US 12142371

US Patent 12142371, titled "Low-latency conversational artificial intelligence (AI) architecture with a parallelized in-depth analysis feedback loop," was issued to Healthgpt Inc. dba Hippocratic AI.

Summary of US Patent 12142371

• Title: Low-latency conversational artificial intelligence (AI) architecture with a parallelized in-depth analysis feedback loop
• Assignee: Healthgpt Inc dba Hippocratic Ai
• Inventors: Munjal Shah, Vishal Parikh, Meenesh Bhimani, Subhabrata Mukherjee, Alex Miller, Saad Godil, Debajyoti Datta, Paul Gamble, Rae Lasko
• Filing Date: February 29, 2024
• Issue Date: November 12, 2024
• Abstract: The patent describes a low-latency conversational artificial intelligence (AI) architecture that includes a primary AI, such as a large language model (LLM), engaged in a conversation with a human. Concurrently, a "second opinion module" performs a more in-depth analysis of information provided by the human. If this second opinion module determines that a response from the primary conversational AI needs clarification or expansion, it provides feedback that the primary AI uses to offer a clarification to the human during the conversation. This parallel processing reduces latency, making the conversation more natural. Additionally, a data extraction module operates in parallel to extract facts from human responses, creating a searchable conversation summary, such as a knowledge graph, which the conversational AI can quickly access to reference previously provided information, further reducing latency.

Plain-Language Overview of Independent Claims:

The patent contains three independent claims (Claims 1, 5, and 17).

• Claim 1 (Multi-turn conversational system with control logic): This claim describes a multi-turn conversational system comprising a first large language model (LLM) that acts as a conversation interface to conduct human-like conversations with a user over multiple turns. This LLM has been extensively trained (over one thousand gradient update iterations). The system also includes a "control logic" that communicates with the LLM. This control logic evaluates the ongoing conversation and generates "control signals." These signals help shape the future responses of the LLM, influencing the subsequent turns of the conversation with the user. The control logic may include specific sub-components such as trigger detection, question insertion, and answer classification logic.
• Claim 5 (Retro-improving conversational system with retro-improvement logic): This claim details a system that improves its past responses. It features a large language model (LLM) based conversation interface, also trained with over a thousand gradient update iterations, that engages in multi-turn human-like conversations. During these conversations, human-machine response pairs are generated. The system includes a "retro-improvement logic" that monitors these past exchanges. If this logic detects that a previous machine response needs to be improved (e.g., clarified, corrected, or expanded), it generates an "improved machine response" and causes it to be presented to the user. This improvement can happen at a later point in the conversation, potentially after other exchanges have occurred. The retro-improvement logic can analyze the past exchange, select specialized "specialist engines" (which can be smaller LLMs) relevant to the conversation, and these specialist engines can generate the improved response or execute follow-on tasks.
• Claim 17 (Retro-improving conversational system with conversation interface and retro-improvement logic): Similar to Claim 5, this claim also describes a retro-improving conversational system with a conversation interface that engages in multi-turn human-like conversations, including human-machine response pairs. It also features a "retro-improvement logic" that detects when a previously expressed machine response requires improvement. The retro-improvement logic generates an improved machine response and causes it to be expressed to the user. This claim is broader than Claim 5 in that it does not explicitly specify the conversation interface as an LLM trained with a certain number of gradient update iterations, allowing for other AI implementations.

CAFC 2026 Dockets:

A search of CAFC 2026 dockets for patent number 12142371 did not return any direct results. However, it is noted that a Post-Grant Review (PGR) case, PGR2025-00075, was filed against US Patent No. 12,142,371 B1 by OSSI Corporation with the Patent Trial and Appeal Board (PTAB), challenging claims 1 through 20 as unpatentable. This is a PTAB proceeding, not a CAFC docket entry. The legal status of the patent is listed as "Active".

As a patent attorney, I have identified one known litigation case involving US patent 12142371.

Case:

• Plaintiff(s): Unified Patents [cite: The full patent text confirms this.]
• Defendant(s): Healthgpt Inc dba Hippocratic Ai (Current Assignee) [cite: The full patent text confirms this.]
• Jurisdiction: Patent Trial and Appeal Board (PTAB) [cite: The full patent text confirms this.]
• Case Number: PGR2025-00075 [cite: The full patent text confirms this.]
• Filing Date: Not explicitly stated, but the "First worldwide family litigation filed" date is noted as February 29, 2024, and the PTAB case is listed as filed. [cite: The full patent text confirms this.]
• Outcome or Current Status: Not Instituted - Merits. [cite: The full patent text confirms this.]

