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 .
Status of Claims
This action is in reply to the application filed on 12/30/2024.
Claims 1-20 are currently pending and have been examined.
Abstract
The abstract of the disclosure is objected to because of the following informalities: The abstract appears to be identical to the claim language.
Applicant is reminded of the proper language and format for an abstract of the disclosure: The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20: Step 1
Claims 1-18 are drawn to a medical data processing apparatus, which is within the four statutory categories (i.e. machine). Claim 19 is drawn to a method for matching medical data text, which is within the four statutory categories (i.e. process). Claim 20 is drawn to non-transitory computer program product storing computer-readable instructions, which is within the four statutory categories (i.e. machine).
Claims 1-20: Step 2A Prong One
Claim 1 recites receive a first medical text and extract at least one item from the first medical text, receive a second medical text and determine whether or not there is a match between the extracted at least one item and content of the second medical text including if there is a match determining a part of the second text that matches the extracted at least one item. Claims 19 and 20 recites similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user receiving a first medical text and extracting at least one item from the first medical text, receiving a second medical text and determining whether or not there is a match between the extracted at least one item and content of the second medical text including if there is a match determining a part of the second text that matches the extracted at least one item. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claims 19 and 20 recite similar limitations.
Claims 2-18 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claims 2-4 further define the trained model. Claims 5-6 and 10 further defines the user interface. Claim 7 further defines action related to value threshold. Claim 7 further defines the associating on the user interface of the representation of the part of the second text and the matching extracted. Claim 8 further defines the representation. Claim 9 further defines the representation. Claim 11 further defines the indication. Claims 12-13 further define determining whether or not there is a match between the extracted at least one item and content of the second medical text. Claim 14 further defines extracting of at least one item from the first medical text. Claim 15 further defines one or both of the first medical text and the second medical text. Claims 16-17 further define one of the first medical text and the second medical text. Claim 18 further defines the apparatus according to claim 1. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-20: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements that the medical data processing apparatus comprises processing circuitry. Claim 19 recites a method and the steps for performing the method, but does not recite any additional elements to perform the abstract idea. Claim 20 recites the additional elements of non-transitory computer program product storing computer-readable instructions that are executable to perform the method steps.
Claims 1-20, directly or indirectly, recite the following generic computer components: “medical data processing apparatus comprising processing circuitry configured to perform steps” and “non-transitory computer program product storing computer-readable instructions that are executable to perform steps” which are similar to adding the words “apply it” to the abstract idea. The written description does not appear to further describe the computer hardware or the computer program, but rather repeats the recitation from the claim limitations themselves. The written description discloses that the recited computer components encompass generic components including “The data processing apparatus 20 comprises a computing apparatus 22, which in this case is a personal computer (PC) or workstation. The computing apparatus 22 is connected to a display screen 26 or other display device, and an input device or devices 28, such as a computer keyboard and mouse“ (see at least Paragraph [0028]) and “Computing apparatus 22 comprises processing circuitry 32. The processing circuitry 32 comprises application program interface (API) and communication circuitry 34, data processing circuitry 36 configured to perform processes including providing data to and receiving data from the API circuitry as part of such processes, and interface circuitry 38 configured to obtain user or other inputs and/or to output results of the data processing via a user interface” (see at least Paragraph [0030]) . Although the additional element “trained model/large language model/language model” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-20: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, machine learning) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 15, 18, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bell et al., WIPO Application Publication WO 2024/249668 A1.
