Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The USPTO “Interim Guidelines for Examination of Patent Applications for Patent Subject Matter Eligibility” (Official Gazette notice of 23 February 2010), Annex IV, reads as follows:
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. § 101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation "non-transitory" to the claim. Cf. Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. § 101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473(Fed. Cir. 1998).
Claim(s) 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 15 defines a “computer program product” embodying functional descriptive material. However, the claim does not define a non-transitory computer-readable medium or memory and is thus non-statutory for that reason (i.e., “examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In see Official Gazette Notice 1351 OG212, February 23,2010). That is, the scope of the presently claimed “computer program product ” typically covers forms of non-transitory tangible media and transitory propagating signals per se. The examiner suggests amending the claim to add the limitation ”non-transitory ” to the claim or equivalent in order to make the claim statutory. Any amendment to the claim should be commensurate with its corresponding disclosure.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anantraman et al (WO 2025/111558) in view of Dwivedi et al ( US 2025/0201367).
As to claim 1, ANANTRAMAN et al teaches the computer-implemented method, executed on a computing device, comprising:
generating an internal state prompt with medical content and a multi-action task to perform on a healthcare system (a prompt input referencing healthcare data included in an electronic health record (EHR), determine, by the one or more processors executing a trained machine learning (ML) model of an agent object, a user intent corresponding to the prompt input indicating retrieval of the healthcare data from the EHR, transmit, by the one or more processors via the agent object and through an associated application programming interface (API), paragraph [0008]);
generating a first output healthcare system command by processing the internal state prompt ( a prompt output that includes the healthcare data, and cause, by the one or more processors, the prompt output to be displayed in an output device, paragraph [0008], note that the prompt output includes the post-processed healthcare data)
using a trained multimodal generative artificial intelligence (AI) model (a multi-modal machine learning model configured to output multi-modal data associated with the input healthcare data, paragraph [0010]);
converting the first output healthcare system command into a first healthcare system-executable command associated with the multi-action task for a first target healthcare subsystem ( the agent objects 206-A-N may be connected to and/or otherwise have access to any suitable number of tools 207a-c, and these tools may perform any suitable action(s) or combinations thereof; paragraph [0122-0123]). While Amantraman et al teaches the limitation .Amantraman fails to teach “ generating modified medical content by executing the first healthcare system-executable command on the medical content using the first target healthcare subsystem; updating the internal state prompt with the modified medical content generated by executing the first healthcare system-executable command and the first output healthcare system command listed as a past action performed during execution of the multi-action task; and generating additional output healthcare system commands by processing the updated internal state prompt using the trained multimodal generative AI model with the first output health as context for generating the additional output healthcare system commands to perform the multi-action task. “
Dwivedi et al teaches a processor that executes computer-executable components stored in a non-transitory computer-readable memory, wherein the computer-executable components comprise: an access component that accesses cardiac data associated with a medical patient; a retrieval component that searches a dynamic cardiology publication repository for one or more cardiology publications that are relevant to the cardiac data of the medical patient and that are published within a threshold margin of a current time or date; and a generative component that synthesizes a natural language cardiology report for the medical patient, by executing a deep learning neural network on both the cardiac data and the one or more cardiology publications(abstract). Dwiveda et al teaches such execution-and-update procedure can be repeated for any suitable number of training inputs (e.g., for each training input in the training dataset 604). This can ultimately cause the trainable internal parameters of the deep learning neural network 402 to become iteratively optimized for accurately generating natural language cardiology reports based on inputted cardiac data (which inputted cardiac data may or may not be accompanied by recent and relevant cardiology publications; paragraph[0118]). It would have been obvious to one skilled in the art before filing of the claimed invention to use the execution-and-update procedure of Dwiveda in order to synthesize a natural language medical report for the medical patient, by executing a deep learning neural network on both the medical data and the one or more medical publications. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2, ANANTRAMAN et al teaches the computer-implemented method of claim 1, wherein generating additional output healthcare system commands includes: generating a second output healthcare system command by processing the updated internal state prompt using the trained multimodal generative AI model ( The agent-controller configuration 200 may include any number of agent objects. For example, the agent-controller configuration 200 depicts an agent object 206-A, which is the first agent object, an agent object 206-B, which is the second agent object, an agent object 206-C, which is the third agent object, an agent object 206-D, which is the fourth agent object and an agent object 206-E, which is the nth agent object, wherein n is a positive natural number. [122] Moreover, as illustrated in FIG. 2, agent objects 206-A and 206-B may access, call, and/or otherwise utilize one or more tools 207a, 207b, 207c, which may be configured to perform various actions related to a patient’s HER, paragraph [0121]); converting the second output healthcare system command into a second healthcare system-executable command associated with the multi-action task for a second target healthcare subsystem; generating modified medical content by executing the second healthcare system-executable command on the second target healthcare subsystem; and updating the internal state prompt with the modified medical content generated by executing the second healthcare system-executable command and the second output healthcare system command listed as a past action performed during execution of the multi-action task (wherein the prompt output includes the post-processed healthcare data. Further in this variation, the post-processed healthcare data includes at least one of: (i) a graphical representation of the healthcare data, (ii) a collated document that includes the healthcare data, (iii) an image included as part of the healthcare data, (iv) a selectable link that directs users to the healthcare data in a source location, or (v) a chat option configured to connect a user with a second user that is associated with the healthcare data and/or indicated in the prompt input or the user intent, the trained ML model of the agent object is (a) a large language model (LLM) that is configured to receive input healthcare data and generate a prompt output based on the input healthcare data or (b) a multi-modal machine learning model configured to output multi-modal data associated with the input healthcare data, paragraph [0126-0127]).
