DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-10 are pending.
Claim Objections
Claim 1 is objected to because of the following informalities: the claim inconsistently recites the term “A/I” as well as “AI” to refer to artificial intelligence. This inconsistency in terminology may cause confusion as to the scope of the claimed invention. The term “AI” is more commonly understood as the shorthand for “artificial intelligence”. Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities: the specification inconsistently recites the term “A/I” as well as “AI” to refer to artificial intelligence. This inconsistency in terminology may cause confusion as to the scope of the claimed invention. The term “AI” is more commonly understood as the shorthand for “artificial intelligence”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claim 1(g) recites implementing robust data security measures, compliance with data protection regulations, anonymization of user data, and transparent privacy policies. The specification only generically refers to encryption and audits, and does not describe anonymization methods or implementation of compliance frameworks. (see paragraph 0027). The written description therefore does not demonstrate possession of the full scope of the claimed subject matter.
Claim 1(h) recite ensuring compatibility with various browsers and mobile devices, deploying to cloud-based hosting infrastructure, and providing regular updates and bug fixes. The specification notes cloud hosting but does not provide details on achieving broad cross-platform compatibility or processes for updates. (see paragraph 0024). The written description does not show possession of these features as broadly claimed.
Claim 1(i) recite training the virtual agent using reinforcement learning techniques. While the specification references reinforcement learning conceptually, it does not reasonably convey to a PHOSITA that the inventor had possession of reinforcement learning as applied to fine-tuning large language models for negotiation dialogs. (see paragraph 0042). The disclosure does not describe specific reward structures, training loops, or examples of reinforcement learning in the negotiation context. (See MPEP §2163). The written description therefore does not demonstrate possession of the full scope of the claimed subject matter.
Claims 1(L) and 9 recite An API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities and the API layer facilitates integration with external platforms, allowing third-party developers to create custom modules, extensions, or plugins for the negotiation platform respectively. While the specification references an API layer, it does not describe endpoint structures, authentication mechanisms, or example integrations. (see paragraph 0028). Therefore, the specification does not show possession of the full breadth of the claimed API functionality.
Claims 3 and 7 recite negotiation scenarios offered include industry-specific scenarios, geographic-specific scenarios, diverse languages and culturally diverse scenarios to enhance user adaptability to various negotiation contexts and a virtual reality (VR) interface option, allowing users to engage in negotiation scenarios through VR devices for an enhanced and immersive learning experience respectively. The specification mentions these features at a high level but does not describe how they are implemented. (See paragraphs 0006, 0026-0027). The written description therefore does not demonstrate possession of the full scope of the claimed subject matter.
Dependent claims 2, 4-6, 8, and 10 are rejected by virtue of their dependency. Accordingly, claims 1-10 are rejected under 35 USC § 112(a) for lack of written description.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or joint inventors regard as the invention.
Claim 1 contains the trademark/trade name iOS/Android. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b). See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe applications and, accordingly, the identification/description is indefinite.
The terms “robust; transparent; regular; and well-documented” in claim 1 are relative terms which render the claim indefinite. The terms “robust; transparent; regular; and well-documented” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitations robust data security measures; transparent privacy policies; providing regular updates and bug fixes; and well-documented API endpoints has been rendered indefinite by the use of the terms aforementioned. Appropriate correction is required.
Claim 1(i) recites an open-source large language model whereas claim 1(L) recites the language model engine. There is no clear antecedent basis for language model engine, and the relationship between both is not established. The lack of antecedent basis renders the claim indefinite. Appropriate correction is required.
Claim 1(L) further recites designing and implementing an API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities. A method does not “communicate”, which makes the scope off this limitation unclear. The claim should indicate communication between system components. Appropriate correction is required.