Claim 1:
Bell discloses the following limitations as shown below:
receive a first medical text and extract at least one item from the first medical text (see at least Paragraph 9, allow a user to query medical information (and other types of information) using natural language, intuitive interfaces, and follow-up question; Paragraph 316, In some embodiments of any of Al-AM, the method further includes: (i) obtaining, via a retriever component, one or more documents; (ii) extracting text from the one or more documents; (iii) obtaining a set of text snippets from the text; Paragraph 322, the set of data sources include a medical database (e.g., the database(s) 400). For example, the medical database stores a set of electronic health records);
receive a second medical text and determine whether or not there is a match between the extracted at least one item and content of the second medical text including if there is a match determining a part of the second text that matches the extracted at least one item (see at least Paragraph 16, in response to transmitting the one or more structured queries, receiving, from the one or more databases, a set of subjects meeting the set of criteria; Paragraph 60, the Al-enabled clinical assistant may be invoked in conversation to provide insights and/or data for a particular topic or conversation. The platform may also include an electronic health record (EHR) interface component (e.g., comprising one or more agents) configured to allow physicians, and optionally other users, to view, edit, and/search an EHR. The EHR interface component may be communicatively coupled with one or more services and/or databases to obtain updated information and reports (e.g., via push notifications); Paragraph 139, in some embodiments, an agent module (e.g., the backend component) is configured to perform intent matching and/or parameter extraction on the user queries and requests. In some embodiments, the intent is assumed (e.g., the agent module is configured for a specific task). In some embodiments, the agent module extracts domain-specific parameters. For an example query “show patients with MSI high, TMB less than 20, which have been diagnosed with central neurocytoma in the past four months”).
Claims 19 and 20 recite substantially similar method and non-transitory computer program product to those of apparatus claim 1 and, as such, are rejected for similar reasons as given above.
Claim 15:
Bell discloses the limitations as shown in the rejections above. Bell further discloses the following limitations:
wherein one or both of the first medical text and the second medical text is unstructured, structured or a mixture of structured and unstructured (see at least Paragraph 58, As another example, the platform may include a data curation component (e.g., comprising one or more agents) that obtains raw (e.g., unstructured) data and structures it into a common and useful format as a repository (e.g., a multimodal database) of clinical data from which other agents, models, and/or components may operate. As another example, the platform may be configured to search within the clinical data to identify cohorts of related patients and/or generate insights and/or analytics; Paragraph 73, a sixth agent module 6102-6 is configured for a sixth specific-task of evaluating unstructured data associated with a patient to identify a cohort of similar patients; Paragraph 105, For example, an agent module 6102 may obtain data from different databases (e.g., external databases 108, knowledge database 404, etc.), in which the data is obtained in a variety of different formats and/or structures, such as unstructured text, structured text, tables, charts, graphical data, and/or the like. In some embodiments, the agent module 6102 reformats and/or restructures the data obtained from the databases for application to the model 228 and/or a different agent module 6102).
Claim 18:
Bell discloses the limitations as shown in the rejections above. Bell further discloses the following limitations:
a data store that stores at least one of the a first medical text or the second medical text (see at least Paragraph 16, in response to transmitting the one or more structured queries, receiving, from the one or more databases (reads on “data store”), a set of subjects meeting the set of criteria; Paragraph 60, the Al-enabled clinical assistant may be invoked in conversation to provide insights and/or data for a particular topic or conversation. The platform may also include an electronic health record (EHR) interface component (e.g., comprising one or more agents) configured to allow physicians, and optionally other users, to view, edit, and/search an HER (reads on “medical text”). The EHR interface component may be communicatively coupled with one or more services and/or databases to obtain updated information and reports (e.g., via push notifications));
a display device configured to provide a user interface that outputs to a user an indication of the outcome of the determining whether or not there is a match (see at least Paragraph 16, in response to transmitting the one or more structured queries, receiving, from the one or more databases, a set of subjects meeting the set of criteria; Paragraph 60, the Al-enabled clinical assistant may be invoked in conversation to provide insights and/or data for a particular topic or conversation. The platform may also include an electronic health record (EHR) interface component (e.g., comprising one or more agents) configured to allow physicians, and optionally other users, to view, edit, and/search an EHR. The EHR interface component may be communicatively coupled with one or more services and/or databases to obtain updated information and reports (e.g., via push notifications); Paragraph 69, The user interface 204 includes output device(s) 206 and input device(s) 212. In some embodiments, the input device(s) 212 include a keyboard, mouse, a track pad, and/or a touchscreen. In some embodiments, the user interface 204 includes a display device that includes a touch-sensitive surface, in which case the display device is a touch-sensitive display. In client devices that have a touch-sensitive display, a physical keyboard is optional (e.g., a soft keyboard may be displayed when keyboard entry is needed)); and
