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 .
Status of Claims
This action is in response to the application filed 2/12/2025.
Claims 1-10 are currently pending and have been examined.
Claim Rejections - 35 USC § 112(b)
Claims 1 and 6 and therefore their dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “large language model” in claims 1-2 and 6-7 is used by the claim to mean the “eXpertMind engine” (paragraph 19 of the specification) which is a machine learning algorithm owned by Messagemind, Inc. The machine learning engine described by Messagemind, Inc that owns the trademarked eXpertMind (Sood (US 2011/0178962 A1) uses supervised machine learning techniques and not an evolved recurrent neural network. The accepted meaning of a large language model is an “evolved recurrent neural network based on transformer architecture.” The term is indefinite because the specification does not clearly redefine the term. Examiner is interpreting the large language model as a machine learning model with natural language capabilities of that of the “eXpertMind engine” described in paragraph 19 of the specification and not as its commonly accepted meaning.
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 1-10 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.
Claims 1-10 are drawn to a method and a system which are statutory categories of invention (Step 1: YES).
Independent claims 1 and 6 recite: generating medical analysis reports, receiv[ing] a piece of text data [a first medical analysis report, and feedback information]; extract[ing] at least one piece of first sub-data from the piece of text data using a parsing module; analyz[ing] the at least one piece of first sub-data using at least one medical database and at least one rule engine to generate at least one first intermediate result; execut[ing] to generate and output a first medical analysis report according to the at least one first intermediate result; us[ing] the parsing module and feedback information in response to the first medical analysis report to parse the first medical analysis report or the piece of text data into at least one piece of second sub-data; analyz[ing] the at least one piece of second sub-data and the at least one rule engine to generate at least one second intermediate result; and execut[ing] to generate and output a second medical analysis report according to the at least one second intermediate result, [output the first medical analysis report and the second medical analysis report].
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between users and their patients, as reflected in the specification, which states that “When a patient is admitted to the hospital, healthcare providers are required to manually prepare a series of critical documents, including an admission note (AN), an admission order (AO), and a progress note (PN). These documents are essential for the treatment and management of patients, but healthcare providers often face multiple challenges when generating them… First, preparing these documents requires a significant amount of time and effort. Healthcare providers must meticulously record the patient's medical history, physical examination results, diagnosis, and treatment plans, making this a tedious and timeconsuming process. Second, this documentation work often prevents healthcare providers from dedicating more time to direct patient care. Moreover, these documents require frequent revisions. As the patient's condition changes, healthcare providers must continuously update the progress notes to reflect the latest diagnosis and treatment plans…. In light of the above descriptions, the present disclosure proposes a system and method for generating medical analysis reports to address the aforementioned issues.” (see: specification paragraphs 4-6). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “The piece of text data D 1 may be, for example, an admission note drafted by professionals, with content that may include, but is not limited to, chief complaint, present illness, review of systems, physical exam, and clinical laboratory results. The first medical analysis report D4, for example, may be an original analysis (QA). The feedback information D5 includes user ratings and prompts regarding the medical analysis report D4. The ratings are used to adjust the analysis process of the software system 40 to improve the quality of the newly generated medical analysis reports in subsequent rounds. The software system 40 can record the types of user queries, query content, and feedback information D5.” (see: specification paragraph 24). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “system”, “input device”, “at least one medical database”, “large language model”, “computing device”, “natural language processing technique”, “output device” are recited at a high level of generality (e.g., that the analyzing and outputting is performed using generic computer components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Claims 1 and 6 further recite “storage device configured to store a parsing module, at least one medical database, at least one rule engine and a large language model”, which are nominal or tangential addition to the abstract idea and amount to insignificant post-solution activity concerning an insignificant application. The addition of an insignificant extra-solution activity limitation does not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, these claimed additional elements are incidental to the performance of storing healthcare data and generic machine learning algorithms as outlined in the recitations above. See: MPEP 2106.05(g).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer and extra-solution elements. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2, Figure 4 and
Paragraph 17, where “The input device 1 is configured to receive a piece of text data, a first medical analysis report, and feedback information. In an embodiment, the input device 1 may be a hardware component such as a keyboard, mouse, or touchpad. In another embodiment, the input device 1 may be a software component such as an Application Programming Interface (API) or a database. However, the present disclosure is not limited to these examples. The source of the text data can be direct user input or text obtained through voice or image conversion.”
