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 .
Notice to Applicant
Claims 1- 24 have been examined in this application. This communication is the first action on the merits. No Information Disclosure Statement (IDS) has been filed to date.
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- 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-24 are directed to document management.
Claim 1 recites a method for document management, and Claim 13 recites a system document management, which include receiving data associated with a multi-party agreement; generating one or more user profiles based on the received data; generating a risk score associated with the multi-party agreement based on output; determining one or more risk mitigation actions based on the risk score and the output; Implementing at least one of the one or more risk mitigation actions to modify the multi-party agreement; monitoring user interaction with the modified multi-party agreement; and automatically adjusting one or more operating parameters of the multi-party document management platform.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions. The recitation of “processor”, “platform”, “system”, and “memory”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “processor”, “platform”, “system”, and “memory” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1 and claim 13 recite using one or more artificial intelligence (AI) analysis techniques. The specification discloses the semantic analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of artificial intelligence analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the natural language processing is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in document management.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “processor”, “platform”, “system”, and “memory” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-12, and 14-24 recite wherein the one or more risk mitigation actions comprise at least one from among the group consisting of: modifying one or more clauses in the multi-party agreement; adjusting access permissions for one or more parties to the multi-party agreement or to one or more accounts related to the multi-party agreement; and implementing additional authentication requirements for high-risk operations; wherein automatically adjusting the one or more operating parameters comprises at least one from among the group consisting of: modifying document approval workflows; adjusting data encryption levels for stored documents; and updating user authentication protocols; generating a recommendation for improving the risk score; presenting the recommendation to at least one party associated with the multi-party agreement; receiving user feedback regarding the recommendation; and utilizing the user feedback; wherein the feedback includes a combination of accepted and rejected recommendations; wherein monitoring user interaction comprises: tracking user actions related to viewing, editing, or approving the modified multi-party agreement; and analyzing patterns in the tracked user actions to identify potential risks and automated actions to initiate responsive to the potential risks; wherein the automated actions include at least one from among the group consisting of: generating alerts or notices; and initiating or modifying one or more workflows related to agreement modification, agreement termination, generation of amendment, agreement notice of control, or data processing; generating, by the one or more processors, a portfolio-level risk assessment for a group of multi-party agreements; and implementing portfolio-wide risk mitigation actions based on the portfolio-level risk assessment; wherein the data associated with the multi-party agreement is received from multiple sources, including user input and third-party systems, and normalizing the received data prior to generating the one or more user profiles; continuously monitoring, by the one or more processors, for changes in external factors affecting the risk score; and automatically initiating a re-assessment of the risk score when a change in external factors is detected; wherein automatically adjusting the one or more operating parameters comprises: identifying inefficiencies in document processing workflows based on the updated Al models; and modifying the document processing workflows to reduce processing time or resource utilization.; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, and 13. Regarding Claims,4-5, 9, 11 and 17, and the additional elements of “processor” and “user interface” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 5,12, 17 and claim 24 and the additional element of AI model - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea..
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 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.
Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Wichern et al., US Publication No. 20200410617A1, [hereinafter Wichern], in view of Shah et al., US Publication No. 20250307749A1, [hereinafter Shah].