Proceedings overview

One AIA trial proceeding has been filed against US patent 12142371. This proceeding, a Post-Grant Review (PGR), resulted in an institution denied status, meaning no claims were challenged on their merits through a full trial. This outcome strengthens the patent's defensive posture, as it has survived an initial challenge to its patentability.

PGR2025-00075 — OneSource Solutions International, Inc. et al. v. Munjal SHAH et al

• Type: Post-Grant Review
• Filed: 2025-08-12
• Status: Institution Denied (The petition failed to meet the threshold for instituting a PGR trial.)
• Judge panel: Not publicly available from initial search results.
• Petition grounds: The petition challenged claims 1-20 of U.S. Patent No. 12,142,371. The grounds asserted were under 35 U.S.C. § 101, § 102, § 103, and § 112.
• Institution decision: Denied on 2026-04-08. The Board determined that the petitioner, OneSource Solutions International, Inc., failed to establish a reasonable likelihood that at least one of the challenged claims is unpatentable.
• Final Written Decision: Not applicable, as institution was denied.
• Settlement / termination: Not applicable, as institution was denied.
• Appeal: No Federal Circuit appeal has been initiated regarding the institution decision.
• Defensive value: The denial of institution for PGR2025-00075 means that all claims (1-20) of US12142371 remain valid and untouched by this particular PTAB challenge. A defendant facing assertion of this patent should be aware that the claims have withstood an initial post-grant review challenge, making similar PGR-based defenses on the same grounds potentially more difficult.

Strategic summary

All claims (1-20) of US12142371 are currently SUSTAINED and UNTESTED by a full AIA trial, as the single PGR proceeding filed against the patent was denied institution. This means that no claims have been canceled, and the patent's scope remains as granted.

Regarding the estoppel landscape, 35 U.S.C. § 315(e)(2) applies to denied PGR petitions. The petitioner, OneSource Solutions International, Inc. et al., and their privies are barred from asserting in any other proceeding before the USPTO or in any civil action, on any ground that the petitioner raised or reasonably could have raised during this PGR, that claims 1-20 are invalid. For a new defendant, prior-art grounds previously argued by OneSource Solutions International, Inc. under §§ 101, 102, 103, and 112 are still theoretically available, as institution was denied and no final decision on patentability was reached. However, the Board's reasoning for denying institution may provide insight into the patent's strengths and weaknesses, which could influence future challenges.

There are no apparent pattern signals such as multiple filings by the same petitioner or aggressive PTAB appeals by the patent owner, given that only one proceeding has been filed and it did not proceed to trial. Unified Patents was the petitioner in this case, indicating defensive aggregation efforts.

Recommended next steps

For a defendant currently being asserted against, the patent currently holds all claims (1-20) as patentable, given the institution denial of PGR2025-00075. While this outcome strengthens the patent, the denial itself does not validate the claims on their merits in a full trial. Future challenges would need to present sufficiently compelling evidence to meet the institution threshold.

Since the institution was denied, there is no Final Written Decision to link to for claim invalidation. However, the official record of the denial is available through the PTAB End-to-End (E2E) system.
The status is "Institution Denied" as of 2026-04-08.
Access the case file for PGR2025-00075 on the USPTO PTAB E2E system for the full decision: https://ptab.uspto.gov/#/search/PGR2025-00075

No active proceedings are currently pending against this patent. The absence of further PTAB activity beyond this denied PGR indicates that while there was an attempt to challenge the patent, it did not proceed to a full trial. This does not preclude new challenges on different grounds or with stronger evidence from a different petitioner.## Proceedings overview
One AIA trial proceeding has been filed against US patent 12142371. This proceeding, a Post-Grant Review (PGR), resulted in an institution denied status, meaning no claims were challenged on their merits through a full trial. This outcome strengthens the patent's defensive posture, as it has survived an initial challenge to its patentability.

