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 .
1. This communication is responsive to the application filed 9/20/2024.
2. Claims 1-20 are pending in this application. Claims 1 and are independent claims. This action is made Non-Final.
Claim Rejections - 35 USC § 103
3. 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.
4. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Masood et al (“Masood” US 2025/0062035) in view of Marcjan et al (“Marcjan” US 2024/0087687).
Regarding claim 1, Masood discloses a data processing apparatus for obtaining an output corresponding to a medical text input (see the Abstract), the apparatus comprising processing circuitry configured to:
provide a medical text input, or data derived from the medical text input, to a trained model (see paragraphs [0020]-[0022]; e.g., ”the patient records 112 stored in the repository 106 can be received from the client device 104. The similarity engine 122 uses the language model 110 to determine associations and patterns in the patient records 112 that match those in the reference documents 114.”);
provide instructions to the trained model to match the medica text input to the ontology (see paragraphs [0020]-[0022]; e.g., ”the patient records 112 stored in the repository 106 can be received from the client device 104. The similarity engine 122 uses the language model 110 to determine associations and patterns in the patient records 112 that match those in the reference documents 114.”); and
output text, or other data (see paragraphs [0014], [0031], [0035], [0046], [0050] and [0072]; e.g., “Different outputs of the language model 110 can be observed for the same input. For example, the patient records 112 can be provided to the initially trained language model 110 to observe the different outputs of the initially trained language model 110. Potential diagnoses (i.e., outputs) of the language model 110 can be ranked and assigned scores based on their relevancy and accuracy.”).
Masood does not expressly disclose provide instructions to the trained model to repeatedly assess layers of a hierarchical ontology that comprises a plurality of nodes at each of a plurality of layers, thereby to determine path(s) through the layers via node(s) in successive layers that are connected according to the hierarchical ontology and that match the medical text input; and select at least one node from the node(s) at the end or other point(s) of the determined path(s), or select no nodes if there are no suitable matches of the medical text input to nodes.
However, Marcjan discloses provide instructions to the trained model to repeatedly assess layers of a hierarchical ontology that comprises a plurality of nodes at each of a plurality of layers, thereby to determine path(s) through the layers via node(s) in successive layers that are connected according to the hierarchical ontology and that match the medical text input; select at least one node from the node(s) at the end or other point(s) of the determined path(s), or select no nodes if there are no suitable matches of the medical text input to nodes (see paragraphs [0017]-[0019], [0028] and [0030]-[0031]; e.g., ontology matching; determine path through nodes; and output selected node in hierarchical ontology based on matching). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to include teachings in user interface in an effort to provide a more user-friendly interface that saves user time and resources.
Regarding claim 2, Marcjan discloses wherein the assessment by the trained model comprises performing a recursive tree search of the hierarchical ontology to determine the path(s) through the ontology (see paragraphs [0017]-[0019] and [0030]-[0031]).
Regarding claim 3, Marcjan discloses wherein the selection of path(s) is such that only a sub-set of nodes of the hierarchical ontology are subject to assessment by the trained model in order to obtain the selected nodes, and/or wherein the selection of path(s) is such as to provide only sparse exploration of the hierarchical ontology in order to obtain the selected nodes (see paragraphs [0017]-[0019] and [0030]-[0031]).
Regarding claim 4, Marcjan discloses wherein the providing of instructions and/or the providing of the medical text input comprises providing one or more prompts to the model, wherein the prompt(s) comprise medical text input or derived data, and/or an in-context training example, and/or an information retrieval task relating to one or more of the nodes (see paragraphs [0017]-[0019] and [0030]-[0031]).
Regarding claim 5, Marcjan discloses wherein the providing of instruction comprises, for each node of the path(s)instructing the trained model to determine if any text associated with child nodes of said node is included in, or otherwise matches, the medical text input (see paragraphs [0017]-[0019] and [0030]-[0031]).
Regarding claim 6, Marcjan discloses wherein the processing circuity is configured to perform a post-processing step to eliminate one or more of the selected nodes, or their associated text (see paragraph [0015]).
Regarding claim 7, Marcjan discloses wherein the processing circuity is configured to instruct the trained model, or a further trained model, to perform the post-processing step (see paragraph [0015]).
Regarding claim 8, Marcjan discloses wherein the instructing of the trained model, or the further trained model, to perform the post-processing step comprises providing at least some rules or other properties of the hierarchical ontology to the trained model or the further trained model thereby to assist in eliminating false positives (see paragraph [0034]).
Regarding claim 9, Marcjan discloses wherein the assessment by the trained model comprises using the trained model as a discriminator function in a multi-label decision tree process performed on the hierarchical ontology (see paragraphs [0013] and [0030]).
Regarding claim 10, Marcjan discloses wherein the discriminator function acts on text associated with nodes of the hierarchical ontology to select relevant nodes thereby to obtain the path(s) through the layers via node(s) in successive layers (see paragraphs [0017]-[0019] and [0030]-[0031]).
Regarding claim 11, Marcjan discloses wherein the trained model is instructed to select a node as relevant if there is a match between at least some of the text associated with the node and at least some of the text of the medical text input (see paragraphs [0030]-[0032]).
Regarding claim 12, Masood discloses wherein the model is trained on training data that includes text that is different from or additional to text of the hierarchical ontology (see claim 1 above).
Regarding claim 13, Marcjan discloses wherein the model is trained on a training data set that does not include training data for at least some nodes of the hierarchical ontology and/or text associated with those nodes, and/or wherein the trained model is such as to perform a zero shot process in respect of at least some of the nodes (see paragraph [0015]).
Regarding claim 14, Masood discloses wherein the model comprises a large language model (LLM) or other language model, and/or where the providing of instructions to the model comprises sending instructions via an API to provide desired input to the model (see paragraph [0031]).
Regarding claim 15, Masood discloses wherein the model comprises at least one of GPT-2, GPT-3.5, GPT-4, PaLM, LLaMa, BLOOM, Ernie, T5, Claude or Claude 2, or any suitable derivatives or developments thereof (see paragraph [0035]).
Regarding claim 16, Masood discloses wherein the hierarchical ontology comprises the International Classification of Disease (ICD), SNOMED CT, Radlex or other diagnostic code ontology (see paragraph [0035]).
Regarding claim 17, Masood discloses wherein the medical text input comprises at least one of medical notes for a patient or other subject, results of a diagnostic or other procedure, test or scan results or text associated with such results (see claim 1 above).
Regarding claim 18, Masood discloses wherein the output comprises a text or code input for use in at least one of billing, audit, resource management, epidemiological study, measurement of treatment effectiveness, insurance processing, or enhancement of medical records (see paragraph [0056]).
Regarding claim 19, Masood discloses wherein the outputting of the text, or other data, associated with the selected at least one node comprises generating the text, or other data, using the or a trained model (see claim 1 above).
Claim 20 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
El Saadawi et al (US 2024/0135108).
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHAWN N TILLERY whose telephone number is (571)272-6480. The examiner can normally be reached M-F 9:00a - 5:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William L Bashore can be reached at (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHAWN N TILLERY/Primary Examiner, Art Unit 2174
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