Regarding Claim 1,
Wichern teaches
A computer-implemented method comprising: receiving, by one or more processors, data associated with a multi-party agreement (Wichern Par. 32; Par. 4-5 –“The present disclosure relates generally to systems and methods that can automatically generate agreements and provisions between two or more parties. In one example, the method includes receiving by a processing element a plurality of entity characteristics corresponding to at least one of the two or more parties, calculation by the processing element of two or more values of the entity characteristics, utilizing by the processing element a calculated score based on the values of two or more characteristics, and the weighting of each characteristic based on a statistical model of the influence of each characteristic on optimally generated agreements and provisions between two or more parties, to identify a first agreement from two or more agreements, and outputting by the processing element the first agreement to a first user device corresponding to at least one of the two or more parties.”) ;
generating, by the one or more processors, one or more user profiles based on the received data (Wichern Par. 61- “FIG. 8 illustrates a specific example of utilizing the entity information to generate agreements or provisions. The method 300 may begin with operation 302 and the entity characteristics are determined. For example, as described above the server 106 may retrieve the party characteristics from information entered via the first user device 102 (either when generating a profile or during additional data entry points, e.g., supplemental questions as part of a new agreement), from data collection of the entity's interactions within the system 100 and other related tracking elements, and/or from third party sources, such as social media, the Internet, new sources, and the like. The entity information may also include feedback or engagement information with the system 100. For example, over time the system 100 may track a particular entity's performance and interactions with other parties within the system 100 and use this information as a separate characteristics that can be evaluated to improve or lower an entity's score.”);
executing, by the one or more processors, one or more artificial intelligence (Al) models using the one or more user profiles as input (Wichern Par. 45- “In some instances, the questions presented to the users may be dynamically generated based on previous answers. For example, a machine learning algorithm including a natural language processor, can analyze received answers and characteristics to determine follow-up or other related questions to be presented to the user, e.g., if the server receives a first answer to question A, then the first answer will drive the server to output question G, rather than question B. By dynamically modifying the questions based on previous questions and answers, the system can tailor the entity characteristics received to better questions that will be useful in generating the agreements and contracts”);
generating, by the one or more processors, a risk score associated with the multi-party agreement based on output from the one or more Al models (Wichern Par. 63- “In another example, the entity characteristic 332 can be a risk class or weighted risk value. A numeric or qualitative ranking can be assigned to a risk class, such as “high”, “medium”, “low” and/or a value associated with a likely risk in a particular category, e.g., growth risk, legal risk, financial risk, and the like. The values or scores that are applied to the characteristic categories or buckets may be varied dynamically by the system 100 as new information is input, such as feedback into the system, and the like. Additionally, while the scores are assigned per characteristic, in some instances, the scores may be dependent on multiple characteristics, e.g., a first characteristic value may receive a first score value, unless a second characteristic value is below or above a threshold and then a second score value may be applied to the same characteristic.; Par. 76”);
determining, by the one or more processors, one or more risk mitigation actions based on the risk score and the output from the one or more Al models (Wichern Par. 69- “In instances where the score is used to select provisions, operations 306 and operation 308 may be repeated for the desired number of provisions in the agreement and the operation 308 may then also include a “building” of the agreement by combining the selected provisions or provision terms into a form template or the like. For example, if an entity has a high score in a risk class or category, certain contract provisions providing indemnity for such risks can be included in the contract, e.g. above a particular value of risk, a risk reduction, indemnity, or other provision may be selected for inclusion in the agreement.”);
implementing, by the one or more processors, at least one of the one or more risk mitigation actions to modify the multi-party agreement (Wichern Par. 77-78- As mentioned, in some implementations the system 100 and platform may utilize feedback to dynamically update agreement provisions, values, scoring, weighting, and analyzed characteristics. FIG. 10 illustrates a flow chart for leveraging or incorporating feedback into the dynamic agreement generation platform. With reference to FIG. 10, the method 400 begins with operation 402 and the server 106 generates one or more tables or other reference structures that correlate agreement provisions of finally executed agreements to party or entity characteristics of the contracting parties. The method 400 also includes operation 404 where the server 106 further analyzes or identifies negotiated or otherwise varied provisions within the agreements, e.g., the server 106 compares the agreement as originally delivered to the executed copy. The server 106 then stores in a memory location the executed agreement characteristics along with the party characteristics of the parties that were bound by the executed agreement. Using the variations and party characteristics, the server 106 analyzes the agreement to detect trends or other statistically significant patterns. Similarly, the server 106 may also in operation 410 consider and analyze external elements as well. The external elements include trends in the marketplace, variations in technology, company or entity information (e.g., updates in financial status, revenue, growth, and the like), types of venture capital money investments with the startup, and the like. External elements can also include changes of law or policy that may affect agreements generated by the system.”);
monitoring, by the one or more processors, user interaction with the modified multi-party agreement (WI churn Par. 26- “In some embodiments, the system may also utilize dynamic feedback to update the contract or other document being prepared. For example, the system may monitor the length of time that a user spends on one or more provisions (e.g., before receiving an input accepting a provision, time before scrolling down on the webpage or to the next provision, etc.) and uses that feedback to dynamically update further provisions or terms within the contract or document. In this manner, the party's engagement within the system during the generation operation may also be used to modify the contract or document.”);
updating, by the one or more processors, the one or more Al models based on the monitored user interaction (WI churn Par. 46- In some instances, the questions presented to the users may be dynamically generated based on previous answers. For example, a machine learning algorithm including a natural language processor, can analyze received answers and characteristics to determine follow-up or other related questions to be presented to the user, e.g., if the server receives a first answer to question A, then the first answer will drive the server to output question G, rather than question B. By dynamically modifying the questions based on previous questions and answers, the system can tailor the entity characteristics received to better questions that will be useful in generating the agreements and contracts.”; Par. 60; Par. 64; Par. 76) ;
Wichern teaches document management analysis and the feature is expounded upon by Shah:
and automatically adjusting, by the one or more processors, one or more operating parameters of the multi-party document management platform based on the updated Al models. (Shah Par. 169-“ In some embodiments, the compliance data 1210a and/or non-compliance data 1210b may be sent back to the obligation management engine 150 for processing and/or any other use. For example, the data 1210 may be used to update identification of entities in the documents 202. It may also be used to update risk scores generated by the risk assignment engine 206. Further, the rules generation engine 210 may use the data 1210 for updating rules. Moreover, the compliance/non-compliance data may be used to train and/or retrain and/or refresh train one or more ML model(s) 208. As can be understood, the data 1210 may be used for any other purposes.