The terms “diverse and culturally diverse” in claim 3 are relative terms which render the claim indefinite. The terms “diverse and culturally diverse” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
The terms “positive and constructive” in claim 6 are relative terms which render the claim indefinite. The terms “positive and constructive” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
The terms “enhanced and immersive” in claim 7 are relative terms which render the claim indefinite. The terms “enhanced and immersive” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
The dependent claims 2, 4-5, 8, and 9-10 are rejected to by virtue of their dependency.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1 is:
providing a user with access;
offering a plurality of negotiation scenarios, including pre-defined scenarios and customizable scenarios based on specific datasets, said scenarios designed to simulate real-world negotiation challenges and accommodating various difficulty levels;
enabling user registration and login functionality, user profile management, and integration for achievements sharing;
simulating negotiation behavior, understanding user inputs and providing context-specific responses influenced by user moves and negotiation tactics;
offering real-time suggestions and prompts tailored to enhance total value realization and foster win/win outcomes, based on historical data, negotiation strategies, and best practices;
evaluating negotiation performance, comprising assessment of individual objective achievement, total value accrued to both parties, and feedback on negotiation strategies, tactics, and decision-making;
implementing robust data security measures, compliance with data protection regulations, anonymization of user data, and transparent privacy policies;
ensuring compatibility with various browsers and mobile devices, and providing regular updates and bug fixes;
enabling parsing and understanding of user inputs, managing negotiation dialogues;
generating suggestion prompts for negotiation moves or tactics based on the negotiation context, evaluating their effectiveness;
implementing data management and versioning systems to store, manage, and preprocess negotiation-specific datasets, along with model monitoring and maintenance procedures for continuous improvement and ethical considerations; and
The abstract idea steps italicized above recite negotiation training and evaluating performance, which constitutes a process that, under its broadest reasonable interpretation (BRI), covers managing personal behavior relationships, interactions between people. This is further supported by paragraphs 0003-0004 of applicant’s specification as filed. If a claim limitation, under its BRI, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim recites negotiation skill development and value maximization, which constitutes a process that, under its BRI, covers commercial activity. This is further supported by paragraphs 0003-0004 of applicant’s specification as filed. If a claim limitation, under its BRI, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: A/I powered virtual negotiation environment; negotiation platform, accessible through a web application and iOS/Android applications, said negotiation platform utilizing an A/I system; social media platform; through an AI-powered virtual agent endowed with natural language processing capabilities; seamlessly integrating the computer-implemented method with web and mobile platforms; deploying to cloud-based hosting infrastructure; utilizing a machine learning framework for integrating and fine-tuning an open- source large language model; training the virtual agent using reinforcement learning techniques; deploying the language model on scalable cloud-based infrastructure; designing and implementing an API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs 0025-0029 of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Claim 2 includes further additional elements: wherein the negotiation platform's user interface provides a visually immersive experience, including interactive elements such as graphical representations of negotiation scenarios, virtual negotiation rooms, and dynamic feedback displays. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Regarding claims 3, 5, 8, and 10, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Claim 4 includes further additional elements: machine-learning driven recommendation engine. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claim 6 includes further additional elements: AI-powered virtual agent's natural language processing capabilities. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claim 7 includes further additional elements: virtual reality (VR) interface; VR devices. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claim 9 includes further additional elements: the API layer facilitates integration with external platforms, allowing third-party developers to create custom modules, extensions, or plugins for the negotiation platform. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Accordingly, claims 1-10 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-8, and 10 are rejected under 35 U.S.C. § 103 as being unpatentable over Giagnocavo (US 20240265827) in view of Krasadakis (US 20170287038) in further view of Wheeler (US 20240412313) in further view of Muriqi (US 20240046318).
