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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN2024105930674, filed on May 14, 2024.
Response to Amendment
In the preliminary amendment filed on July 11, 2026, the following has occurred: claim(s) 1 and 9-10 have been amended. Now, claim(s) 1, 7-10 are pending.
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.
Claim(s) 1, 7-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1
Claims 1, 7-8 are drawn to a method, claim 9 is drawn to a system, claim 10 is drawn to a computer-readable medium, and of which are within the four statutory categories (i.e., a machine and a process). Claims 1-7-10 are further directed to an abstract idea on the grounds set out in detail below.
Claim 1: Step 2A Prong One
Claim 1 recite(s):
S3, performing semantic extraction on the patient medical record information to obtain patient information to be recorded, wherein the patient information to be recorded comprises patient identity information;
S4, classifying the patient information to be recorded to generate a category of the patient information to be recorded;
S5, generating an input window related to the patient identity information based on an extensible stylesheet language (XSL) file, wherein the input window comprises input sub-windows bound to the patient information to be recorded, the XSL file comprises an extensible stylesheet language transformation (XSLT) tool and an extensible markup language (XML) path language (XPath) tool, and the input window comprises a plurality of operation buttons related to the patient information to be recorded;
wherein the S3 specifically comprises:
S301, obtaining a historical patient medical record information dataset wherein the historical patient medical record information dataset comprises a plurality of historical patient medical record information each with label information, and the label information comprises patient identity information, items to be recorded and time of the respective items to be recorded in each of the plurality of historical patient medical record information;
S304, extracting specific named entities, namely the label information, in the patient medical record information, wherein the specific named entities comprise the patient identity information, the items to be recorded and the time of the respective items to be recorded;
wherein the S304 specifically comprises:
introducing a confidence parameter, and extracting the specific named entities in the patient medical record information through the first module in the trained entity recognition model as per formulas expressed as follows:
PNG
media_image1.png
118
209
media_image1.png
Greyscale
where, Y* represents label information to be selected, P(Y|IX; θ
)
a
represents a conditional probability of label information Y under given patient medical record information X,
a
r
g
m
a
x
Y
represents taking the label information Y maximizing the conditional probability as the label information to be selected, θ represents a model parameter, a represents an adjustable parameter, β represents the confidence parameter, namely a trust degree of the first module to the label information, e represents a base of natural logarithm, N represents a number of types of the label information,
p
i
represents a prediction probability of the first module for an
i
t
h
of label information; and
wherein the S305 specifically comprises:
constructing, based on the conditional random field algorithm, a loss function expressed as follows:
L
θ
=
-
log
P
Y
X
;
θ
+
λ
θ
2
;
where, L(θ) represents the loss function under the model parameter θ, log represents a natural logarithm function, λ represents a regularization parameter, and
θ
2
represents taking a square norm of the model parameter θ; and
correcting the specific named entities through the loss function, and outputting the corrected specific named entities corresponding to respective minimum values of the loss function;
wherein the patient information to be recorded is classified, and the S4 specifically comprises:
S401, vectorizing the patient information to be recorded to obtain a plurality of feature vectors of the patient information to be recorded;
S402, determining a quantity of the plurality of feature vectors of the patient information to be recorded and clustering centers, wherein the clustering centers are randomly selected from the plurality of feature vectors of the patient information to be recorded;
S403, allocating the plurality of feature vectors of the patient information to be recorded to different ones of the clustering centers according to a distance nearest principle as per a formula expressed as follows:
c
i
=
a
r
g
m
i
n
j
x
i
-
μ
j
2
where,
c
i
represents a clustering center with a nearest distance to a feature vector
x
i
of
i
t
h
patient information to be recorded, and
μ
j
represents a
j
t
h
clustering center;
S404, calculating an average value of feature vectors of each category of the patient information to be recorded, and redetermining clustering centers in a result of the allocating as per a formula expressed as follows:
μ
j
'
=
1
S
j
∑
x
i
ϵ
S
j
x
i
;
where,
μ
j
represents a redetermined
j
t
h
clustering center,
S
j
represents a set of feature vectors of the patient information to be recorded in the
j
t
h
clustering center; and
S405, increasing a count of iteration by 1, and repeating the S403 and the S404 until the clustering centers remain unchanged or a maximum count of iteration is reached; and
wherein the S5 specifically comprises:
S501, converting the patient information to be recorded to an XML through the XSLT tool;
S502, constructing original components by using a page corresponding to the XML formed after the converting as a template;
S503, modifying label characters, in the page corresponding to the XML formed after the converting, to custom labels, and constructing custom components wherein each of the custom components corresponds to one of the input sub-windows bound to the patient information to be recorded;
S504, connecting the custom components to the original components;
S505, adding attributes to the patient information to be recorded through a form control of the XSL file, wherein the attributes comprise name and category of the patient information to be recorded;
S506, performing regularization description on the attributes to obtain a string set related to the attributes:
S507, obtaining a field corresponding to a specific named entity in the patient information to be recorded through the XPath tool;
S508, matching the field corresponding to the specific named entity with the string set, maintaining attributes corresponding to the specific named entity when the matching is successful, otherwise, constructing attributes corresponding to the specific named entity that fails to match in the form control;
S509, transferring maintained attributes and constructed attributes to the custom components; and
S510, submitting the custom components to obtain the input sub-windows bound to the patient information to be recorded
These limitations, as drafted, given the broadest reasonable interpretation but for the
recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, performing semantic extraction, classifying the patient information, generating an input window related to the patient identity information, obtaining a historical patient medical record information dataset, extracting specific named entities, inputting the specific named entities, vectorizing the patient information, determine a quantity of the plurality of feature vectors, converting the patient information to be recorded, constructing original components, modifying label characters, connecting the custom components to the original components, adding attributes to the patient information, performing regularization description, obtaining a field correspond to a specific named entity, matching the field corresponding to the specific named entity with the string set, transferring maintained attributes and constructed attributes to the custom components, and submitting the custom components could be carried out by a person following rules or instructions. Additionally, the recitation of and use of the listed equations and formulas encompass Mathematical Concepts.