Wichern and Shah are directed to document management analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wichern, as taught by Shah by utilizing additional model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wichern with the motivation of improving the user experience and make search more efficient (Shah Par. 45).
Regarding Claim 2 and Claim 14, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
wherein the one or more risk mitigation actions comprise at least one from among the group consisting of :modifying one or more clauses in the multi-party agreement; adjusting access permissions for one or more parties to the multi-party agreement or to one or more accounts related to the multi-party agreement; and implementing additional authentication requirements for high-risk operations ( Wichern Par. 25-26- “In some embodiments, the system may also utilize dynamic feedback to update the contract or other document being prepared. For example, the system may monitor the length of time that a user spends on one or more provisions (e.g., before receiving an input accepting a provision, time before scrolling down on the webpage or to the next provision, etc.) and uses that feedback to dynamically update further provisions or terms within the contract or document. In this manner, the party's engagement within the system during the generation operation may also be used to modify the contract or document.”)
Regarding Claim 3 and Claim 15, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
wherein automatically adjusting the one or more operating parameters comprises at least one from among the group consisting of: modifying document approval workflows; adjusting data encryption levels for stored documents; and updating user authentication protocols. (Wichern Par.73- “n other examples, the server may analyze the changes via a natural language processor or other similar algorithm that compares the changed words, meaning, and/or grammatical rules to determine if the changes result are of form rather than substance or under a threshold value and then may be approved. As another example, the changes may be compared to stored acceptable changes and then if the changes matches a previously stored redline (or other tracked changes format showing revisions), the redline may be accepted. In yet another example, if changes are requested, the server may reject the changes, but automatically pull the next step lower provision stored in memory. For example, if there are three template provisions stored, with three different values assigned (1 to 3) and the party is served the provision 3, which is the most restrictive and provides changes to the provision, the system may disregard both provision 3 and the changes and transmit the next stored provision, provision 2, which is a known provision already determined to be acceptable by the system. In this example the system may not need to evaluate actual changes input by a party, but rather use the fact that there are changes in a provision to serve up a new provision.”)
Regarding Claim 3 and Claim 15, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
wherein automatically adjusting the one or more operating parameters comprises at least one from among the group consisting of: modifying document approval workflows; adjusting data encryption levels for stored documents; and updating user authentication protocols. (Wichern Par.73- “n other examples, the server may analyze the changes via a natural language processor or other similar algorithm that compares the changed words, meaning, and/or grammatical rules to determine if the changes result are of form rather than substance or under a threshold value and then may be approved. As another example, the changes may be compared to stored acceptable changes and then if the changes matches a previously stored redline (or other tracked changes format showing revisions), the redline may be accepted. In yet another example, if changes are requested, the server may reject the changes, but automatically pull the next step lower provision stored in memory. For example, if there are three template provisions stored, with three different values assigned (1 to 3) and the party is served the provision 3, which is the most restrictive and provides changes to the provision, the system may disregard both provision 3 and the changes and transmit the next stored provision, provision 2, which is a known provision already determined to be acceptable by the system. In this example the system may not need to evaluate actual changes input by a party, but rather use the fact that there are changes in a provision to serve up a new provision.”)