Claim 1
Regarding claim 1, Giagnocavo discloses:
A computer-implemented method for facilitating negotiation skill development and value maximization in an A/I powered virtual negotiation environment, comprising the following steps: {A computer-implemented method is carried out by AI/ML systems with virtual personas that interact with users through different interfaces, including VR. It includes negotiation as a trainable skill (paragraphs 0023, 0086-0087, 0119).}
Generating suggestion prompts for negotiation moves or tactics based on the negotiation context, evaluating their effectiveness; {The system can analyze conversations to find goals and generate conversational threads, which work as suggestion prompts for negotiation moves or tactics. It then evaluates their effectiveness by checking flow, sentiment, tone, and changes in user skill level, and updates plans accordingly (paragraphs 0087, 0092-0096, 0119).}
Implementing data management and versioning systems to store, manage, and preprocess negotiation-specific datasets, along with model monitoring and maintenance procedures for continuous improvement and ethical considerations; and {Data management is used via multiple databases for user info and domain datasets that the system stores and analyzes during interactions. Negotiation is included as a target skill and instructional data is used to generate lesson plans and conversational threads (i.e., negotiation-specific datasets) that are processed/preprocessed to assess skill level for example. The system performs continuous monitoring (e.g., tone/sentiment) during conversations and updates the AI/ML model after the interaction for continuous improvement. It also iteratively updates and adapts lessons plans (i.e., versioning). The system also uses guardrails/parameters to steer conversations and layered handling of personal data (i.e., ethical considerations) (paragraphs 0035-0037, 0087, 0114, 0117-0119, 0148-0149).}
Giagnocavo does not disclose:
Providing a user with access to a negotiation platform, accessible through a web application and iOS/Android applications, said negotiation platform utilizing an A/I system;
Offering a plurality of negotiation scenarios, including pre-defined scenarios and customizable scenarios based on specific datasets, said scenarios designed to simulate real-world negotiation challenges and accommodating various difficulty levels;
Enabling user registration and login functionality, user profile management, and integration with social media platforms for achievements sharing;
Simulating negotiation behavior through an AI-powered virtual agent endowed with natural language processing capabilities, capable of understanding user inputs and providing context-specific responses influenced by user moves and negotiation tactics;
Offering real-time suggestions and prompts tailored to enhance total value realization and foster win/win outcomes, based on historical data, negotiation strategies, and best practices;
Evaluating negotiation performance, comprising assessment of individual objective achievement, total value accrued to both parties, and feedback on negotiation strategies, tactics, and decision-making;
Implementing robust data security measures, compliance with data protection regulations, anonymization of user data, and transparent privacy policies;
Seamlessly integrating the computer-implemented method with web and mobile platforms, ensuring compatibility with various browsers and mobile devices, deploying to cloud-based hosting infrastructure, and providing regular updates and bug fixes;
Utilizing a machine learning framework for integrating and fine-tuning an open- source large language model, enabling parsing and understanding of user inputs, managing negotiation dialogues, and training the virtual agent using reinforcement learning techniques;
deploying the language model on scalable cloud-based infrastructure;
Designing and implementing an API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities.
However, Krasadakis, in a similar field of endeavor directed to an AI-powered framework whereby autonomous AI agents negotiate deals on behalf of buyers and sellers, teaches:
Providing a user with access to a negotiation platform, accessible through a web application and iOS/Android applications, said negotiation platform utilizing an A/I system; {Buyers and sellers access the negotiation system through client devices (e.g., smartphones, laptops, tablets) via pre-installed or downloadable applications, which create and manage AI negotiation agents (paragraphs 0016, 0042, 0052).}
Offering a plurality of negotiation scenarios, including pre-defined scenarios and customizable scenarios based on specific datasets, said scenarios designed to simulate real-world negotiation challenges and accommodating various difficulty levels; {Predefined buyer and seller plans may include goals and elasticities (i.e., real-world negotiation conditions) based on datasets such as product specifications, pricing, inventory, supply-demand, seasonality, and social media data (paragraphs 0017-0018, 0024).}
Enabling user registration and login functionality, user profile management, and integration with social media platforms for achievements sharing; {The system includes user profile data management, including unique identifiers, purchase history, and social media integration to influence negotiation (paragraphs 0017, 0055).}
Simulating negotiation behavior through an AI-powered virtual agent endowed with natural language processing capabilities, capable of understanding user inputs and providing context-specific responses influenced by user moves and negotiation tactics; {AI negotiators autonomously simulate negotiation behavior using predefined parameters and elasticity, capable of multi-stage, back and forth negotiations. The system includes NLP capabilities (paragraphs 0025, 0028, 0124).}
Offering real-time suggestions and prompts tailored to enhance total value realization and foster win/win outcomes, based on historical data, negotiation strategies, and best practices; {Buyer and seller AI negotiators dynamically adjust parameters and elasticity based on historical offers, market data, and strategies (i.e., producing optimized suggestions) (paragraphs 0058, 0060).}
Evaluating negotiation performance, comprising assessment of individual objective achievement, total value accrued to both parties, and feedback on negotiation strategies, tactics, and decision-making; {Negotiation steps, offer terms, and decisions may be registered to support reporting, analytics, and performance assessment (paragraph 0081).}
Seamlessly integrating the computer-implemented method with web and mobile platforms, ensuring compatibility with various browsers and mobile devices, deploying to cloud-based hosting infrastructure, and providing regular updates and bug fixes; {The platform works with mobile devices, desktops, wearables, and cloud-based scenarios (i.e., integrates with web applications and mobile apps) (paragraphs 0016, 0032, 0050).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the synthetic persona interaction features of Giagnocavo to include the AI negotiation agent framework features of Krasadakis, to streamline the deal decision-making process for buyers and sellers. (see abstract and paragraph 0004 of Krasadakis).
