DETAILED ACTION
This action is in response to the initial filing filed on June 4, 2025. Claims 1-17 have been examined and are currently pending.
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 Statement filed on August 4, 2025 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
ALICE/ MAYO: TWO-PART ANALYSIS
2A. First, a determination whether the claim is directed to a judicial exception (i.e., abstract idea).
Prong 1: A determination whether the claim recites a judicial exception (i.e., abstract idea).
Groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations.
Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Mental processes- concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
Prong 2: A determination whether the judicial exception (i.e., abstract idea) is integrated into a practical application.
Considerations indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception
Considerations that are not indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
2B. Second, a determination whether the claim provides an inventive concept (i.e., Whether the claim(s) include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
Considerations indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception NOTE: The only consideration that does not overlap with the considerations indicative of integration into a practical application associated with step 2A: Prong 2.
Considerations that are not indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. NOTE: The only consideration that does not overlap with the considerations that are not indicative of integration into a practical application associated with step 2A: Prong 2.
See also, 2019 Revised Patent Subject Matter Eligibility Guidance; Federal Register; Vol. 84, No. 4; Monday, January 7, 2019
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
1: Statutory Category
Applicant’s claimed invention, as described in independent claim 1 is directed to a method, independent claim 9 is directed to an apparatus, and independent claim 17 is directed a nontransitory computer readable storage medium.
2(A): The claim(s) are directed to a judicial exception (i.e., an abstract idea).
PRONG 1: The claim(s) recite a judicial exception (i.e., an abstract idea).
Certain Methods of Organizing Human Activity
Independent claims 1, 9, and 17 recite the limitations, “obtaining a target manifestation text corresponding to a target medical examination; extracting medical examination inference knowledge that matches the target manifestation text from a medical examination inference knowledge base, wherein the medical examination inference knowledge comprises a manifestation text and a conclusion text corresponding to a medical examination;” are directed to the abstract idea of certain methods of organizing human activity. In particular the limitation, “extracting medical examination inference knowledge that matches the target manifestation text from a medical examination inference knowledge base, wherein the medical examination inference knowledge comprises a manifestation text and a conclusion text corresponding to a medical examination” is interpreted/directed to as filtering content. As per MPEP 2106.04(a)(2)(II)(C), the function of “filtering content” is directed to the abstract idea of managing personal behavior under the abstract idea of certain methods of organizing human activity.
PRONG 2: The judicial exception (i.e., an abstract idea) is not integrated into a practical application.
The applicant has not shown or demonstrated any of the requirements described above under "integration into a practical application" under step 2A. Specifically, the applicant's limitations are not "integrated into a practical application" because they are adding words "apply it" with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea (see MPEP 2106.05(f)). Additionally, improvements to the functioning of a computer or any other technology or technical field has not been shown or disclosed (see MPEP 2106.05(a)). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the applicant’s limitations are not “significantly more” because they are adding words “apply it” with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea (see MPEP 2106.05(f)). The applicant’s claimed limitations do not demonstrate an improvement to another technology or technical field, an improvement to the functioning of the computer itself, effecting a transformation or reduction of particular article to a different state or thing. The current application does not amount to 'significantly more' than the abstract idea as described above. The claim does not include additional elements or limitations individually or in combination that are sufficient to amount to significantly more than the judicial exception. Specifically, the individual elements of processors, memory, non-transitory computer storage medium amount to no more than implementing an idea with a computerized system and they are adding words “apply it” with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea. The additional elements taken in combination add nothing more than what is present when the elements are considered individually. Therefore, based on the two-part Alice Corp. analysis, there are no meaningful limitations in the claims that transform the exception (i.e., abstract idea) into a patent eligible application.
Dependent claims 2-8 and 10-16 are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to the claims from which they depend. The following dependent claims: dependent claims 10-14 and 16 recite a processor. Dependent claims 10-14 and 16 does not recite additional elements that amount to significantly more than the judicial exception.