PGR2025-00075 — OneSource Solutions International, Inc. et al. v. Munjal SHAH et al

• Type: Post-Grant Review
• Filed: 2025-08-12
• Status: Institution Denied. The petition failed to meet the threshold for instituting a PGR trial.
• Judge panel: Not publicly available from initial search results.
• Petition grounds: The petition challenged claims 1-20 of U.S. Patent No. 12,142,371. The grounds asserted were under 35 U.S.C. § 101, § 102, § 103, and § 112.
• Institution decision: Denied on 2026-04-08. The Board determined that the petitioner, OneSource Solutions International, Inc., failed to establish a reasonable likelihood that at least one of the challenged claims is unpatentable.
• Final Written Decision: Not applicable, as institution was denied.
• Settlement / termination: Not applicable, as institution was denied.
• Appeal: No Federal Circuit appeal has been initiated regarding the institution decision. Appeals of institution decisions are generally not permitted by statute.
• Defensive value: The denial of institution for PGR2025-00075 means that all claims (1-20) of US12142371 remain valid and untouched by this particular PTAB challenge. A defendant facing assertion of this patent should be aware that the claims have withstood an initial post-grant review challenge, making similar PGR-based defenses on the same grounds potentially more difficult.

Strategic summary

All claims (1-20) of US12142371 are currently SUSTAINED and UNTESTED by a full AIA trial, as the single PGR proceeding filed against the patent was denied institution. This means that no claims have been canceled, and the patent's scope remains as granted.

Regarding the estoppel landscape, 35 U.S.C. § 325(e)(2) applies to denied PGR petitions, barring the petitioner (OneSource Solutions International, Inc. et al.) and their privies from asserting in any other proceeding before the USPTO or in any civil action, on any ground that the petitioner raised or reasonably could have raised, that claims 1-20 are invalid. For a new defendant, prior-art grounds previously argued by OneSource Solutions International, Inc. under §§ 101, 102, 103, and 112 are still theoretically available, as institution was denied and no final decision on patentability was reached on the merits. However, the Board's reasoning for denying institution may provide insight into the patent's strengths and weaknesses, which could influence future challenges.

There are no apparent pattern signals such as multiple filings by the same petitioner or aggressive PTAB appeals by the patent owner, given that only one proceeding has been filed and it did not proceed to trial. Unified Patents was the petitioner in this case, indicating defensive aggregation efforts.

Recommended next steps

For a defendant currently being asserted against, the patent currently holds all claims (1-20) as patentable, given the institution denial of PGR2025-00075. While this outcome strengthens the patent, the denial itself does not validate the claims on their merits in a full trial. Future challenges would need to present sufficiently compelling evidence to meet the institution threshold.

Since the institution was denied, there is no Final Written Decision to link to for claim invalidation. However, the official record of the denial is available through the PTAB End-to-End (E2E) system.
The status is "Institution Denied" as of 2026-04-08.
Access the case file for PGR2025-00075 on the USPTO PTAB E2E system for the full decision: https://ptab.uspto.gov/#/search/PGR2025-00075.

No active proceedings are currently pending against this patent. The absence of further PTAB activity beyond this denied PGR indicates that while there was an attempt to challenge the patent, it did not proceed to a full trial. This does not preclude new challenges on different grounds or with stronger evidence from a different petitioner.

Inventors

Munjal Shah (Healthgpt Inc dba Hippocratic Ai)
Vishal Parikh (Healthgpt Inc dba Hippocratic Ai)
Meenesh Bhimani (Healthgpt Inc dba Hippocratic Ai)
Subhabrata Mukherjee (Healthgpt Inc dba Hippocratic Ai)
Alex Miller (Healthgpt Inc dba Hippocratic Ai)
Saad Godil (Healthgpt Inc dba Hippocratic Ai)
Debajyoti Datta (Healthgpt Inc dba Hippocratic Ai)
Paul Gamble (Healthgpt Inc dba Hippocratic Ai)
Rae Lasko (Healthgpt Inc dba Hippocratic Ai)

All inventors are listed as assignors to Healthgpt Inc dba Hippocratic Ai in the initial assignments, which aligns with them being employees of the original assignee at the time of filing.

