity, wherein the third-party entity risk data may include publicly available regulatory and legal data, wherein the legal and regulatory data may provide information that describes entity activity that relates to the regulatory and compliance obligations that help to uphold and relate to lawful legal requirements, wherein the risk categories can be aggregated to generate risk scores; see also: [0037, 0056, 0075]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Pasumarthi to include further configured to integrate with external compliance systems for collaborative risk management. One would have been motivated to do so in order to provide improvements to facilitate entity provisioning, thus avoiding the increasingly difficult selection for organizations (Pasumarthi, [0001, 0027]). By incorporating the teachings of Pasumarthi, one would have been able to predict desired entity characteristics based on a risk classification for the entity (Pasumarthi, [0037]).
Claim(s) 7, 9, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wales et al. (US 20250278792 A1) in view of Eidelman et al. (US 20230214754 A1).
Regarding claim 7, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach wherein the database of known SOEs and NGOs is compiled from a plurality of public and proprietary sources.
From the same or similar field of endeavor, Eidelman teaches wherein the database of known SOEs and NGOs is compiled from a plurality of public and proprietary sources ([0093] teaches generating and analyzing policymaker and organizational issue graphs, wherein the system can gather data from various sources including structured and unstructured data, wherein the data can be gathered from internet sources or otherwise collected from publicly available sources, wherein the data accessed may also include proprietary data that may be collected from a user or organization, as well as in [0128] teaches proprietary information includes information with limited or restricted accessibility, wherein [0464] teaches those involved in the relationship determinations include NGOs, trade associations, and more, wherein [0005] teaches combining data related to many governmental jurisdictions in order to analyze collected data with proprietary user provided data in order to calculate and maintain complex relationships, wherein [0180] teaches the accessing proprietary information related to organizations, and wherein [0178] teaches an organization includes a corporation, government, or legally recognizable group; see also: [0181, 0202-0204]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Eidelman to include wherein the database of known SOEs and NGOs is compiled from a plurality of public and proprietary sources. One would have been motivated to do so in order to calculate and maintain complex relationships between structured and unstructured data (Eidelman, [0005]). By incorporating the teachings of Eidelman, one would have been able to analyze proprietary information to gather valuable insights into the influence various entities may have in relation to a policy or issue (Eidelman, [0093]).
Regarding claim 9, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach further configured to determine, based on applying a machine learning model to historical data patterns, a likelihood of an ongoing association of the individual with the entity.
From the same or similar field of endeavor, Eidelman teaches further configured to determine, based on applying a machine learning model to historical data patterns, a likelihood of an ongoing association of the individual with the entity ([0105] teaches the machine learning analysis can generate a prediction or likelihood including of the strength of a relationship between entities, wherein in the context of a future event, the prediction refers to an outcome, wherein [0144] teaches determining a likelihood of predictions that indicates the probability of a future event, wherein [0378] teaches the relationship can indicate a similar interest pattern and two organization nodes that are similar, wherein [0381] teaches the machine learning model is trained to extract relationships from unstructured data, wherein [0432] teaches generating a likelihood score indicating the relationship between two nodes; see also: [0461, 0477]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of further configured to determine, based on applying a machine learning model to historical data patterns, a likelihood of an ongoing association of the individual with the entity. One would have been motivated to do so in order to calculate and maintain complex relationships between structured and unstructured data (Eidelman, [0005]). By incorporating the teachings of Eidelman, one would have been able to analyze proprietary information to gather valuable insights into the influence various entities may have in relation to a policy or issue (Eidelman, [0093]).
Regarding claim 11, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach wherein the text link analysis comprises determining a relational graph comprising a connection between the entity and the individual.
From the same or similar field of endeavor, Eidelman teaches wherein the text link analysis comprises determining a relational graph comprising a connection between the entity and the individual ([0001] teaches generating policy, policymaker, and organizational entities and relationships through the construction of issue-based knowledge graphs, wherein the system can analyze data related to legislative, regulatory, and judicial processes to compute entities and their relationships in a policy intelligence platform, wherein [0008] teaches a graph model that includes a plurality of nodes representing individuals associated with an organization and links representing relationships between first nodes and second nodes, wherein [0375] teaches nodes corresponding to persons, organizations, or events by computing a graph based embedding indicating a type of relationship, and wherein [0178] teaches an organization includes a corporation, government, or legally recognizable group; see also: [0364, 0372, 0399]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Eidelman to include wherein the text link analysis comprises determining a relational graph comprising a connection between the entity and the individual. One would have been motivated to do so in order to calculate and maintain complex relationships between structured and unstructured data (Eidelman, [0005]). By incorporating the teachings of Eidelman, one would have been able to analyze proprietary information to gather valuable insights into the influence various entities may have in relation to a policy or issue (Eidelman, [0093]).