Since the claim(s) recite a judicial exception and fails to integrate the judicial exception into a practical application, the claim(s) is/are “directed to” the judicial exception. Thus, the claim(s) must be reviewed under the second step of the Alice/ Mayo analysis to determine whether the abstract idea has been applied in an eligible manner.
2(B): The claims do not provide an inventive concept (i.e., The claim(s) do not include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
As discussed with respect to Step 2A Prong Two, the additional element(s) in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Additionally, receiving data and searching a database is well-known routine and conventional as stated in the MPEP 2106.05(d)(II). Therefore, the limitations are not “significantly more” under step 2B.
For these reasons, there is no invention concept in the claim, and thus the claim is ineligible.
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) 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Glottmann et al. US Publication 20200043600 A1 in view of Forsberg et al. US Publication 20190287665 A1 further in view of Grbic et al. US Publication 20250068668 A1.
Claims 1, 9, and 17:
As per claims 1, 9, and 17, Glottmann teaches a method, apparatus, and non-transitory computer readable medium comprising:
obtaining a target manifestation text corresponding to a target medical examination (paragraphs 0031 and 0033 “Further, the platform 100 may be configured to receive textual data by way of being communicatively coupled to hospital information systems 105 and/or reporting systems 107. The hospital information systems 105 and/or reporting systems 107 may provide radiological reports, radiology diagnosis, electronic health records, referring letters, related metadata, information containing medical text statements, and the like. In some embodiments, the textual data may be received using DICOM, SR, Health Level 7 (HL7) protocol, JavaScript Object Notation (JSON), eXtensible Markup Language (XML) formats, using application programming interfaces and the like. In some embodiments, the hospital information system 105 may be integrated into an IT system in a hospital that is configured to manage medical records including radiology reports. Such a system may be configured to distribute messages in HL7 format and the like. In some embodiments, the reporting systems 107 may include dedicated software configured to write radiology reports. The reporting systems 107 may be capable of distributing reports through HL7 protocol and/or application programming interface (API) calls.” and “The present disclosure is related to systems and methods for the improved analysis and generation of medical imaging reports. In particular, embodiments of the present disclosure may include receiving medical images and textual data and generating improved medical imaging reports by applying artificial intelligence and natural language processing techniques. For example, medical images may include images from medical image archives and scanning devices such as magnetic resonance imaging (MRI), computed tomography (CT), X-Rays and the like. Additionally, textual data may include radiological reports, electronic health records, referring letters, and the like.”);
Glottmann does not teach extracting medical examination inference knowledge that matches the target manifestation text from a medical examination inference knowledge base, wherein the medical examination inference knowledge comprises a manifestation text and a conclusion text corresponding to a medical examination. However, Forsberg teaches a Template-Based Medical Summary Interface Generation System and further teaches, “Concept component 34 is configured to identify concepts in the obtained medical report. In some embodiments, the concepts are associated with one or more medical conditions experienced by subject 12 and/or other topics. The concepts are identified based on the parsed and associated words and/or phrases, and/or other information. In some embodiments, concept component 34 is and/or includes a concept extraction engine configured to extract concepts in narrative text based on lexical descriptions of concepts in a contextual knowledge database and/or based on other information. In some embodiments, concept component 34 is and/or includes a concept extraction engine such as MetaMap and/or other concept extraction engines. In some embodiments, words and/or phrases of interest are associated with two or more concepts identified by concept component 34.” (paragraph 0025) and “In some embodiments, responsive to the receipt of the words and/or phrases of interest and the determination of a concept and/or concepts associated with those words and/or phrases of interest, template component 38 accesses the contextual knowledge database, and searches the context knowledge database to locate the concept. Template component 38 accesses a list (for example) of concepts used in the templates (e.g., “anatomy”, “device”, “diagnosis”, etc.). Template component 38 traverses the abstraction layers of the contextual knowledge database until a match is established between the transmitted concept and the templates' concepts (e.g., “pneumonia” and “disease”). In some embodiments, these abstraction steps are implemented based on pre-existing relationships between concepts. For example, template component 38 may start from the transmitted concept (e.g., “pneumonia”) and generalize along the “is-a” relation until it hits a concept that is used in one of the templates. Thus, the concept may be generalized until it reaches the root node of the ontology. Template component 38 is configured to return words and/or phrases associated with the identified concept and/or related concepts (e.g., “pneumonia” can potentially match “disease” and “anatomy” (lungs) in the database for populating the template.” (paragraph 0033). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include extracting medical examination inference knowledge that matches the target manifestation text from a medical examination inference knowledge base, wherein the medical examination inference knowledge comprises a manifestation text and a conclusion text corresponding to a medical examination as taught by Forsberg in order to identify similar terms or medical terms associated with a medical or health condition.
