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 .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 12/5/24, 2/13/25, 10/13/25, 2/11/26, 4/17/26, 7/13/26& 7/29/26. The submission are in compliance with the provisions of 37 CFR § 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over CYRIL ZAKKA ET AL: ("Almanac: Knowledge-Grounded Language Models for Clinical Medicine", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 1 March 2023 (2023-03-01), hereinafter ZAKKA) in view of Hubl et al. (US 11354599, hereinafter Hubl).
Regarding Claim 1, ZAKKA discloses a method of configuring a task-specific machine-learning model (Abstract, "Large-language models", "applications in clinical medicine (e.g. medical record documentation, treatment guideline-lookup)"), comprising:
receiving a request from a user to modify a machine-learning model that is configured to perform a specific clinical task (Pages 2 and 3, "queries related to clinical concepts, up-to-date treatment plans, guidelines and recommendations", wherein, considering that LLM models are used, the query is necessarily processed as a plurality of tokens, see also page 2, 1st paragraph, "predicting the next token in a sentence - large language models (LLMs)")
retrieving, based on the machine-learning model, a corresponding node architecture defining a conditional logic for performing the specific clinical task by the machine-learning model (page 7, chapter 4, "we envision that more domain-specific language models will further improve performance", using domain-specific models which necessarily need to match the user input and task/command, see also page 4, paragraph 3, "a variety of clinically useful tasks"), wherein:
the conditional logic is executed in accordance with a first order of a first set of interconnected nodes from a plurality of nodes, the first order includes an input node, at least one output node, and an intermediate node disposed between the input node and the at least output node (Fig. 1 wherein multiple nodes are defined, including a retriever node and an LLM node, a Vector Database node, wherein conditional logic is defined in said Fig. 1, the conditional logic including for instance the fact that LLM is conditioned to process data when it receives input from the Retriever and Browser nodes; another example of conditional logic corresponds to choosing a domain-specific model in the context of multiple possible tasks/commands), and
the first set of interconnected nodes comprises one or more data source nodes, one or more machine-learning model nodes, and one or more conditional logic nodes (Fig. 1 wherein multiple nodes are defined, including a retriever node and an LLM node, a Vector Database node, wherein conditional logic is defined in said Fig. 1, the conditional logic including for instance the fact that LLM is conditioned to process data when it receives input from the Retriever and Browser nodes; another example of conditional logic corresponds to choosing a domain-specific model in the context of multiple possible tasks/commands);
generating, (Fig. 1 wherein multiple nodes are defined, including a retriever node and an LLM node, a Vector Database node, wherein conditional logic is defined in said Fig. 1, the conditional logic including for instance the fact that LLM is conditioned to process data when it receives input from the Retriever and Browser nodes; another example of conditional logic corresponds to choosing a domain-specific model in the context of multiple possible tasks/commands) including:
a first input feature for configuring the conditional logic of the corresponding node architecture, and a second input feature for configuring a parameter of a corresponding node in the first set of interconnected nodes (retrieving data from ("Vector Database") based on a scoring of multiple document: an external data source similarity and sequential see Fig. l and page 6, section 3.2, "a similarity metric such as cosine distance. These vectors can later be retrieved through approximate nearest neighbor search", "The retriever is a text encoder that encodes queries and reference materials into the same high-dimensional space before storing them in the database. This language model is pretrained on domain-specific corpora to ensure that texts with similar content get closer time, vector representations documents matching a in this space. At search given query embedding are scored");
receiving a selection of either the first input feature or the second input feature, wherein the selection of either the first input feature or the second input feature defines a second order of a second set of interconnected nodes from the plurality of nodes (Fig. l and page 6, section 3.2, "a similarity metric such as cosine distance. These vectors can later be retrieved through approximate nearest neighbor search", "The retriever is a text encoder that encodes queries and reference materials into the same high-dimensional space before storing them in the database. This language model is pretrained on domain-specific corpora to ensure that texts with similar content get closer time, vector representations documents matching a in this space. At search given query embedding are scored"); and
updating the conditional logic of the corresponding node architecture in accordance with the second order of the second set of interconnected nodes, thereby configuring how the machine-learning model performs the specific clinical task (page 7, chapter 4, "we envision that more domain-specific language models will further improve performance", using domain-specific models which necessarily need to match the user input and task/command, see also page 4, paragraph 3, "a variety of clinically useful tasks").
ZAKKA does not explicitly disclose display at a remote device
Hubl teaches display at a remote device (Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models, as defined below, to a user of a remote device 112 and permits user to manipulate, move, edit, connect together, and/or otherwise interact with such graphical models 116 and/or combinations thereof. Visual interface 108 may include a window in which graphical models, and/or combinations thereof, to be used may be displayed. Visual interface 108 may include one or more graphical locator and/or cursor facilities allowing a user to interact with graphical models and/or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of display at a remote device as taught by Hubl (Col. 4, ll. 16-27 ) into the machine-learning model system of ZAKKA in order to provide cloud computing as a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service (Hubl, Col. 19, ll. 46-49).
Regarding Claim 2, ZAKKA in view of Hubl discloses the method of claim 1, Hubl discloses wherein the request is generated by the user by selecting and arranging graphical user interface elements within a user interface associated with the corresponding node architecture (Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models).