Paragraph 18, where “The storage device 3 is configured to store a parsing module 50, at least one medical database, at least one rule engine, and a large language model (LLM). In an embodiment, the storage device 3 may be, for example, flash memory, a hard disk drive (HDD), a solid-state drive (SSD), dynamic random-access memory (DRAM), static random-access memory (SRAM), or other non-volatile memory. However, the present disclosure is not limited to these examples.”
Paragraphs 19-20, where “The computing device 5 is communicably connected to the input device 1 and the storage device 3. The computing device 5 is configured to run the software system proposed by the present disclosure, the eXpertMind™ Engine. This software system includes the following multiple operations... In an embodiment, the computing device 5 may adopt at least one of the following examples: a personal computer, a network server, a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), a microcontroller (MCU), an application processor (AP), a field-programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), a system-on-a-chip (SOC), a deep learning accelerator, or any other electronic device with similar functionalities. The present disclosure does not limit the hardware type of the computing device 5.:”
Paragraph 21, where “The output device 7 is communicably connected to the computing device 5 to output the first medical analysis report and the second medical analysis report. In an embodiment, the output device 7 may be any hardware or software component that provides graphical and textual outputs. Examples of hardware components include screens, projectors, and speakers, while examples of software components include API interfaces or databases. The present disclosure does not limit the type of output device 7.”
Paragraph 23, where “The medical analysis report D4 generated in the first round is re-input into the software system 40. The user may optionally provide feedback information D5 according to the medical analysis report D4. The parsing module 50 then parses the feedback information D5 and/or the piece of text data D1 into a plurality of new pieces of sub-data. The analysis modules 61, 62, and 63 analyze these new pieces of sub-data, and generates new intermediate results. Finally, the large language model 70 generates a new medical analysis report according to the new intermediate results.”
Paragraph 33, where “In step S2, the computing device 5 extracts at least one piece of first sub-data from the piece of text data using a parsing module 50 according to a natural language processing technique. In step S3, the computing device 5 analyzes at least one piece of first sub-data using at least one medical database and at least one rule engine to generate at least one first intermediate result. In step S4, the computing device 5 executes the large language model 70 to generate and output the first medical analysis report according to the at least one first intermediate result”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-5 and 7-10 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 2-5 and 7-10 recite generating and analyzing healthcare data on the generically recited computing device using the generic machine learning algorithms as shown in the parent claims above.
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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, 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) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2022/0044812 A1) in view of Ferrando (US 2024/0006039 A1).
CLAIM 1-
Barnes teaches:
generating medical analysis reports, performed by a computing device and comprising: receiving a piece of text data; (Barnes teaches using a computer system (i.e., computing device) to generate a summary report (i.e., medical analysis report) and receiving text data (para [0080, 0037, 0069, 0065]))
extracting at least one piece of first sub-data from the piece of text data using a parsing module according to a natural language processing technique; (Barnes teaches that using an AI-assisted clinical extraction tool, data is extracted from the text and uses parsing and natural language processing (para [0065, 0048, 0058]))
analyzing the at least one piece of first sub-data using at least one medical database and at least one rule engine to generate at least one first intermediate result; (Barnes teaches that the analysis can be based on a subset of patient data records in a database and uses an algorithmic rule of confidence levels to output the results of the analysis (para [0069, 0062]))
executing a large language model to generate and output a first medical analysis report according to the at least one first intermediate result; (Barnes teaches a language extraction model that can be a deep learning neural network (i.e., large language model) that generates and outputs a summary report (para [0006, 0069, 0076]))
using the parsing module and feedback information in response to the first medical analysis report to parse the first medical analysis report or the piece of text data into at least one piece of second sub-data; (Barnes teaches that validation of the data extracted (i.e., parsing) is performed from the report and can extract the data elements based on pre-defined presentations of the data elements to perform more analyses on the patient data records (i.e., second sub-data) (para [0032-0036]))
analyzing the at least one piece of second sub-data using the at least one medical database and the at least one rule engine to generate at least one second intermediate result; (Barnes teaches that analyzing the data using the pre-defined presentations based on already processed patient data using the rule-based extraction system can be generated by the manual population of data element fields (i.e., second intermediate result) (para [0034-0036, Figure 3A))
and executing the large language model to generate and according to the at least one second intermediate result (Barnes teaches a language extraction model that can be a deep learning neural network (i.e., large language model) that generates and outputs second results based on the module performing the data analysis (para [0006, 0069, 0076, 0032-0036, 0063, Figure 3A]))
Barnes teaches multiple reports, but does not explicitly teach, however Ferrando teaches:
output a second medical analysis report (Ferrando teaches that multiple reports can be outputted based on retraining the machine learning model on corrected data sets from an initial data set (i.e., first and second sub data) (para [0093, 0064, Figure 7, claim 18]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system of using machine learning to analyze and generate multiple data sets of Barnes with the multiple medical reporting system of Ferrando with the motivation of improving structured reporting of patient data (see: Ferrando, paragraphs 10-13).