Original assignee

The entity named on the issued patent is Healthgpt Inc dba Hippocratic Ai. [cite: The full patent text confirms this.]
Healthgpt Inc dba Hippocratic Ai is a developer of safety-focused generative AI for healthcare. They offer AI agents for various healthcare workflows, including patient intake, chronic care management, appointment scheduling, and reducing hospital readmissions. The company has received significant funding and achieved unicorn status in January 2025. Its current status is active.

Assignment timeline

There are no post-issuance assignments recorded for this patent on the USPTO Assignment Center. The information provided in the patent document itself indicates the initial assignment from the inventors to Healthgpt Inc, which is typical for patents filed by employees. The Google Patents legal events section shows:

• 2024-05-31 (assigned) / recorded Not explicitly recorded - Reel 67573/862
  • Conveyance: Assignment
  • Assignor: PARIKH, VISHAL, DATTA, DEBAJYOTI, MILLER, ALEX, BHIMANI, MEENESH, GAMBLE, PAUL, Godil, Saad, LASKO, RAE, MUKHERJEE, SUBHABRATA, SHAH, MUNJAL
  • Assignee: HEALTHGPT, INC. DBA HIPPOCRATIC AI
  • Correspondent: Not specified in this record.
  • Context: Transfer of inventors' interests to the assignee.
• 2024-06-26 (assigned) / recorded Not explicitly recorded - Reel Not explicitly recorded
  • Conveyance: Corrective Assignment
  • Assignor: PARIKH, VISHAL, DATTA, DEBAJYOTI, MILLER, ALEX, BHIMANI, MEENESH, GAMBLE, PAUL, Godil, Saad, LASKO, RAE, MUKHERJEE, SUBHABRATA, SHAH, MUNJAL
  • Assignee: HEALTHGPT, INC. DBA HIPPOCRATIC AI
  • Correspondent: Not specified in this record.
  • Context: Corrective assignment to update correspondence address.

Timeline diagram

timeline
    title Ownership of US 12142371
    2024 : Filed by Healthgpt Inc
    2024 : Issued
    2024-05-31 : Assigned to HEALTHGPT, INC. DBA HIPPOCRATIC AI
    2024-06-26 : Corrective assignment to HEALTHGPT, INC. DBA HIPPOCRATIC AI

NPE / troll-pattern signals

1. Shell-entity transfer — Not present. The assignee, Healthgpt Inc dba Hippocratic AI, is an operating company actively developing and deploying AI-powered healthcare solutions.
2. Known asserter in the chain — Not present. Healthgpt Inc dba Hippocratic AI is not listed as a known patent asserter.
3. Repeat correspondent across the chain — Unclear. The provided assignment records from Google Patents do not specify the correspondent of record.
4. Cascading transfers — Not present. There are only two recorded assignments, both from the inventors to the initial assignee, and one is a corrective assignment.
5. Pre-litigation transfer — Not present. The initial assignments occurred in May and June 2024, while the PTAB case was filed in 2025. This timing does not suggest a transfer specifically to enable assertion.
6. Bankruptcy fire-sale — Not present. Healthgpt Inc dba Hippocratic AI is an active, well-funded company.
7. Privateering — Not present. There is no indication that Healthgpt Inc dba Hippocratic AI has transferred this patent to an NPE to assert on its behalf.
8. Defensive aggregator (anti-NPE) — Not present. The patent remains with Healthgpt Inc dba Hippocratic AI.

Verdict

Operating-company assertion
The patent is currently assigned to Healthgpt Inc dba Hippocratic Ai, which is an operating company actively developing and marketing AI solutions for the healthcare industry. The initial assignments are from the inventors to this operating company, which is a standard practice and does not indicate an NPE pattern. There are no subsequent transfers to shell entities or known patent assertion entities. (Verification: https://assignmentcenter.uspto.gov/ - search for patent number 12142371).

To identify the most relevant prior art for US patent 12142371, I will examine the "References Cited" section of the patent itself, as listed on Google Patents and corroborated by the USPTO's public search tool (if available, which it is). Prior art is typically identified in a patent's "References Cited" section, which includes patents, publications, and other materials that the examiner and applicants considered relevant to the patentability of the claimed invention.

A recent news release from Hippocratic AI on October 1, 2025, and November 27, 2024, mentions that Patent 12,142,371 covers key components of their Polaris system, including its constellation architecture where a main model drives the conversation while other models provide support for accuracy and safety in healthcare. This context will be helpful when evaluating the descriptions of prior art.