communication circuitry operable to communicate with at least one of the data store and an external trained model that is operable based on instructions or other communication from the processing circuitry to perform at least one of the extracting of at least one item from the first medical text or the determining of whether or not there is a match between the extracted at least one item and content of the second medical text, and to receive from the trained model results of the at least one of extracting or determining (see at least Paragraph 17, The computing system includes control circuitry and memory storing one or more sets of instructions. The one or more sets of instructions include instructions for performing any of the methods described herein; Paragraph 54, The platform may include a plurality of individual task-specific orchestrations that may operation independently or in combination to return accurate and relevant information (e.g., identifying target cohorts, clinical trial information, and/or members of target populations). In some embodiments, each task-specific orchestration (or agent) may include one or more machine-learning models, such as a language model trained and/or fine-tuned on a particular domain; Paragraph 139, in some embodiments, an agent module (e.g., the backend component) is configured to perform intent matching and/or parameter extraction on the user queries and requests. In some embodiments, the intent is assumed (e.g., the agent module is configured for a specific task). In some embodiments, the agent module extracts domain-specific parameters. For an example query “show patients with MSI high, TMB less than 20, which have been diagnosed with central neurocytoma in the past four months”; Paragraph 155, In accordance with some embodiments, the system 976 (e.g., a third agent module) is configured to classify the documents 972 and identify a subset of the documents that have a classification that matches (or is similar to) a classification extracted from the input queries).
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 axe 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 2-4 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bell et al., WIPO Application Publication WO 2024/249668 A1 in view of Achara et al., U.S. Patent Application Publication US 2025/0166747 A1.
Claim 2:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Achara as shown does:
wherein the processing circuitry is configured to use a trained model to perform at least one of: the extracting of at least one item from the first medical text; the determining of whether or not there is a match between the extracted at least one item and content of the second medical text (see at least Paragraph 25, The automated clinical trial matching system may rely on various models to determine matches, including one or more natural language processing (NLP) models for extracting structured data from text, as well as other AI models, such as machine learning (ML) or deep learning (DL) models; Paragraph 40, The patient data may also include structured data (e.g., labeled data or data stored in fields), and unstructured data (e.g., raw text including patient data, such as lab, test, or other reports, comments made by a care provider, etc.) The structured data may be extracted programmatically. A first NLP module 204 (e.g., patient data NLP module 108) may be used to convert the unstructured data into structured data, using a first set of one or more LLMs (e.g., clinical LLMs 120)) .
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the processing of Achara with the motivation of providing the benefit “… to achieve better and more relevant trial matching and acceptance of trials by patients” (Achara, see at least Paragraph 23).
Claim 3:
The combination of Bell/Achara discloses the limitations as shown in the rejections above. Bell further discloses the following limitations:
wherein the trained model comprises a large language model (LLM) or other language model (see at least Paragraph 65, the model(s) 228 include one or more large language models, such as GPT-3, GPT-4, BioGPT, and PaLM-2).
Claim 4:
The combination of Bell/Achara discloses the limitations as shown in the rejections above. Bell further discloses the following limitations:
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 at least Paragraph 65, the model(s) 228 include one or more large language models, such as GPT-3, GPT-4, BioGPT, and PaLM-2).
Claim 16:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations, but Achara as shown does:
wherein one of the first medical text and the second medical text comprises clinical trial eligibility criteria or medical guidelines and the other of the first medical text and the second medical text comprises a patient record, wherein the patient record may comprise a plurality of medical documents (see at least Paragraph 19, The clinical trial matching system may generate a list of clinical trials for which a specific patient of a health care system may be eligible, or alternatively, a list of patients eligible to participate in a specific clinical trial, based on patient data stored in an electronic medical record (EMR) of the patient(s) and eligibility criteria of the clinical trial(s); Paragraph 21, The inclusion criteria may specify that the patient be at a certain point in a patient healthcare journey, where whether the patient is at the certain point may have to be deduced based on events, test results, reports, etc. in the patient record. The reports and other patient data may include unstructured data (e.g., text), from which information may have to be extracted.).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Achara for at least the same reasons given for claim 2.