The combination of Giagnocavo and Krasadakis does not teach:
Implementing robust data security measures, compliance with data protection regulations, anonymization of user data, and transparent privacy policies;
Utilizing a machine learning framework for integrating and fine-tuning an open- source large language model, enabling parsing and understanding of user inputs, managing negotiation dialogues, and training the virtual agent using reinforcement learning techniques.
deploying the language model on scalable cloud-based infrastructure;
Designing and implementing an API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities.
However, Wheeler, in a similar field of endeavor directed to using chatbots or other bots for training employees and career development, teaches:
Utilizing a machine learning framework for integrating and fine-tuning an open- source large language model, enabling parsing and understanding of user inputs, managing negotiation dialogues, and training the virtual agent using reinforcement learning techniques; {The system includes a full ML framework with explicit modules for training and inference and integration with common ML libraries. It allows for fine-tuning a pretrained language model/LLM via supervised fine-tuning, NLP/NLU/NLG components to parse and understand user inputs and to generate dialog responses, and reinforcement learning-based training (paragraphs 0037-0039, 0046-0047, 0065-0073).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Giagnocavo and Krasadakis to include the natural language processing functionality features of Wheeler, to improve the productivity, quality, and efficiency of the users. (see paragraph 0003 of Wheeler).
The combination of Giagnocavo, Krasadakis, and Wheeler does not teach:
Implementing robust data security measures, compliance with data protection regulations, anonymization of user data, and transparent privacy policies;
deploying the language model on scalable cloud-based infrastructure;
Designing and implementing an API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities.
However, Muriqi, in a similar field of endeavor directed to a social or user network that provides network-activity based rewards or incentives, teaches:
Implementing robust data security measures, compliance with data protection regulations, anonymization of user data, and transparent privacy policies {The system uses encryption (i.e., security measures), regulatory compliance procedures like “know your customer” regulation (i.e., compliance with data protection regulations), and anonymization/pseudonymization like credentials usable anonymously issued by a registrar (paragraphs 0014, 0016, 0039, 0042). Also, “The media content of the social network may be protected by [Digital Rights Management] DRM. As noted above, the DRM may interaction directly with Fungible Tokens or Non-Fungible Tokens, or the GRM may employ separate cryptographic credentials” (i.e., privacy policies) (paragraph 0290).}
deploying the language model on scalable cloud-based infrastructure; {The system can perform LLM driven context analysis to select and rank outputs and adapt ads (i.e., suggestions), evaluate effectiveness via ranking and adaptive updates, and deploy the LLM on centralized or cloud resources (paragraph 0069).}
Designing and implementing an API layer to facilitate communication between the computer-implemented method and the language model engine, with well- documented API endpoints for various functionalities. {The system includes a dedicated, authenticated API layer with enumerated operations that mediates the system’s workflows and communicates with a LLM hosted as a cloud service. This way it connects the “method” to the LLM engine via defined endpoints (e.g., monitoring, updating, verification, accounting) (paragraphs 0069, 0082-0087, 0090).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Giagnocavo, Krasadakis, and Wheeler to include the social network security and marketing features of Muriqi, to improve confidence in the transactions and streamline government regulations compliance. (See paragraph 0008 of Muriqi).