Regarding Claim 4 and Claim 16, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
Wichern teaches document management analysis and the feature is expounded upon by Shah:
further comprising: generating, by the one or more processors, a recommendation for improving the risk score; and presenting, via an interactive graphical user interface (GUI), the recommendation to at least one party associated with the multi-party agreement. (Shah Par. 53; Par. 93 Par.105- “In general, the data collector 402 may collect data 410 from one or more data sources to use as training data for the ML model 330. The data collector 402 may collect different types of data 410, such as, text information, audio information, image information, video information, graphic information, and so forth. The model trainer 404 may receive as input the collected data and uses a portion of the collected data as test data for an AI/ML algorithm to train the ML model 330. The model evaluator 406 may evaluate and improve the trained ML model 330 using a portion of the collected data as test data to test the ML model 330. The model evaluator 406 may also use feedback information from the deployed ML model 330. The model inferencer 408 may implement the trained ML model 330 to receive as input new unseen data, generate one or more inferences on the new data, and output a result such as an alert, a recommendation or other post-solution activity.”; Par. 154-156)
Wichern and Shah are directed to document management analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wichern, as taught by Shah by utilizing additional model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wichern with the motivation of improving the user experience and make search more efficient (Shah Par. 45).
Regarding Claim 5 and Claim 17, Wichern in view Shah teach The method of claim 4,… and The system of claim 16,…
Wichern teaches document management analysis and the feature is expounded upon by Shah:
further comprising: receiving, via the user interface, user feedback regarding the recommendation; and utilizing the user feedback to further update the one or more AI models by generating a new training data set that includes a combination of a prior training data set and the feedback, and re-training the one or more AI models according to the new training data set.. (Shah Par.158- “I In some embodiments, risk scores may be updated based on availability of new data, a feedback received from one or more entities, users of user devices 216, etc. The data may include data related to recent compliance/noncompliance with an obligation by an entity and/or any other entity, identification of new entity, generation of one or more rules by the rules generation engine 210, and/or any other factors. Once new risk scores are determined, the rules generation engine 210 may be configured to update one or more of its rules that may be generated. The risk scores may be updated periodically, continuously, and/or at any desired intervals.”; Par. 169)
Wichern and Shah are directed to document management analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wichern, as taught by Shah by utilizing additional model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wichern with the motivation of improving the user experience and make search more efficient (Shah Par. 45).
Regarding Claim 6 and Claim 18, Wichern in view Shah teach The method of claim 5,… and The system of claim 17,…
wherein the feedback includes a combination of accepted and rejected recommendations. (Wichern Par.73- “In other examples, the server may analyze the changes via a natural language processor or other similar algorithm that compares the changed words, meaning, and/or grammatical rules to determine if the changes result are of form rather than substance or under a threshold value and then may be approved. As another example, the changes may be compared to stored acceptable changes and then if the changes matches a previously stored redline (or other tracked changes format showing revisions), the redline may be accepted. In yet another example, if changes are requested, the server may reject the changes, but automatically pull the next step lower provision stored in memory. For example, if there are three template provisions stored, with three different values assigned (1 to 3) and the party is served the provision 3, which is the most restrictive and provides changes to the provision, the system may disregard both provision 3 and the changes and transmit the next stored provision, provision 2, which is a known provision already determined to be acceptable by the system. In this example the system may not need to evaluate actual changes input by a party, but rather use the fact that there are changes in a provision to serve up a new provision.”)