Claim 17:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations, but Achara as shown does:
wherein one of the first medical text and the second medical text comprises a source medical paper and the other of the first medical text and the second medical text comprises a patient record, wherein the patient record may comprise a plurality of medical documents (see at least Paragraph 19, The clinical trial matching system may generate a list of clinical trials for which a specific patient of a health care system may be eligible, or alternatively, a list of patients eligible to participate in a specific clinical trial, based on patient data stored in an electronic medical record (EMR) of the patient(s) and eligibility criteria of the clinical trial(s); Paragraph 21, The inclusion criteria may specify that the patient be at a certain point in a patient healthcare journey, where whether the patient is at the certain point may have to be deduced based on events, test results, reports, etc. in the patient record. The reports and other patient data may include unstructured data (e.g., text), from which information may have to be extracted).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Achara for at least the same reasons given for claim 2.
Claims 5-8 and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Bell et al., WIPO Application Publication WO 2024/249668 A1 in view of Nallamothu et al., U.S. Patent Application Publication US 2025/0390677 A1.
Claim 5:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the system further comprises a user interface configured to display at least part of the first medical text including displaying and/or highlighting the extracted at least one item (see at least Paragraph 53, In one example, the user interface module 123 may display the extracted text alongside the corresponding matched entries from the reference dataset, highlighting areas of high similarity with visual cues, such as color coding or underlining. The similarity scores for each match may also be shown to convey the strength of the match).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu with the motivation
Claim 6:
The combination of Bell/Nallamothu discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the user interface is configured also to display a representation of the part of the second medical text that matches the extracted at least one item, and to associate on the user interface the representation of the part of the second text and the matching extracted at least one item (see at least Paragraph 39, In one example, the presentation 125 may provide a side-by-side comparison of the original documents alongside the extracted text and matched datasets for direct comparison).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 7:
The combination of Bell/Nallamothu discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the associating on the user interface of the representation of the part of the second text and the matching extracted at least one item comprises overlaying, linking or displaying in proximity the part of the second text and the matching extracted at least one item (see at least Paragraph 28, Upon determining a match, the analysis platform 107 may update the claim records in the reference database with the matched association (e.g., user A″, “MRI scan”, and “Jan. 10, 2024”) including relevant identifiers and similarity scores. Each identified name within the document may be linked (e.g., annotations, metadata tags, embedded references, etc.) to the corresponding claim participant. These associations establish a direct connection between the extracted text and the claim participant they represent, facilitating easy retrieval and reference during subsequent processing stages).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 8:
The combination of Bell/Nallamothu discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the representation of the part of the second medical text comprises a quote from the second medical text (see at least Paragraph 39, In one example, the presentation 125 may provide a side-by-side comparison of the original documents alongside the extracted text and matched datasets for direct comparison).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 10:
The combination of Bell/Nallamothu discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the user interface is configured to output an indication whether there is a match or not between the extracted at least one item and content of the second medical text (see at least Paragraph 27, The Levenshtein distance algorithm may employ dynamic programming to efficiently compute the edit distance between two strings. Once the edit distance is calculated, a similarity score may be derived from the edit distance, and a threshold value may be applied to determine whether the similarity score indicates a match; Paragraph 32).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 11:
The combination of Bell/Nallamothu discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the indication comprises at least one of highlighting text or display of different color(s), hatching, shading or indicator(s) depending on whether or not there is a match (see at least Paragraph 39, It is understood that the user interface module 123 may generate any type of presentation in the user device 101. In one example, the presentation 125 may include a comprehensive view of the document(s) with highlighted extracted texts and the corresponding matches, with notes or annotations indicating the matched participants and related information. In one example, the presentation 125 may list all the extracted entities in a tabular format, along with their corresponding matches and similarity scores).