Claim 2
Regarding claim 2, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Krasadakis further teaches:
wherein the negotiation platform's user interface provides a visually immersive experience, including interactive elements such as graphical representations of negotiation scenarios, virtual negotiation rooms, and dynamic feedback displays. {The system may include immersive UI modalities (e.g., VR/AR/holographic) that display negotiation outputs and updates users during negotiations (i.e., supports interactive and dynamic feedback displays (paragraphs 0035, 0041, 0065).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi to include the AI negotiation agent framework features of Krasadakis, to streamline the deal decision-making process for buyers and sellers. (see abstract and paragraph 0004 of Krasadakis).
Claim 3
Regarding claim 3, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Giagnocavo further discloses:
wherein the negotiation scenarios offered include industry-specific scenarios, geographic-specific scenarios, diverse languages and culturally diverse scenarios to enhance user adaptability to various negotiation contexts. {The system can train users negotiation skills. It can adapt languages, accents, cultural mannerisms, and phraseology. The system also leverages layered information (e.g., per-company, per-market, per-user), which provides context for industry-specific and geographic-specific scenarios (paragraphs 0095-0096, 0101, 0119).}
Claim 4
Regarding claim 4, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Giagnocavo further discloses:
wherein the negotiation platform further comprises a machine learning-driven recommendation engine that suggests personalized training modules and exercises based on the user's historical performance, identified areas for improvement, and individual negotiation style. {The system uses AI/ML models to analyze conversations with the user, identify goals, determine skill levels, and adapt interaction structures. It includes negotiation as a trainable skill. Based on the analysis, the system updates lesson plans and generates new conversational threads to address weaknesses and improve performance (i.e., recommendation engine that adapts training content). The system also incorporates stored historical information such as previous interactions, which enables personalization according to historical performance and individual style (paragraphs 0093-0096, 0117-0119).}
Claim 5
Regarding claim 5, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Muriqi further teaches:
wherein the evaluation of negotiation performance includes generating a comprehensive performance report for users, incorporating statistical analyses, graphical representations, and comparative data against benchmark performances. {Monitored data exported via an API produce reports, applies statistical methods, visualizes and adjust metrics through graphical sliders, and evaluates users against peer or structural benchmarks (paragraphs 0068, 0071, 0078-0079, 0123-0126).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi to include the social network security and marketing features of Muriqi, to improve confidence in the transactions and streamline government regulations compliance. (See paragraph 0008 of Muriqi).
Claim 6
Regarding claim 6, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Giagnocavo further discloses:
wherein the AI-powered virtual agent's natural language processing capabilities include sentiment analysis to gauge the emotional tone of user inputs and adapt its responses to foster a positive and constructive learning environment. {The AI/ML persona performs sentiment and emotional tone analysis of user inputs and adapts responses accordingly for improved interaction. Negotiation is a listed skill (0095, 0102, 0119, 0130, 0159).}
Claim 7
Regarding claim 7, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Giagnocavo further discloses:
further comprising a virtual reality (VR) interface option, allowing users to engage in negotiation scenarios through VR devices for an enhanced and immersive learning experience. {The system includes multiple types of user interfaces (UIs) such as VR/AR/MR. Avatars of personas driven by AI/ML may be generated, displayed, and animated within those interfaces to interact with the user during conversations. Negotiation is included as a trainable skill (paragraphs 0106-0107, 0119).}
Claim 8
Regarding claim 8, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Giagnocavo further discloses:
wherein the data security measures comprise end-to-end encryption for user communication, secure storage protocols for user profiles and negotiation data, and regular security audits to identify and address potential vulnerabilities. {The system includes security mechanisms such as identity verification and two-factor authentication, and manages sensitive stored data including billing information and contracts, for example (i.e., uses secure storage protocols) (paragraphs 0092, 0097, 0159-0161, 0170).}
Claim 10
Regarding claim 10, the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the features set forth above. Giagnocavo further discloses:
wherein the language model engine's training process involves continuous learning from user interactions, user feedback, and evolving negotiation trends, ensuring the virtual agent remains adaptive to dynamic negotiation landscapes. {The system performs continuous tone and sentiment analysis during conversations and then updates the AI/ML model after the interaction. It also stores and analyzes historical user data, previous interactions, and personal information to adapt future responses. Negotiation is included as a skill trainable by the system (paragraphs 0096, 0102, 0119).}
Claim 9 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi, in further view of O’Brien (WO 2013131121-A2).