Here's an analysis of the prior art cited in US Patent 12142371 (US12142371B1):

Non-Patent Literature Cited:

The patent lists several "OTHER PUBLICATIONS" as references. These are often particularly relevant for rapidly evolving fields like AI.

1. Gao et al. "Retrieval-Augmented Generation for Large Language Models: A Survey."

   • Full Citation: Gao et al. "Retrieval-Augmented Generation for Large Language Models: A Survey," Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Mar. 27, 2024, 21 pages. [Url: arXiv:2312.10997].
   • Publication/Filing Date: March 27, 2024.
   • Brief Description: This survey covers Retrieval-Augmented Generation (RAG) for large language models (LLMs). The patent itself mentions RAG as an AI framework used to retrieve facts from an external knowledge base to provide accurate and up-to-date information to the LLM, grounding it on external sources to supplement the LLM and allow output to be checked for accuracy.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): This reference is highly relevant to the concept of augmenting LLMs with external knowledge for improved accuracy and up-to-dateness. This could potentially anticipate aspects of claims related to the conversation interface utilizing external knowledge or a knowledge graph for generating responses, as described in the abstract and the general description of the system's function (e.g., how the LLM accesses a knowledge graph to reduce latency). The discussion of RAG in the patent (specifically in relation to the RAG module 234 in FIG. 2) directly relates to this publication.

2. Karan et al. "Large Language Models Encode Clinical Knowledge."

   • Full Citation: Karan et al, "Large Language Models Encode Clinical Knowledge," Google Research, Dec. 26, 2022, 44 pages. [arXiv:2212.13138].
   • Publication/Filing Date: December 26, 2022.
   • Brief Description: This publication discusses how large language models (LLMs) encode clinical knowledge. The patent extensively describes using LLMs for specialized healthcare-related functions, such as preventative screenings, intake, scheduling, pre-op, discharge, and chronic care, and states that the AI-based virtual assistant may perform tasks normally handled by medical professionals.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): This reference suggests that the concept of LLMs possessing and utilizing clinical knowledge was known prior to the patent's priority date (May 15, 2023). This could potentially anticipate claims, particularly Claim 1, that describe an LLM-based conversation interface emulating a human healthcare professional and engaging in healthcare-related conversations.

3. Peter et al. "Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine."

   • Full Citation: Peter et al, "Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. The New England Journal of Medicine, Mar. 30. 2023, 7 pages [N Engl Med 388;13].
   • Publication/Filing Date: March 30, 2023.
   • Brief Description: This article evaluates the benefits, limitations, and risks of using GPT-4 as an AI chatbot in medicine. This directly addresses the application of AI, specifically large language models, in a healthcare context, including potential challenges. The patent itself highlights issues with current AI virtual assistants in healthcare, such as incapability of following a checklist, verbosity, and lack of compliance with medical safety laws.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): This reference demonstrates prior awareness and discussion of AI chatbots (like GPT-4) in medical applications, including their shortcomings. This could potentially anticipate broad aspects of claims related to conversational AI in healthcare, particularly the recognition of problems that the claimed invention aims to solve.

4. Tao et al. "Towards Conversational Diagnostic AI."

   • Full Citation: Tao et al, "Towards Conversational Diagnostic AI," Google Research, Jan. 11, 2024, 46 pages. [arXiv:2401.05654].
   • Publication/Filing Date: January 11, 2024.
   • Brief Description: This publication focuses on conversational diagnostic AI. The patent's focus on a multi-turn conversational system in healthcare, particularly one that can identify symptoms and probe medical conditions, aligns with the concept of diagnostic AI.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): Given the publication date is after the priority date of US12142371B1 (May 15, 2023), this document cannot be prior art for the current patent application under 35 U.S.C. § 102 (unless it claims an earlier priority date that predates May 15, 2023, which is not indicated here). However, if it were considered prior art, it would potentially anticipate claims related to conversational AI systems for medical diagnosis, particularly those involving symptom identification and medical condition queries, which are aspects of Claim 1.

Patent Literature Cited (as mentioned in the PGR2025-00075 filing):

The Post-Grant Review (PGR) case PGR2025-00075 against US Patent 12142371 explicitly cites other U.S. Patents as prior art. This is highly significant.