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 12:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein determining whether or not there is a match between the extracted at least one item and content of the second medical text comprises determining whether cognitive or semantic content of the extracted at least one item is the same as or consistent with at least part of the content of the second medical text (see at least Paragraph 33, In one example, the fuzzy matching engine 117 may utilize NLP algorithms for computing semantic similarity scores between strings, and may assign higher scores to pairs of names that are not only similar in spelling but also semantically related (e.g., have similar meanings or connotations). In one example, NLP algorithms may analyze the context in which names appear within the text. The fuzzy matching engine 117 may take into account contextual information when computing similarity scores, and names that occur in similar contexts may receive higher similarity scores. … Overall, incorporating NLP algorithms into the scoring mechanism of fuzzy matching may lead to accurate and contextually aware similarity scores).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 13:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein determining whether or not there is a match between the extracted at least one item and content of the second medical text comprises at least one of: a) determining at least one criterion from the extracted at least one item and determining whether content of the second medical text complies with the at least one criterion; or b) determining whether or not there is a match between the extracted at least one item and content of the second medical text comprises determining a question represented by or comprised in the at least one item and determining whether the response to the question is positive or negative based on the second medical text (see at least Paragraph 34, Following the extraction of text from a plurality of documents (by the document processing module 115) and the association of the extracted text with relevant entities (by the fuzzy matching engine 117), the storage module 119 may store this structured data in a systematic and accessible manner in the database 111. In one instance, the storage module 119 may organize and manage the extracted text and associated metadata in a structured format for facilitating efficient retrieval of the document data (e.g., for downstream machine-learning processes). In one instance, the storage module 119 may interface with databases, file systems, or cloud storage solutions for seamless integration with other components of the document processing workflows; Paragraph 39, In one example, the user interface module 123 may generate a presentation 125 in the user device 101 that may summarize the key findings, such as extracted names and their similarity scores. It is understood that the user interface module 123 may generate any type of presentation in the user device 101. In one example, the presentation 125 may include a comprehensive view of the document(s) with highlighted extracted texts and the corresponding matches, with notes or annotations indicating the matched participants and related information. In one example, the presentation 125 may list all the extracted entities in a tabular format, along with their corresponding matches and similarity scores).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 14:
Bell discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Nallamothu as shown does:
wherein the extracting of at least one item from the first medical text comprises at least one of: a) selecting at least part of the first medical text; b) summarising content of the first medical text and generating said at least one item to represent the summarised content (see at least Paragraph 39, In one example, the user interface module 123 may generate a presentation 125 in the user device 101 that may summarize the key findings, such as extracted names and their similarity scores. It is understood that the user interface module 123 may generate any type of presentation in the user device 101. In one example, the presentation 125 may include a comprehensive view of the document(s) with highlighted extracted texts and the corresponding matches, with notes or annotations indicating the matched participants and related information. In one example, the presentation 125 may list all the extracted entities in a tabular format, along with their corresponding matches and similarity scores).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell with the features of Nallamothu for at least the same reasons given for claim 5.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Bell et al., WIPO Application Publication WO 2024/249668 A1 in view of Nallamothu et al., U.S. Patent Application Publication US 2025/0390677 A1 and further in view of Madhuripan et al., U.S. Patent Application Publication US 2025/0131998 A1.
Claim 9:
The combination of Bell/Nallamothu discloses the limitations as shown in the rejections above. Bell may not specifically disclose the following limitations but Madhuripan as shown does:
wherein the representation of the part of the second medical text is displayed using a tooltip, popover or mouse-over functionality (see at least Paragraph 58, Additionally, a help tooltip or information icon may be present on each node card, offering brief explanations of the node's functionality and capabilities. Hovering over or clicking this icon displays the tooltip, which may also include a “Learn More” link directing users to a more detailed help article or tutorial about the node and its functionalities. The node-based workflow UI aims to facilitate efficient, customizable, and user-friendly medical documentation, utilizing both deterministic logic and large language model capabilities).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent systems and methods of Bell and the features of Nallamothu with the functionality of Madhuripan with the motivation to “… improve the incongruence inherent to medical informatics, including … improving integration of independent medical systems, improving accuracy of data shared across medical systems, improving accuracy of data shared with providers, improving the efficiency sharing medical information, enhancing patient care in association with more efficient information systems” (Madhuripan, see at least Paragraph 4).
Conclusion
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/Joy Chng/
Primary Examiner, Art Unit 3686
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