Claim 1: 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 idea, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claim 1, directly or indirectly, recites the following generic computer components configured to implement the abstract idea “an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model comprises an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm and an output module sequentially connected in that order”. As set forth in the MPEP 2106.04(d) “merely including 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.
Additionally, the claim recites “S1, obtaining patient medical record information;”, “S6, receiving the patient identity information;”, “S7, displaying a plurality of input sub-windows associated with the patient identity information;”, “S8, receiving user input through the plurality of input sub-windows;”, “S9, displaying the user input according to the category of the patient information to be recorded;”, “S303, receiving the patient medical record information through the input module in the trained entity recognition model;”, “S306, outputting the corrected specific named entities as the patient information to be recorded through the output module in the trained entity recognition model;”, “outputting the label information to be selected as the label information, namely as the specific named entities;”, “wherein the patient information recording method further comprises: transmitting the user input into the HIS to update the patient medical record information thereby achieve real-time synchronization and data sharing of the patient medical record information” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “S2, constructing an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model comprises an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm and an output module sequentially connected in that order;”, “through the entity recognition model”, “S302, training the entity recognition model by using the historical patient medical record information dataset to obtain a trained entity recognition model;”, “through the first module in the trained entity recognition model”, “S305, inputting the specific named entities into the second module in the trained entity recognition model, and correcting the specific named entities to obtain corrected specific named entities;”, “into the trained entity recognition model”, “through a K-means clustering algorithm” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 1: Step 2B
The claim(s) does/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, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an 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.”)
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment
does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Dependent claims 7-8 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claim 7 further describes generic computer components. Finally, claim 8 further describes the step of S9. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims recite additional subject matter which amount to limitations consisted with the additional elements in independent claim 1 (such as claim 7 recites additional
limitations that amount to generic computer components).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims are not patent eligible.
Claims 9-10 recite similar functions to claims 1, 7-8, but in system and computer-readable medium form. The addition of “a processor; and a non-transitory memory stored with computer-readable instructions, wherein the computer-readable instructions are configured, when being executed by the processor,” in claim 9, and “wherein the computer program is configured, when being executed by a processor,” in claim 10 are recited at a high-level of generality that amounts to no more than generic computer components. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an 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.”) The claims are not patent eligible.
Distinguishing Subject Matter
The following is a statement of reasons for the indication of distinguishing subject matter: Claims 1, 7-10 distinguish over the prior art. The closest prior art (Leventhal, Drake), discloses a method for building a health predictive model is provided based on a plurality of electronic medical records representing a plurality of electronic medical cases each associated with a diagnosis of a medical condition and combined with a method that includes, for each entity, obtaining data specific to the entity, generating a feature vector based on the data, and processing the feature vector to generate an entity fall likelihood that indicates a likelihood that the entity will experience a fall based on the feature vector. However, the prior art does not describe applying the combination of the features S4-S9, S301-S306, S401-S405, S501-S508, as recited in claims 1, 7-10.
Response to Arguments
In the Remarks filed on July 11, 2026, the Applicant argues that the newly amended and/or added claims overcome the Claim Objection(s) and 35 U.S.C. 101 rejection(s). The Examiner acknowledges that the newly added and/or amended claims overcome the Claim Objection(s) and 35 U.S.C. 101 rejection(s) in reference to the non-statutory subject matter. However, the Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) in reference to the abstract idea without significantly more.
