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 reply filed 5/27/2026.
Claims 1 and 6 were amended 5/27/2026.
Claims 3-5 and 8-10 were canceled 5/27/2026.
Claims 1-2 and 6-7 are currently pending and have been examined.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 6 and therefore their dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The specification recites that the computing device can adopt at least one of the following examples in paragraph 20 of: a network server, a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), an application processor (AP), a field-programmable gate array (FPGA), an application-specific integrated circuit (SIC), a system-on-a-chip (SOC), a deep learning accelerator or any other electronic device with similar functionalities. The components listed of “a network server, a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), an application processor (AP), a field-programmable gate array (FPGA), an application-specific integrated circuit (SIC), a deep learning accelerator or any other electronic device with similar functionalities” are components built to do specific implementations and as stand-alone components would be unable to be a “computing device” that implements the claim limitations as recited. Having these components as stand-alone components would create issues in implementing the invention without a combination of elements that would work together in a computer. For example, running a GPU or an ASIC would need a host system in order to function. A generic system-on-chip (SoC) would not be able to guide generation by a large language model implemented by a machine learning model of eXpertMind (paragraphs 19-20) as it would need memory bandwidth which SoCs do not generally have.
The specification recites that the computing components can stand alone to implement the claimed invention (“at least one”), however this would not be able to be enabled by the claim limitations as the stand-alone components would be unable to process the data of a large language model as described without other processing components in a host system.
Further, the specification recites that the eXpertMind software system (a generic machine learning model with natural language processing) is executing a large language model and in the claim limitations of “a software system comprising…a large language model”. A generic machine learning model comprising an LLM without specific architecture does not enable the claimed invention.
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.
The term “large language model” in claims 1-2 and 6-7 is comprised by the “eXpertMind engine” software system (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” and would not be able to be comprised by the machine learning model described by eXpertMind. The large language model comprised by a machine learning model with natural language capabilities of that of the “eXpertMind engine” described in paragraph 19 of the specification would not be possible due to the differing architectures of the models and renders the claim indefinite.
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 1-2, 6-7 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 1-2, 6-7 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, comprising a parsing module, 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 the parsing module; analyz[ing] the at least one piece of first sub-data using the 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; wherein analyzing the at least one piece of second sub-data comprises adjusting an analytical weighting parameter used in the rule engine according to an acceptability of the feedback information, so that the at least one piece of second intermediate result is different from at least one first 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 time -consuming 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”, “storage device”, “software 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 storing a software system comprising 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 network server, a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), an application processor (AP), a field-programmable gate array (FPGA), an application-specific 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 and 7 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 and 7 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.
Allowable Subject Matter
Claims 1-2 and 6-7 are allowable over the prior art. The prior art of record of Barnes (US 2022/0044812 A1), Ferrando (US 2024/0006039 A1), Gale (US 20190171714 A1), and Sood (US 2011/0178962 A1) does not teach analyzing second sub-data by adjusting an analytical weighting parameter in a rule engine according to an acceptability of the feedback information. The newly amended claim limitations in the independent claims, in combination with the other claim limitations, overcome the prior art of record. A new prior art search was conducted and found the prior art of Miller (WO 2017/007461 A1) that teaches using parameters and weights on medical data when parsing through medical records, however it did not explicitly teach using acceptability of feedback information when analyzing data.
Response to Arguments
The arguments filed 5/27/2026 have been fully considered.
The arguments pertaining to the 103 rejection are persuasive. The amendments overcome the 103 rejection and it has been withdrawn.
The arguments pertaining to the 112(b) rejection are not persuasive. The newly amended claims include that the software system (eXpertMind in paragraph 19 of the specification) comprises a large language model (LLM) (paragraph 20). However, it would not be possible for the machine learning model of eXpertMind to comprise a large language model that can implement the claim limitations as they have different architectures. Further, a new 112(a) rejection has been made as without a host system, the computer components as stand-alone components such as listed would not enable the invention. The hardware inconsistencies persist and the 112 rejection remains.
The arguments pertaining to the 101 rejection are not persuasive. Applicant argues that the claimed invention is similar to Enfish. Examiner respectfully disagrees, as the current claimed invention is using a generic machine learning model (software system of eXpertMind in paragraph 19) to analyze the data. The implementation of the LLM appears to be disjointed from the generic machine learning model and it is unclear how the two models are configured to create a closed-loop feedback data pipeline. Applicant argues that the specific data flow features are recited in the claims, however the claim limitations do not recite executing deterministic medical logic, restricting the generation space of the LLM, or rule architecture in the system itself. Further, the current claim limitations do not recite dynamic real-time feedback loops, but rather analyzing sub-data by adjusting parameters using rules based on feedback information. Further, the claim limitations do not recite that the parsing module re-parses the data but rather: “according to an acceptability of the feedback information, at least one piece of second intermediate result is different from the at least one first intermediate result”.
The claimed invention does not recite structurally anchoring the probabilistic model toa deterministic expert rule engine nor does it recite a real-time feedback control loop to enable dynamic modification. The claimed invention is dissimilar to McRo as the functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Miller (WO 2017/007461 A1) teaches using parameters and weights on medical data when parsing through medical records, however it did not explicitly teach using acceptability of feedback information when analyzing data.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KIMBERLY A. SASS/ Examiner, Art Unit 3686
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