Claim 9
Regarding claim 9, while the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi teaches the limitations set forth above, it does not explicitly teach:
wherein the API layer facilitates integration with external platforms, allowing third-party developers to create custom modules, extensions, or plugins for the negotiation platform.
However, O’Brien, in a similar field of endeavor directed to occupational-related education of personnel, teaches:
wherein the API layer facilitates integration with external platforms, allowing third-party developers to create custom modules, extensions, or plugins for the negotiation platform. {The system for facilitating education (i.e., negotiation training) allows for a third party to manage content or data in a database (page 11, line 31 – page 12, line 8).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Giagnocavo, Krasadakis, Wheeler, and Muriqi to include the education organizational features of O’Brien, to improve the tracking of real educational progress of users. (see page 2 of O’Brien).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20250094723, which teaches: A system for training a user for a conversational encounter with a customer receives encounter-defining parameters via a user interface. The encounter-defining parameters are descriptive of attributes of the conversational encounter. The system formats the encounter-defining parameter for transmission to a large language model (LLM). The LLM receives the instructions and outputs a conversational opening viewable by a user via a user interface. The user then responds to the conversational opening and iteratively converses with the LLM. After a defined maximum number of iterations have been reached, the LLM provides an evaluation to the user via the user interface. The evaluation is indicative of the evaluated outcome of the conversational encounter, including at least one of a score, a summary of the encounter, a likelihood that a deal is reached, and suggested improvements.
US 20140006326, which teaches: An approach is provided for rapport management. A rapport management platform processes and/or facilitates a processing of coach multimodal sensor information to determine movement information, cognitive information, or a combination thereof, wherein the coach multimodal sensor information is captured from at least one coach device, at least one coach user of the at least one coach device, or a combination thereof while the at least one coach device, the at least one coach user, or a combination thereof is engaged in at least one activity. The rapport management platform is capable of processing and adapting mixed reality objects, changing virtual reality, creating coaching reality based on information, adapting to rendering of reality, reducing and adapting notes and information according to people habits. The rapport management platform is also capable of controlling the quality of mixed reality content transferred in computation clouds, and selecting appropriate CODEC for coaching model transfer.
US 20210390647, which teaches: The present disclosure is directed to systems and methods for intelligently staging, capturing, and facilitating real estate negotiations. In one example aspect, a proposal may be submitted by a first party. The proposal may be locked to capture the state of the proposal terms at that point in time. The proposal may also be cloned, wherein the cloned proposal may be editable by a second party. The system may provide intelligent suggestions to include in the counter-proposal to the second party based on historical transactional data related to the first party, property characteristics, and/or current market data. The intelligent suggestions may be approved and integrated into the cloned proposal. Once the cloned proposal is updated accordingly, the cloned proposal with the updated terms may become a counter-proposal that is then transmitted back to the first party. The system may then receive an acceptance or rejection indication from the first party.
“Negotiation Behaviors Based on Artificial Intelligence and Social and Cognitive Human-Agent Interaction” (NPL attached), which teaches: Behaviors, in which the characters such as conciliatory (Con), neutral (Neu), or aggressive (Agg) define a 'psychological' aspect of the human personality, play an important role for negotiation agent. Elsewhere, learning in negotiation is fundamental for understanding human behaviors as well as for developing new solution concepts. In this paper, a brief description of SISINE (Integrated System of Simulation for Negotiation) project, which aims to develop innovative teaching methodology of negotiation skills, is given. Then, a negotiation strategy essentially based on negotiation behaviors of the human personality is suggested for SISINE. Such negotiation behaviors which are based on the characters Con, Neu, and Agg, and acquired first by reinforcement learning (Q-learning, and Sarsa-Learning) approaches, and second by Neural Networks (NN) are then developed. From this, the suggested strategy is developed and the negotiation behavior results presented. The suggested strategy displays the ability to provide agents, through a basic buying strategy, with a first intelligence level.
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/C.F.M./ Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629