The Applicant argues that:
(1) closed loop ensures that the instant application is not merely linking the judicial exception to a particular technological environment, but rather integrates the results of the information extraction, classification, and GUI generation into a specific and tangible method of updating patient electronic health records in real time, thereby ensuring the integrity of the recorded patient information as expressly recited in the original specification. Thus, the currently amended claim 1 constitutes a complete technical solution that integrates the judicial exception into a practical application;
(2) Applicant submits that the currently amended claim 1 provides improvements
to computer functionality and the technical field of medical information processing. In support of this position, Applicant respectfully directs the Examiner's attention to Ex parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), which was designated as precedential on November 4, 2025. To address the above technical problems, the currently amended claim 1 improves the way computers process unstructured medical text. The NER+CRF model comprehensively considers contextual information among named entities, improving the accuracy of entity recognition. The CRF algorithm uses dependencies among entities in the sequence to label them in a global optimization manner, thereby avoiding the cumulative effect of local annotation errors. Traditional sequence labeling models (e.g., using NER alone) label each token independently, ignoring dependencies among entities in the sequence, which leads to a cumulative effect of local annotation errors - an annotation error in one entity propagates to subsequent entities. The claimed CRF-based global optimization, by contrast, performs joint probabilistic modeling of the entire label sequence given the entire observation sequence, thereby selecting the globally optimal label sequence. This is precisely an improvement to the model architecture itself;
(3) the introduction of the confidence parameter helps the entity recognition model to evaluate and select the label information more accurately, improving the reliability and accuracy of the entity recognition. This method effectively optimizes the model parameters, reduces deviation of the prediction results, and makes the entity recognition process more accurate and reliable. The claimed NER+CRF model provides analogous technological improvements: reduced cumulative errors in sequence labeling, improved accuracy of entity recognition, and more reliable information extraction - all improvements to how the model itself operates. These improvements enable the entity recognition model to better adapt to the needs of medical information processing and provide reliable technical support for the recording and management of patient information. In other words, the claim improves how the computer itself operates when processing unstructured medical text, rather than merely requiring a computer to perform generic data processing;
(4) the currently amended claim 1 also improves the way computers generate graphical user interfaces (GUIs). The input sub-windows are bound to the patient information to be recorded to ensure that the entered data corresponds correctly to the patient identity information, thereby avoiding information confusion or entry errors, and this binding relationship improves the accuracy and reliability of data entry. The improvement is directly reflected in the computer's display effect and user interaction efficiency, the input window generated by the XSL file can be easily customized according to needs to make the interface simple and intuitive, enabling medical staff to record patient information more efficiently. Such an input interface allows medical staff to focus more on recording patient information and reduces unnecessary visual interference, enabling medical staff to record patient information intuitively and conveniently, thereby improving work efficiency and recording quality. In other words, the currently amended claim 1 improves how the computer generates and renders user interfaces for data entry;
(5) the currently amended claim 1 improves the efficiency of computer information retrieval and organization. Through these improvements, similar patient information is classified into the same larger category, which is convenient for medical staff to access and avoids confusion in information management;
(6) The Examiner characterizes the claimed limitations as encompassing ''managing personal behavior or relationships or interactions between people'' and states that they ''could be carried out by a person following rules or instructions''. Applicant respectfully disagrees. As the August 2025 USPTO Memorandum clarified, the ''mental process'' category does not extend to claim limitations that cannot be practically performed in the human mind. The claimed operations - CRF global optimization requiring joint probability modeling over all possible label sequences, confidence parameter requiring real-time statistical computation across N types of label information, and loss function minimization requiring gradient-based parameter iteration - cannot be performed in the human mind. The claimed invention is therefore not directed to a ''mental process'' or ''method of organizing human activity''. It is directed to specific technical improvements to a computer-implemented machine learning model;
(7) As the ARP in Desjardins emphasized, eligibility determinations should turn on whether ''the claims are directed to an improvement to computer functionality versus being directed to an abstract idea''. The Federal Circuit in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), recognized that ''software can make nonabstract improvements to computer technology, just as hardware improvements can''. Here, the claimed NER+CRF model improves how a computer processes sequential data for named entity recognition - a specific improvement to computer functionality - rather than merely applying a known algorithm on a generic computer. The Examiner's conclusion that the claimed entity recognition model, confidence parameter, loss function, K-means clustering, and XSLT dynamic UI are merely ''generic computer components'' is inconsistent with Desjardins and the updated MPEP. Under the August 2025 USPTO Memorandum, examiners should reject claims under§ 101 only when it is more likely than not (i.e., >50% probability) that the claims fail eligibility. Thus, the currently amended claim 1 provides a complete technical solution that integrates model-based information extraction, intelligent classification, dynamic GUI generation, and real-time data synchronization, thereby improving both computer functionality and the technical field of medical information processing. Accordingly, the currently amended claim 1 integrates the judicial exception into a practical application, such that the currently amended claim 1 is not directed to the judicial exception (Step 2A: NO), and the currently amended claim 1 is eligible. Based on the above reasons, Applicant submits that the currently amended claim 1 of the instant application is subject to the conditions and requirements under 35 USC §101, that is to say, the currently amended claim 1 is patent eligible;