As to claim 3, ANANTRAMAN et al teaches the computer-implemented method of claim 1, wherein the multimodal generative AI model is one of a large multimodal model (LMM) and a large language model (LLM) (the trained ML model of the agent object is (a) a large language model (LLM) that is configured to receive input healthcare data and generate a prompt output based on the input healthcare data or (b) a multi-modal machine learning model configured to output multi-modal data associated with the input healthcare data, paragraph [0127]).
As to claim 4, ANANTRAMAN et al teaches the computer-implemented method of claim 1, wherein the medical content includes medical image content ( image, paragraph [0105]) .
As to claim 5, ANANTRAMAN et al teaches the computer-implemented method of claim 1, wherein the multi-action task includes a series of actions to process medical content for a diagnostic-based task(retrieval augmented generation applied in this manner allows the model to dynamically generate outputs that are more relevant to the user’s input query at runtime. Information that may be retrieved may include data corresponding to a patient (e.g., patient demographic information, medical history, clinical notes, diagnoses, medications, allergies, immunizations, laboratory results, oncology information, radiation and imaging information, vitals, etc.) and additional training information, such as medical journals, notes or speech transcripts from symposia or other meetings/conferences, etc., paragraph [0107]).
As to claim 6, ANANTRAMAN et al teaches the computer-implemented method of claim 1, further comprising: training the generative AI model by providing a graphical user interface to a user and recording each action performed by the user on the graphical user interface( a user may access a prompt graphical user interface via the client computing device 102. The prompt graphical user interface may be configured, generated, and/or displayed by the input processing module 146 via the output device 128. Specifically, the input processing module 146 may configure the graphical user interface to accept prompts and display corresponding prompt outputs generated by one or more models processing the accepted outputs. The input processing module 146 may also be configured to transmit the prompts input by the user via the network electronic network 106 to the AP1 166 of the server computing device 104. The AP1 166 may process the user inputs via one or more trained models, or agents, paragraph [0117]) and each action performed on healthcare subsystems within the healthcare system to accomplish the multi-action task concerning the medical image content(t the time the user accesses the prompt graphical user interface, one or more models may already be trained, including pretraining and fine-tuning. These trained models may be selectively loaded into the one or more agent objects based on configuration parameters, and/or based upon the content of the user’s input prompts. Further, in some aspects, the user may engage in a question-answer session with the client computing device 102, paragraph [0118]).
As to claim 7, ANANTRAMAN et al teaches the computer computer-implemented method of claim 1, further comprising: training the generative AI model by: processing medical image content(a prompt may be processed by the input processing module 146 of the client computing device 102, prior to processing the prompt by the one or more generative models. The input processing module 146 may trigger retrieval augmented generation based on the presence of certain inputs, such as patient information, or a request for specific information, in the form of keywords. The input processing module 146 may perform entity recognition or other natural language processing functions to determine whether the prompt should be processed using retrieval augmented generation prior to being provided to the trained model, paragraph [0109]) , a medical report concerning the medical image content, and a plurality of predefined medical image content boundaries associated with a first medical feature within the medical image content (when the chatbots/systems described herein analyze data/f Iles incorporated/stored as part of the EHR, the chatbots may associate these documents/files with their outputs to be able to provide the citation and the full text review after the practitioner interacts with the interactive report link. This fifth GUI 800 further includes a "generate summary" option, which may allow practitioners to obtain a concise summary of the document based on the information they are interested in, as indicated in their initial query and/or the output of the chatbot., paragraph [0147]); generating a first action to navigate within the medical image content to the first medical feature using the predefined medical image content boundaries associated with the first medical feature, wherein the first action defines an initial state for the internal state prompt; generating a plurality of actions to process the first medical feature within the medical image content, wherein the plurality of actions define subsequent actions that update the internal state prompt; and generating a summarizing action to provide results from the medical report concerning the first medical feature within the medical image content( receiving, at the one or more processors, a subsequent prompt input related to the prompt output; determining, by the one or more processors executing the trained ML model of the agent object, a subsequent user intent corresponding to the subsequent prompt input; transmitting, by the one or more processors via the agent object and through a second API, a subsequent control instruction for a second digital tool to perform an action related to the subsequent user intent, wherein the second digital tool is different from the digital tool; generating, by the one or more processors executing the trained ML model, a subsequent prompt output based on the action performed by the second digital tool; and causing, by the one or more processors, the subsequent prompt output to be displayed in the output device., paragraph[0129-0130]; wherein the trained ML model of the agent object is (a) a large language model (LLM) that is configured to receive input healthcare data and generate a prompt output based on the input healthcare data or (b) a multimodal machine learning model configured to output multi-modal data associated with the input healthcare data, paragraph[0170]).
The limitation of claims 8-20 has been addressed above.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached Mon-Friday from 8:00 am to 5:00 p.m..
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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