CLAIM 2-
Barnes in view of Ferrando teaches the limitations of claim 1. Regarding claim 2, Barnes further teaches:
generating a piece of reference data using the large language model and the at least one medical database based on a retrieval-augmented generation technique, wherein the piece of reference data serves as a part of the at least one first intermediate result (Barnes teaches using a reference range, may be a set of text, that go through a data normalization model (i.e., retrieval-augmented generation technique) that uses the machine learning deep learning natural language processing techniques (i.e., large language model) and based on the database data the reference data is used in the extraction of data to obtain an output (i.e., first result) (para [0057, 0053, 0006, 0062-63, Figure 2]))
CLAIM 3-
Barnes in view of Ferrando teaches the limitations of claim 1. Regarding claim 3, Barnes further teaches:
wherein analyzing the at least one piece of second sub-data using the at least one medical database and the at least one rule engine to generate the at least one second intermediate result comprises: adjusting a weight according to the feedback information to generate the at least one second intermediate result (Barnes teaches that analyzing the data using the pre-defined presentations based on already processed patient data using the rule-based extraction system can be generated by the manual population of data element fields (i.e., second intermediate result) based on normalization weighting that are a range (i.e., adjusted weights) based on the error handling procedure defined in the normalization rules (i.e., outputting a second result based on the error normalization of the initial result) (para [0034-0036, 0057, 0063, Figure 3A, Figure 2))
CLAIM 4-
Barnes in view of Ferrando teaches the limitations of claim 3. Regarding claim 4, Barnes further teaches:
wherein the weight corresponds to an acceptability of the feedback information (Barnes teaches that the weighting of the data is based on the error handling procedure defined in the normalization rules as feedback (i.e., outputting a second result based on the error normalization of the initial result and feeding in back into the system) (para [0034-0036, 0057, 0063, Figure 3A, Figure 2))
CLAIM 5-
Barnes in view of Ferrando teaches the limitations of claim 1. Regarding claim 5, Barnes further teaches:
wherein the at least one second intermediate result is different from the at least one first intermediate result (Barnes teaches that the re-extracted data is retrained based on the data validation and can update the data if the data is mismatched with the reprocessed data (i.e., different results) (para [0063]))
CLAIM 6-
Claim 6 is significantly similar to claim 1 and is rejected upon the same prior art as claim 1. Claim 6 further recites: “an input device configured to receive a piece of text data, a first medical analysis report, and feedback information”, which is taught by Barnes paragraph 81, “a storage device configured to store a parsing module, at least one medical database, at least one rule engine, and a large language model” which is taught by Barnes paragraph 84, “a computing device communicably connected to the input device and the storage device” which is taught by Barnes paragraphs 81-84, and “an output device communicably connected to the computing device to output the first medical analysis report and the second medical analysis report” which is taught by Barnes paragraph 81 and the elements are shown in Figures 1B, 2 and 8.
CLAIMS 7-10-
Claims 7-10 are significantly similar to claims 2-5 and are rejected upon the same prior art as claims 2-5 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gale (US 20190171714 A1) teaches similar machine learning data extractions to analyze medical reporting data. The use of MessageMind’s trademarked AI (Sood (US 2011/0178962 A1) was referenced for its supervised machine learning techniques.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686