Glottmann and Forsberg do not teach constructing a sample based on the extracted medical examination inference knowledge, and constructing a prompt text based on the sample and the target manifestation text. However, Grbic teaches a Clinical Workflow Efficiency Using Large Language Models and further teaches, “At step 102 of FIG. 1, one or more prompts comprising 1) patient data retrieved from one or more patient databases and 2) instructions are received. A prompt refers to the input text to an LLM for generate a response. A prompt is typically provided by a user to enable the user to interact with the LLM. The one or more prompts may be received from a computing device (e.g., computer 902 of FIG. 9) with which a user is interacting.” (paragraph 0022), “The one or more prompts comprises patient data retrieved from one or more patient databases. The patient databases may include, for example, an EHR (electronic health record), EMR (electronic medical record), PHR (personal health record), HIS (health information system), RIS (radiology information system), PACS (picture archiving and communication system), LIMS (laboratory information management system), or any other database or system suitable for storing patient data.” (paragraph 0023) and “In one embodiment, the patient data comprises text-based data of one or more patients, such as, e.g., medical records, laboratory reports, radiology reports, indications for imaging/examination, demographic information, administrative data, etc. stored across the one or more patient databases. In another embodiment, the patient data comprises medical images of one or more patients stored across the one or more patient databases. In this embodiment, the medical images may be automatically retrieved from the one or more patient databases but may additionally or alternatively be received as user input via the one or more prompts. The patient data may include any other suitable data of a patient. The patient data may be represented in the form of unstructured free text, tables, or any other suitable format using different nomenclature, for example, where the patient data is retrieved from a plurality of different patient databases. In one example, as shown in workflow 200 of FIG. 2, the patient data may be patient information 202 comprising indication for exam 206-A, reports 206-B, and imaging 206-C respectively retrieved from EMR 204-A, radiology information system 204-B, and PACS 204-C.” (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottman to include constructing a sample based on the extracted medical examination inference knowledge, and constructing a prompt text based on the sample and the target manifestation text as taught by Grbic in order to analyze instructions and terms for the next activity or action.