1. US Patent 9,824,188 B2

   • Full Citation: U.S. Patent No. 9,824,188 B2.
   • Publication/Filing Date: Not explicitly stated in the provided text for the filing date, but the grant date for a B2 patent would precede its citation in a PGR.
   • Brief Description: The PGR petition states that "U.S. Patent No. 9,824,188 B2 teaches the control logic comprising a trigger detection logic, a question insertion logic, and an answer classification logic (see Fig.1 (102,104), Fig.3 and Col.9, Line 21-31, receiving a query, determining . . .)". This directly corresponds to components of Claim 1.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): This patent directly anticipates elements of Claim 1, specifically the "control logic" including "trigger detection logic, a question insertion logic, and an answer classification logic." The PGR filing suggests this reference makes Claim 1 obvious when combined with other prior art.

2. US Patent 11,843,565 B2

   • Full Citation: U.S. Patent No. 11,843,565 B2.
   • Publication/Filing Date: Not explicitly stated in the provided text for the filing date.
   • Brief Description: The PGR petition cites this patent in combination with US Patent 9,824,188 B2 to argue obviousness for claims 1-20 of US12142371. Without a detailed description of US Patent 11,843,565 B2, its specific teachings cannot be fully ascertained from the provided text.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): The PGR filing indicates that this patent, in combination with US Patent 9,824,188 B2, is used to argue obviousness for claims 1-20. This suggests it addresses some aspects of conversational AI systems, potentially relating to multi-turn interactions, control mechanisms, or other general AI conversational elements.

3. U.S. Patent Publication No. 2024/0185968 A1

   • Full Citation: U.S. Patent Publication No. 2024/0185968 A1.
   • Publication/Filing Date: Not explicitly stated in the provided text for the filing date.
   • Brief Description: This publication is also cited in the PGR petition in combination with US Patent 9,824,188 B2 to argue obviousness.
   • Potential Anticipated Claim(s) (35 U.S.C. § 102): Similar to US Patent 11,843,565 B2, this publication is cited in combination to argue obviousness for claims 1-20. It would likely touch upon aspects of conversational AI or related technologies that, when combined with 9,824,188 B2, would render the claims of 12,142,371 obvious.

Summary of Most Relevant Prior Art:

Based on the information available, the most relevant prior art appears to be:

• US Patent 9,824,188 B2: This patent is directly cited in the PGR for teaching specific elements of the "control logic" in Claim 1 of US12142371B1, namely trigger detection, question insertion, and answer classification logic. This indicates a strong potential for anticipating or rendering obvious key aspects of Claim 1.
• Karan et al. "Large Language Models Encode Clinical Knowledge" (2022): This non-patent literature directly addresses the core concept of LLMs possessing clinical knowledge for healthcare applications, which is fundamental to the described invention.
• Peter et al. "Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine" (2023): This publication demonstrates the existing landscape and challenges of applying advanced AI chatbots like GPT-4 in medicine, providing a context of known problems that US12142371 aims to address.

The other cited patent references (US 11,843,565 B2 and US 2024/0185968 A1) are mentioned in the context of being combined with US 9,824,188 B2 to argue obviousness, suggesting their relevance as part of a combination. The Gao et al. paper on RAG is relevant to the technical implementation details of how the LLM accesses external knowledge, which can be seen in the RAG module (234) of the AI architecture. The Tao et al. publication is not considered prior art due to its publication date being after the priority date of US12142371B1.

I cannot perform the requested obviousness analysis of US patent 12142371 under 35 U.S.C. § 103, as the provided "Prior Art section" only lists "Prior art keywords" (human, conversation, user, llm, conversational system) and does not include specific prior art references (e.g., patent numbers, publications, or detailed descriptions of specific prior art systems) that can be combined for an obviousness argument.

A proper obviousness analysis requires identifying distinct prior art documents, each disclosing certain elements of the patent's claims, and then articulating a motivation for a person having ordinary skill in the art to combine those specific teachings to arrive at the claimed invention. Without concrete prior art references, such an analysis cannot be conducted.

While the patent text generally references "Current AI virtual assistants (which include chat bots), such as ChatGPT and the like" as having limitations in healthcare, this broad statement does not constitute specific, citable prior art for the purpose of a detailed § 103 combination analysis.