(8) in response to the rejections of dependent claims 7-10, Applicant has currently amended claim 9 to replace ''a memory'' with ''a non-transitory memory'', and amended claim 10 to replace ''a computer-readable medium'' with ''a non-transitory computer-readable medium'', so as to overcome the rejections applied thereto, and thus claims 9-10 are patent eligible under 35 U.S.C. 101. Moreover, Applicant submits that current claims 7-8 depend upon the currently amended independent claim 1 directly, and thus also are patent eligible under 35 U.S.C. 101.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the recitation of “S2, constructing an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model comprises an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm and an output module sequentially connected in that order;”, “through the entity recognition model”, “S302, training the entity recognition model by using the historical patient medical record information dataset to obtain a trained entity recognition model;”, “through the first module in the trained entity recognition model”, “S305, inputting the specific named entities into the second module in the trained entity recognition model, and correcting the specific named entities to obtain corrected specific named entities;”, “into the trained entity recognition model”, “through a K-means clustering algorithm” are recited at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The Examiner does not acknowledge that the newly amended claims recite a technical solution as the Applicant’s newly amended claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” which the courts have indicated may not be sufficient to show an improvement in computer-functionality (See MPEP 2106.05(a)(I)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the Applicant’s newly amended claims provide clear improvements to computer functionality and the technical field of medical information processing. The Examiner maintains that the newly amended claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” which the courts have indicated may not be sufficient to show an improvement in computer-functionality (See MPEP 2106.05(a)(I)) and “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” which the courts have indicated may not be sufficient to show an improvement to technology (See MPEP 2106.05(a)(II)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the Applicant’s newly amended claims provide clear improvements to computer functionality and the technical field of medical information processing. The Examiner maintains that the newly amended claims are similar to “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)” which the courts have indicated may not be sufficient to show an improvement in computer-functionality (See MPEP 2106.05(a)(I)) and “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” which the courts have indicated may not be sufficient to show an improvement to technology (See MPEP 2106.05(a)(II)). A confidence parameter is commonplace in the technical field of machine learning models. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (4), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the Applicant’s newly amended claims provide clear improvements to computer functionality and the technical field of medical information processing. The Examiner maintains that the newly amended claims are similar to “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” which the courts have indicated may not be sufficient to show an improvement to technology (See MPEP 2106.05(a)(II)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (5), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the Applicant’s newly amended claims provide clear improvements to computer functionality and the technical field of medical information processing. The Examiner maintains that the newly amended claims are similar to “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” which the courts have indicated may not be sufficient to show an improvement to technology (See MPEP 2106.05(a)(II)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (6), the Examiner does not find the Applicant’s argument(s) persuasive. The Applicant’s newly amended claims encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity, and the recitation of and use of the listed equations and formulas encompass Mathematical Concepts. The Examiner did not characterize the Applicant’s claimed limitations to be within the “mental process” category. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (7), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains, as recited, the “an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model comprises an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm and an output module sequentially connected in that order” are recited at a level of generality that amounts to generic computer components as the recitation of “module” and “a named entity recognition algorithm and a conditional random field algorithm” is recited as instructions to implement an abstract idea on a computer. The additional elements of “S2, constructing an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model comprises an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm and an output module sequentially connected in that order;”, “through the entity recognition model”, “S302, training the entity recognition model by using the historical patient medical record information dataset to obtain a trained entity recognition model;”, “through the first module in the trained entity recognition model”, “S305, inputting the specific named entities into the second module in the trained entity recognition model, and correcting the specific named entities to obtain corrected specific named entities;”, “into the trained entity recognition model”, “through a K-means clustering algorithm” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (8), the Examiner does find the Applicant’s argument(s) persuasive in reference to the non-statutory subject matter rejection. The Examiner does not find the Applicant’s argument(s) persuasive in reference to the dependent claims 7-8 and independent claims 9-10 rejection under an abstract idea without significantly more. Independent claims 9-10 are similarly rejected to independent claim 1, and dependent claims 7-8 are rejected due to their dependency on independent claim 1. The 35 U.S.C. 101 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Swisher et al. (U.S. Patent Pre-Grant Publication No. 2023/0260665), describes machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine.
Subramanian et al. (U.S. Patent Pre-Grant Publication No. 2023/0162830), describes a computer system includes memory hardware configured to store a machine learning model, a record database, and historical feature vector inputs.
Lu et al. (“XML-ECG: An XML-Based ECG Presentation for Data Exchanging”), describes XML-based data presentation method: XML-ECG, and the work of XML-ECG conversion tool set is introduced, which could also verify the completeness of XML-ECG.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
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