Glottmann and Forsberg do not teach and inputting the prompt text into a large language model, and outputting, by using the large language model, a target conclusion text that corresponds to the target medical examination and that is obtained by performing inference based on the target manifestation text and under guidance of the sample. However, Grbic teaches a Clinical Workflow Efficiency Using Large Language Models and further teaches, “The one or more prompts also comprise an instruction to perform a medical task on the patient data. An instruction refers to guidelines or directions provided to guide the behavior and output of the LLM. An instruction may include commands, questions, constraints, requirements, or any other guideline or direction guiding the behavior and output of the LLM. The one or more prompts may include any other information for performing the medical task, such as, e.g., contextual data.” (paragraph 0026), “At step 104 of FIG. 1, in response to receiving the one or more prompts, a response summarizing the patient data is generated based on the instructions using a large language model. In one embodiment, the response comprises a text-based response (e.g., represented as sentences, phrases, or any other suitable format). However, the response may comprise a non-text-based response represented in any other suitable format (e.g., using an auxiliary machine learning based network). In one example, as shown in workflow 200 of FIG. 2, the large language model is large language model 208, which generates a response summarizing patient information 202. Lage language model 208 provides connectivity/access, interpretation support, and synthesis of summaries of patient information 202.” (paragraph 0027), “In accordance with one or more embodiments, systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instructions using a large language model. The response is output.” (paragraph 0003), and “In one embodiment, the LLM is constrained to a specific medical domain. For example, the LLM may be constrained for the radiology use case for summarizing radiology-related patient data. To constrain the LLM to the radiology use case, the LLM may be updated (e.g., trained, retrained, or fine-tuned) using, e.g., clinical data and data extracted from medical images using AI-based systems. Such extracted data may include, e.g., clinical measurements (e.g., diameters, volumes, distances, etc.), anatomical locations, detections, etc. The constrained LLM may automatically generate a response as output based on such clinical measurements. FIG. 3 shows a workflow 300 for automatically generating a text-based response summarizing clinical measurements extracted from a medical image, in accordance with one or more embodiments. In workflow 300, one or more AI-based systems automatically detect and segment pulmonary nodule 304 is chest CT (computed tomography) image 302. An LLM, constrained for the radiology use case, receives as input clinical measurements and other findings of the AI-based systems (as patient data) and generates as output a text-based response 306 summarizing the clinical measurements and other findings as follows: “There is a 12 mm (millimeter) solid pulmonary nodule in the upper right lobe.” (paragraph 0029). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include inputting the prompt text into a large language model, and outputting, by using the large language model, a target conclusion text that corresponds to the target medical examination and that is obtained by performing inference based on the target manifestation text and under guidance of the sample as taught by Grbic in order to generate a summary of the patient’s medical examination.
Claims 2 and 10:
As per claims 2 and 10, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 1 and 9 as described above and Forsberg further teaches wherein the medical examination inference knowledge further comprises a descriptive text of an inference step of inferring the conclusion text from the manifestation text (paragraphs 0025 and 0033). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include wherein the medical examination inference knowledge further comprises a descriptive text of an inference step of inferring the conclusion text from the manifestation text as taught by Forsberg in order to identify or search medical terms or medical conditions based on the medical examination.
Grbic further teaches and the sample is a chain-of-thought sample (paragraph 0026-0028). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include the sample is a chain-of-thought sample as taught by Grbic in order to allow the system to perform reasoning steps to complete or perform an activity.
Grbic further teaches and the inputting the prompt text into a large language model, and outputting, by using the large language model, a target conclusion text that corresponds to the target medical examination and that is obtained by performing inference based on the target manifestation text and under guidance of the sample comprises: inputting the prompt text into the large language model, and outputting, by using the large language model, a target descriptive text of an inference step of performing inference based on the target manifestation text and under guidance of the chain-of-thought sample and the inferred target conclusion text corresponding to the target medical examination (paragraphs 0026, 0027, and 0003). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include inputting the prompt text into the large language model, and outputting, by using the large language model, a target descriptive text of an inference step of performing inference based on the target manifestation text and under guidance of the chain-of-thought sample and the inferred target conclusion text corresponding to the target medical examination as taught by Grbic in order to generate a summary of the patient’s medical examination.
Claims 3 and 11:
As per claims 3 and 11, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 2 and 10 as described above and Forsberg further teaches further comprising:
performing regular verification on the target descriptive text and the target conclusion text (paragraphs 0025 and 0033). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include performing regular verification on the target descriptive text and the target conclusion text as taught by Forsberg in order to ensure the quality and accuracy of the data with respect to medical or health conditions.
and in response to success of the regular verification, storing the target manifestation text, the target conclusion text, and the target descriptive text in the medical examination inference knowledge base as medical examination inference knowledge (paragraphs 0025 and 0033). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include in response to success of the regular verification, storing the target manifestation text, the target conclusion text, and the target descriptive text in the medical examination inference knowledge base as medical examination inference knowledge as taught by Forsberg in order to update the database with keywords or terms based on the verification.