The same reason or rational of obviousness motivation applied as used above in claim 1.
Regarding Claim 3, ZAKKA in view of Hubl discloses the method of claim 2, ZAKKA discloses wherein the user interface comprises an agent builder component in a control plane of the computer system Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models).
The same reason or rational of obviousness motivation applied as used above in claim 1.
Regarding Claim 4, ZAKKA in view of Hubl discloses the method of claim 1, Hubl discloses wherein the request comprises a plurality of text data comprising one or more text strings inputted by the user (Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models, as defined below, to a user of a remote device 112 and permits user to manipulate, move, edit, connect together, and/or otherwise interact with such graphical models 116 and/or combinations thereof. Visual interface 108 may include a window in which graphical models, and/or combinations thereof, to be used may be displayed. Visual interface 108 may include one or more graphical locator and/or cursor facilities allowing a user to interact with graphical models and/or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device).
The same reason or rational of obviousness motivation applied as used above in claim 1.
Regarding Claim 5, ZAKKA in view of Hubl discloses the method of claim 1, Hubl discloses wherein the specific clinical task comprises: (i) generating a summary report of a patient's medical records, (ii) guiding a patient through a care plan, (iii) creating patient care guidelines based on a patient's health profile, (iii) identifying patients requiring follow-up at a hospital, (v) identifying changes in a standard of care for a disease setting, or (vi) evaluating unstructured data associated with a patient to identify a cohort of similar patients (Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models, as defined below, to a user of a remote device 112 and permits user to manipulate, move, edit, connect together, and/or otherwise interact with such graphical models 116 and/or combinations thereof. Visual interface 108 may include a window in which graphical models, and/or combinations thereof, to be used may be displayed. Visual interface 108 may include one or more graphical locator and/or cursor facilities allowing a user to interact with graphical models and/or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device).
The same reason or rational of obviousness motivation applied as used above in claim 1.
Regarding Claim 6, ZAKKA in view of Hubl discloses the method of claim 1, Hubl discloses wherein the input node is configured to receive a prompt from a user associated with the specific clinical task (Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models, as defined below, to a user of a remote device 112 and permits user to manipulate, move, edit, connect together, and/or otherwise interact with such graphical models 116 and/or combinations thereof. Visual interface 108 may include a window in which graphical models, and/or combinations thereof, to be used may be displayed. Visual interface 108 may include one or more graphical locator and/or cursor facilities allowing a user to interact with graphical models and/or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device).
The same reason or rational of obviousness motivation applied as used above in claim 1.
Regarding Claim 7, ZAKKA in view of Hubl discloses the method of claim 1, ZAKKA discloses wherein the output node is configured to generate a response to the request from the user based on a respective task-specific machine-learning model associated with the output node.
(abstract, "Large-language models", "applications in clinical medicine (e.g. medical record documentation, treatment guideline-lookup)" + Fig. 1; page 7, chapter 4, "we envision that more domain-specific language models will further improve performance").
Regarding Claim 8, ZAKKA in view of Hubl discloses the method of claim 1, ZAKKA discloses wherein each respective machine-learning model node in the one or more machine-learning model nodes is configured to obtain information corresponding to the request using a corresponding domain associated with the respective machine-learning model (abstract, "Large-language models", "applications in clinical medicine (e.g. medical record documentation, treatment guideline-lookup)" + Fig. 1; page 7, chapter 4, "we envision that more domain-specific language models will further improve performance").
Regarding Claim 9, ZAKKA in view of Hubl discloses the method of claim 1, Hubl discloses wherein each respective machine-learning model node in the one or more machine-learning model nodes includes one or more parameters and one or more functions for interacting with other nodes in the plurality of nodes(Col. 4, ll. 16-27, A “visual interface,” as used in this disclosure, graphical user interface (GUI) that displays graphical models, as defined below, to a user of a remote device 112 and permits user to manipulate, move, edit, connect together, and/or otherwise interact with such graphical models 116 and/or combinations thereof. Visual interface 108 may include a window in which graphical models, and/or combinations thereof, to be used may be displayed. Visual interface 108 may include one or more graphical locator and/or cursor facilities allowing a user to interact with graphical models and/or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device).
The same reason or rational of obviousness motivation applied as used above in claim 1.
Regarding Claim 10, ZAKKA in view of Hubl discloses the method of claim 1, ZAKKA discloses further comprising generating a configuration file for the corresponding node architecture, the configuration file setting a working environment for the corresponding node architecture and one or more type-specific machine learning models associated with the corresponding node architecture(abstract, "Large-language models", "applications in clinical medicine (e.g. medical record documentation, treatment guideline-lookup)" + Fig. 1; page 7, chapter 4, "we envision that more domain-specific language models will further improve performance").
Regarding Claims 11-15, System claims 11-15 of using the corresponding method claimed in claims 1-6, and the rejections of which are incorporated herein for the same reasons as used above.
Regarding Claims 16-20, Computer-readable storage medium claims 16-20 of using the corresponding system claimed in claims 11-15, and the rejections of which are incorporated herein for the same reasons as used above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samuel D Fereja whose telephone number is (469)295-9243. The examiner can normally be reached 8AM-5PM.
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/SAMUEL D FEREJA/Primary Examiner, Art Unit 2487
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