Regarding Claim 7 and Claim 19, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
wherein monitoring user interaction comprises: tracking user actions related to viewing, editing, or approving the modified multi-party agreement; and analyzing patterns in the tracked user actions to identify potential risks and automated actions to initiate responsive to the potential risks. (Wichern Par.23- “The present disclosure includes a system and method for the dynamic and automatic generation of contracts, agreements, terms, and the like. In one embodiment, the system gathers information regarding one or more of the parties or contracting entities, such as form input from a user, automatically from third party sources (e.g., Internet, databases such as news websites, social media, and the like), from internal sources (e.g., past history with the entity, ecosystem and experience with related entities or similar entities, interaction on the platform, and the like), as well as feedback and tracking data, to generate provisions and/or contracts that are a fit for the one or more contracting parties. The automatic generation, along with dynamic feedback into the system, allows contracts, vendor agreements, and other documentation to be generated and agreed to with substantially no negotiation or back and forth between the parties, streamlining business and relationships.; Par. 78;”)
Regarding Claim 8 and Claim 20, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
wherein the automated actions include at least one from among the group consisting of: generating alerts or notices; and initiating or modifying one or more workflows related to agreement modification, agreement termination, generation of amendment, agreement notice of control, or data processing. (Wichern Par.54- “Once the contract is generated in operation 214, the server 106 may store and/or transmit the contract to the first party, the hosting entity, and optionally the third party that will be executing the agreement with the first party. The type of output may be varied depending on the situation, but often, once the agreement is generated, both the first party and the third party will receive alerts on the respective devices of the agreement and either a link to the agreement, a copy of the agreement, or the like.”; Par. 73;”)
Regarding Claim 9 and Claim 21, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
Wichern teaches document management analysis and the feature is expounded upon by Shah:
further comprising: generating, by the one or more processors, a portfolio-level risk assessment for a group of multi-party agreements; and implementing portfolio-wide risk mitigation actions based on the portfolio-level risk assessment. (Shah Par.153-154 “The generated entities 1004 may be provided to the risk assignment engine 206 for determination of risk scores and to generative AI model(s) 214 for generation of one or more rules, as shown in FIG. 11 . FIG. 11 illustrates an example of the risk assignment engine 206, according to some embodiments of the current subject matter. The risk assignment engine 206 may be configured to receive one or more entities 1102 (e.g., entities 1004 a, 1004 b, . . . , 1004 c, as shown in FIG. 10 ). For each entity 1102, the risk assignment engine 206 may be configured to determine a risk score, e.g., for entity A 1004 a, the engine 206 may determine that it has a risk score A 1104 a, for entity B 1004 b, the engine 206 may determine that it has a risk score B 1104 b, and for entity C, the engine 206 may determine that it has a risk score C 1104 c, etc.”)
Wichern and Shah are directed to document management analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wichern, as taught by Shah by utilizing additional model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wichern with the motivation of improving the user experience and make search more efficient (Shah Par. 45).
Regarding Claim 10 and Claim 22, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
wherein the data associated with the multi-party agreement is received from multiple sources, including user input and third-party systems, and the method further comprises: normalizing the received data prior to generating the one or more user profiles. (Wichern Par.23; Par. 42- “Examples of party fields can include, age of the company 238 (e.g., years in operation), type of company 240 (e.g., LLC, Ltd., Inc., etc.), location 242 (e.g., city, state, country, or the like), team size 244 (e.g., number of employees, number of employees or persons in the contracting entity, etc.), annual revenue 246, type of technology 248, number of paying customers 250, number of other customers 252 (e.g., non-paying or proof of concept customers or users), average one year deal value 254 (e.g., average values of deals that the company has previously executed or is looking for), number of current pilots, industry (hardware vs. software vs. tech enabled services), revenue per month, experience of founders and other key employees, money values and class raise (e.g., friends and family vs. series A vs. series B), growth rate, board structure (e.g. private vs. public), and optionally areas where the company does not want to operate 256, such as excluded regions, or the like.”)