Regarding claim 18, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach further configured to determine potential conflicts of interest associated with the individual and the entity based on historical associations and current associations.
From the same or similar field of endeavor, Eidelman teaches further configured to determine potential conflicts of interest associated with the individual and the entity based on historical associations and current associations ([0220] teaches the feedback and collaboration may change the calculation of an existing system defined feature, wherein the feedback may generate new features for the model, wherein the feature may be associated with outcome conflicts with any set of features derived by the model, or predicted outcome, wherein the feature may include a correlation, wherein [0464] teaches calculating metrics for other entities involved in policy including NGOs, wherein an organization’s history of offering comments on regulations or an individual’s interactions to generate additional relations and weights between organization and individual nodes in the relationship graph; see also: [0461]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Eidelman to include further configured to determine potential conflicts of interest associated with the individual and the entity based on historical associations and current associations. One would have been motivated to do so in order to calculate and maintain complex relationships between structured and unstructured data (Eidelman, [0005]). By incorporating the teachings of Eidelman, one would have been able to analyze proprietary information to gather valuable insights into the influence various entities may have in relation to a policy or issue (Eidelman, [0093]).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wales et al. (US 20250278792 A1) in view of Shukla et al. (US 20170286981 A1).
Regarding claim 10, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach further configured to determine a geopolitical risk factor based on a locations associated with the entity or the individual, wherein the indication of the entity is further based on the geopolitical risk factor.
From the same or similar field of endeavor, Shukla teaches further configured to determine a geopolitical risk factor based on a locations associated with the entity, wherein the indication of the entity is further based on the geopolitical risk factor ([0021] teaches the market data can include commercial risk information and geopolitical risk information, wherein [0022] teaches providing the market data to the risk server to calculate risk scores including risk factor scores related to geopolitical risk, wherein [0031] teaches the risk score can be associated with a location desirability, and wherein [0029] teaches generating geopolitical risk category scores can be calculated that allow companies to improve business performance; see also: [0019, 0025]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Shukla to include further configured to determine a geopolitical risk factor based on a locations associated with the entity or the individual, wherein the indication of the entity is further based on the geopolitical risk factor. One would have been motivated to do so in order to calculate risk scores in order to allow companies to improve business performance (Shukla, [0029]). By incorporating the teachings of Shukla, one would have been able to accurately make informed decisions regarding risk and location desirability (Shukla, [0019]).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wales et al. (US 20250278792 A1) in view of Rosenberg et al. (US 20210150432 A1).
Regarding claim 12, Wales anticipates all the limitations of claim 1 above.
However, Wales does not explicitly teach wherein the NLP further comprises parsing the references for regulatory terms indicating compliance risks associated with the entity.
From the same or similar field of endeavor, Rosenberg teaches wherein the NLP further comprises parsing the references for regulatory terms indicating compliance risks associated with the entity ([0005] teaches a risk assessment system that can derive up to date risks scores using a natural language processor that, for each of the input events, scrapes the event, classifies the event, and for each keyword found in the event calculates a NLP suggested sub-dimension score associated with the keyword based on the word score, as well as in [0033] teaches a natural language processor scraps an event and finds a keyword combination related to legislative reform related to a country, wherein the word score entries are sent to the scoring widget for the event in order for the word score to be aggregated into the dimension scores for policy environment and state capacity; see also: [0008]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wales to incorporate the teachings of Rosenberg to include wherein the NLP further comprises parsing the references for regulatory terms indicating compliance risks associated with the entity. One would have been motivated to do so in order to calculate comprehensive risk measures when it increasingly expected that geopolitical instability will have an effect on global business (Rosenberg, [0002-0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tremblay et al. (US 20220343250 A1) discloses identifying content based on relationships between entities
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625