Claims 4 and 12:
As per claims 4 and 12, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 3 and 11 as described above and Grbic further teaches further comprising:
in response to success of the regular verification, generating an electronic medical report corresponding to the target medical examination based on the target manifestation text and the target conclusion text, and outputting the electronic medical report to a user corresponding to the target medical examination (paragraph 0027). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include in response to success of the regular verification, generating an electronic medical report corresponding to the target medical examination based on the target manifestation text and the target conclusion text, and outputting the electronic medical report to a user corresponding to the target medical examination as taught by Grbic in order to provide an overview of the patient’s medical treatment or medical examination.
Claims 5 and 13:
As per claims 5 and 13, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 3 and 11 as described above and Forsberg further teaches further comprising:
in response to failure of the regular verification, re-extracting medical examination inference knowledge that matches the target manifestation text from the medical examination inference knowledge base, constructing a chain-of-thought sample based on the extracted medical examination inference knowledge, constructing a prompt text based on the chain-of- thought sample and the target manifestation text, and inputting the prompt text into the large language model (paragraphs 0025 and 0033). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Glottmann to include in response to failure of the regular verification, re-extracting medical examination inference knowledge that matches the target manifestation text from the medical examination inference knowledge base, constructing a chain-of-thought sample based on the extracted medical examination inference knowledge, constructing a prompt text based on the chain-of- thought sample and the target manifestation text, and inputting the prompt text into the large language model as taught by Forsberg in order to update the records to ensure the accuracy of the generated reports.
Claims 6 and 14:
As per claims 6 and 14, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 2 and 10 as described above and Glottmann further teaches manifestation text comprises:
converting the manifestation text into a structured text, wherein the structured text comprises at least one text substructure (paragraph 0053);
determining sub-conclusion texts corresponding to all text substructures comprised in the structured text (paragraph 0063);
and integrating the sub-conclusion texts corresponding to all the text substructures, to generate the conclusion text (paragraph 0064).
Claims 7 and 15:
As per claims 7 and 15, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 6 and 14 as described above and Glottmann further teaches wherein the text substructure comprises a text key- value pair, a key in the text key-value pair is an examined-part identification text, and a value in the text key-value pair is an examined-part manifestation text (paragraph 0064).
Claims 8 and 16:
As per claims 8 and 16, Glottmann, Forsberg, and Grbic teach the method and apparatus of claims 6 and 14 as described above and Glottmann further teaches wherein the integrating the sub-conclusion texts corresponding to all the text substructures, to generate the conclusion text comprises:
concatenating sub-conclusion texts indicating an examined-part abnormality in the sub- conclusion texts corresponding to all the text substructures, to generate the conclusion text (paragraph 0082).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schudlo et al. US Patent 12014807 B2 Automated Report Generation using Artificial Intelligence Algorithms
Schudlo discloses a method, computer system, and a computer program product for automated report generation is provided. The present invention may include receiving a plurality of patient images. The present invention may include retrieving relevant data based on an analysis of the plurality of patient images. The present invention may include providing the relevant data to a user, wherein the relevant data provided to the user is in accordance with a relevant medical guideline. The present invention may include monitoring data input to generate a tailored medical report.
Amarasingham et al. US Publication 20230104655 A1 Creating Multiple Prioritized Clinical Summaries using Artificial Intelligence a system, method, and a computer program product for generating a clinical summary of a patient using artificial intelligence is provided. A patient data that includes unstructured data and structured data is collected from multiple computing devices. Natural language processing models determine clinical issues from the unstructured data. Active clinical issues are determined from the clinical issues. A knowledge graph generated using a relational language model determines treatments associated with the active clinical issues. Active diagnostic and treatment orders are determined from the structured data. Multiple summaries summarizing the active clinical issues, treatments, active diagnostic orders, and active treatment orders are determined using natural language generation models trained to summarize multiple tasks. The multiple summaries are aggregated into a single summary. The language of the summary is modified using a hyperparameter in a smoothing natural language model to direct the summary toward an audience having a particular type.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571)270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3682