Regarding Claim 11 and Claim 23, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
further comprising: continuously monitoring, by the one or more processors, for changes in external factors affecting the risk score; and automatically initiating a re-assessment of the risk score when a change in external factors is detected. (Wichern Par.78- “Using the variations and party characteristics, the server 106 analyzes the agreement to detect trends or other statistically significant patterns. Similarly, the server 106 may also in operation 410 consider and analyze external elements as well. The external elements include trends in the marketplace, variations in technology, company or entity information (e.g., updates in financial status, revenue, growth, and the like), types of venture capital money investments with the startup, and the like. External elements can also include changes of law or policy that may affect agreements generated by the system. Sources of change of law information can include, for example, decisions from courts of law, equity, or administrative courts; new, changed or expiring regulations; statutes passed by legislative bodies and enacted by an executive body; tariffs; treaties; decisions by international bodies such as the United Nations, the International Trade Commission and the like; changes in monetary policy from central banks such as the Federal Reserve or the European Central Bank.; Par. 79”)
Regarding Claim 12 and Claim 24, Wichern in view Shah teach The method of claim 1,… and The system of claim 13,…
Wichern teaches document management analysis and the feature is expounded upon by Shah:
wherein automatically adjusting the one or more operating parameters comprises: identifying inefficiencies in document processing workflows based on the updated Al models; and modifying the document processing workflows to reduce processing time or resource utilization. (Shah Par. 82; Par. 127;Par.183- The obligation management engine 150 may store the document vectors 1328 in a database 1310 and index the document vectors 1328 into a searchable document index 1332. The document index 1332 allows for rapid retrieval of relevant document vectors 1328 by the obligation management engine 150 during the online search phase. The document index 1332 may include any data structure that stores these embeddings in a way that allows for efficient retrieval. For example, the document index 1332 may be implemented as a hash table or a tree structure to index the embeddings by the words or phrases they represent.; Par. 189”)
Wichern and Shah are directed to document management analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wichern, as taught by Shah by utilizing additional model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wichern with the motivation of improving the user experience and make search more efficient (Shah Par. 45).
Regarding Claim 13,
Wichern teaches
A system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to: receive data associated with a multi-party agreement; (Wichern Par. 32; Par. 4-5 –“The present disclosure relates generally to systems and methods that can automatically generate agreements and provisions between two or more parties. In one example, the method includes receiving by a processing element a plurality of entity characteristics corresponding to at least one of the two or more parties, calculation by the processing element of two or more values of the entity characteristics, utilizing by the processing element a calculated score based on the values of two or more characteristics, and the weighting of each characteristic based on a statistical model of the influence of each characteristic on optimally generated agreements and provisions between two or more parties, to identify a first agreement from two or more agreements, and outputting by the processing element the first agreement to a first user device corresponding to at least one of the two or more parties.”) ;
generate one or more user profiles based on the received data; (Wichern Par. 61- “FIG. 8 illustrates a specific example of utilizing the entity information to generate agreements or provisions. The method 300 may begin with operation 302 and the entity characteristics are determined. For example, as described above the server 106 may retrieve the party characteristics from information entered via the first user device 102 (either when generating a profile or during additional data entry points, e.g., supplemental questions as part of a new agreement), from data collection of the entity's interactions within the system 100 and other related tracking elements, and/or from third party sources, such as social media, the Internet, new sources, and the like. The entity information may also include feedback or engagement information with the system 100. For example, over time the system 100 may track a particular entity's performance and interactions with other parties within the system 100 and use this information as a separate characteristics that can be evaluated to improve or lower an entity's score.”);
execute one or more artificial intelligence (AI) models using the one or more user profiles as input; (Wichern Par. 45- “In some instances, the questions presented to the users may be dynamically generated based on previous answers. For example, a machine learning algorithm including a natural language processor, can analyze received answers and characteristics to determine follow-up or other related questions to be presented to the user, e.g., if the server receives a first answer to question A, then the first answer will drive the server to output question G, rather than question B. By dynamically modifying the questions based on previous questions and answers, the system can tailor the entity characteristics received to better questions that will be useful in generating the agreements and contracts”);
generate a risk score associated with the multi-party agreement based on output from the one or more Al models (Wichern Par. 63- “In another example, the entity characteristic 332 can be a risk class or weighted risk value. A numeric or qualitative ranking can be assigned to a risk class, such as “high”, “medium”, “low” and/or a value associated with a likely risk in a particular category, e.g., growth risk, legal risk, financial risk, and the like. The values or scores that are applied to the characteristic categories or buckets may be varied dynamically by the system 100 as new information is input, such as feedback into the system, and the like. Additionally, while the scores are assigned per characteristic, in some instances, the scores may be dependent on multiple characteristics, e.g., a first characteristic value may receive a first score value, unless a second characteristic value is below or above a threshold and then a second score value may be applied to the same characteristic.; Par. 76”);
determine one or more risk mitigation actions based on the risk score and the output from the one or more Al models (Wichern Par. 69- “In instances where the score is used to select provisions, operations 306 and operation 308 may be repeated for the desired number of provisions in the agreement and the operation 308 may then also include a “building” of the agreement by combining the selected provisions or provision terms into a form template or the like. For example, if an entity has a high score in a risk class or category, certain contract provisions providing indemnity for such risks can be included in the contract, e.g. above a particular value of risk, a risk reduction, indemnity, or other provision may be selected for inclusion in the agreement.”);
implement at least one of the one or more risk mitigation actions to modify the multi-party agreement (Wichern Par. 77-78- As mentioned, in some implementations the system 100 and platform may utilize feedback to dynamically update agreement provisions, values, scoring, weighting, and analyzed characteristics. FIG. 10 illustrates a flow chart for leveraging or incorporating feedback into the dynamic agreement generation platform. With reference to FIG. 10, the method 400 begins with operation 402 and the server 106 generates one or more tables or other reference structures that correlate agreement provisions of finally executed agreements to party or entity characteristics of the contracting parties. The method 400 also includes operation 404 where the server 106 further analyzes or identifies negotiated or otherwise varied provisions within the agreements, e.g., the server 106 compares the agreement as originally delivered to the executed copy. The server 106 then stores in a memory location the executed agreement characteristics along with the party characteristics of the parties that were bound by the executed agreement. Using the variations and party characteristics, the server 106 analyzes the agreement to detect trends or other statistically significant patterns. Similarly, the server 106 may also in operation 410 consider and analyze external elements as well. The external elements include trends in the marketplace, variations in technology, company or entity information (e.g., updates in financial status, revenue, growth, and the like), types of venture capital money investments with the startup, and the like. External elements can also include changes of law or policy that may affect agreements generated by the system.”);
monitor user interaction with the modified multi-party agreement (WIchern Par. 26- “In some embodiments, the system may also utilize dynamic feedback to update the contract or other document being prepared. For example, the system may monitor the length of time that a user spends on one or more provisions (e.g., before receiving an input accepting a provision, time before scrolling down on the webpage or to the next provision, etc.) and uses that feedback to dynamically update further provisions or terms within the contract or document. In this manner, the party's engagement within the system during the generation operation may also be used to modify the contract or document.”);
update the one or more Al models based on the monitored user interaction (WIchern Par. 46- In some instances, the questions presented to the users may be dynamically generated based on previous answers. For example, a machine learning algorithm including a natural language processor, can analyze received answers and characteristics to determine follow-up or other related questions to be presented to the user, e.g., if the server receives a first answer to question A, then the first answer will drive the server to output question G, rather than question B. By dynamically modifying the questions based on previous questions and answers, the system can tailor the entity characteristics received to better questions that will be useful in generating the agreements and contracts.”; Par. 60; Par. 64; Par. 76) ;
Wichern teaches document management analysis and the feature is expounded upon by Shah:
and automatically adjust one or more operating parameters of the multi-party document management platform based on the updated Al models. (Shah Par. 169-“ In some embodiments, the compliance data 1210a and/or non-compliance data 1210b may be sent back to the obligation management engine 150 for processing and/or any other use. For example, the data 1210 may be used to update identification of entities in the documents 202. It may also be used to update risk scores generated by the risk assignment engine 206. Further, the rules generation engine 210 may use the data 1210 for updating rules. Moreover, the compliance/non-compliance data may be used to train and/or retrain and/or refresh train one or more ML model(s) 208. As can be understood, the data 1210 may be used for any other purposes.
Wichern and Shah are directed to document management analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wichern, as taught by Shah by utilizing additional model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wichern with the motivation of improving the user experience and make search more efficient (Shah Par. 45).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20240119463 A1 to Gunther.- Abstract-“ A system software and method which relates to the field of computing technology or business process models or computer systems or distributed computer systems or computer networks relating to general purpose devices that can be programmed to carry out a set of data table or hash table updates, validations or modifications supporting, enabling or executing public and private services, financial transactions and commercial applications. More specifically, the present invention is directed to distributed and decentralized computing in which said distributed network is supported and encrypted by cryptographic technology, hash functions and distributed public or private keys or other related technology. System and methods for managing dynamic electronic documents on a private distributed ledger comprise establishing a dynamic electronic document comprising a first state object, wherein the state object references a prior approved first transaction; proposing a second transaction comprising as an input the first state object and as an output a transaction command to alter the state object as well as what parameters are required to validate the second transaction; validating the proposed second transaction; and updating the state object on a private distributed ledger to reference the second transaction.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
Prosecution Insights