DETAILED CORRESPONDANCE
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 final office action on merits is in response to the communication received on 12/24/2025. Claims 2, 4, and 7-8 are cancelled. Claims 1, 3, 5-6, and 9-12 are pending and considered below.
Claim Objections
Claim 10 is objected to because of the following informalities: “The assessment evaluation device according to claim 8” should read “The assessment evaluation device according to claim 1”. Appropriate correction is required.
Subject Matter Free of Art
Claims 1, 3, 5, 6, and 9-12 include subject matter that is free of prior art. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within independent claims 1, 11, and 12. In particular, the cited prior art fails to expressly teach or suggest the specific combination of elements and ordered operations recited in these claims, including generating, from patient condition information using a machine-learned regression model, an ideal word vector representing probabilities that predetermined candidate words should be included in a nursing assessment, and evaluating the nursing assessment by calculating a similarity between the generated ideal word vector and a described word vector generated from the nursing assessment text. For claims 1, 11, and 12, the cited prior art of record also fails to expressly teach or suggest, either alone or in combination, predicting expected documentation vocabulary from patient condition information and determining assessment quality based on a comparison between predicted documentation content and actual documentation content.
The closest prior art of record includes Thomas (U.S. Patent Publication 2018/0240352 A1), referred to hereinafter as Thomas, in view of Tang (International Publication WO 2021/139424 A1), referred to hereinafter as Tang.
Thomas teaches evaluating a clinical assessment by comparing entered keywords or responses with predetermined expected responses and generating a score based on textual similarity. However, Thomas fails to teach or suggest generating, using a machine-learned regression model trained on past patient information, a probability-based ideal word vector derived from patient condition information that represents words that should appear in the assessment.
Tang teaches vectorizing medical record text using a bag-of-words model and evaluating text based on features extracted from the text itself. However, Tang fails to teach or suggest generating an expected documentation vocabulary from patient condition attributes and comparing that predicted vocabulary with the actual documentation to determine an evaluation.
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, 3, 5-6, and 9-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1, 3, 5-6, 9-10 are drawn to an assessment evaluation device, claim 11 is drawn to an assessment evaluation method, claim 12 is drawn to a non-transitory computer readable storage medium. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. 101.
Step 2A Prong One
Claim 1 recites the limitations of performing morphological analysis on a nursing assessment text for a target patient to extract words contained in the nursing assessment text; generating, based on the nursing assessment text which is made as a necessary description item of a nursing record for a target patient, a described word vector by vectorizing whether or not each of the extracted words in the nursing assessment text is correspond to predetermined candidate words; learning a relationship between patient conditions and a probability that each of the predetermined candidate words is necessary to be described in the nursing assessment; generating an ideal word vector, wherein the ideal word vector is a probability vector representing, for each of the predetermined candidate words, a probability that the candidate word should be included in a nursing assessment for the target patient; calculating a degree of similarity between the described word vector and the ideal word vector; and determining an evaluation of the nursing assessment based on the calculated degree of similarity. These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind or by using a pen and paper. But for the “at least one memory configured to store instructions; and at least one processor configured to execute the instructions to” language, the claim encompasses a user reading the assessment, identifying relevant terms, determining expected terms based on patient information, comparing the identified terms with the expected terms, and judging the adequacy of the assessment in their mind or by using a pen and paper. The mere nominal recitation of at least one memory configured to store instructions; and at least one processor does not take the claim limitations out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Independent claim 11 and 12 recites identical or nearly identical steps with respect to claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
perform morphological analysis on a nursing assessment text for a target patient to extract words contained in the nursing assessment text;
generate, based on the nursing assessment text which is made as a necessary description item of a nursing record for a target patient, a described word vector by vectorizing whether or not each of the extracted words in the nursing assessment text is correspond to predetermined candidate words;
access a regression model that has been trained, by machine learning on past patient information including nursing records and patient condition information, to learn a relationship between patient conditions and a probability that each of the predetermined candidate words is necessary to be described in the nursing assessment;
acquire a patient condition of a target patient, wherein the patient condition comprising at least one of attribute information, examination results, and past nursing records of the target patient;
generate an ideal word vector by inputting the acquired patient condition into the regression model, wherein the ideal word vector is a probability vector representing, for each of the predetermined candidate words, a probability that the candidate word should be included in a nursing assessment for the target patient;
calculate a degree of similarity between the described word vector and the ideal word vector;
determine an evaluation of the nursing assessment based on the calculated degree of similarity; and
cause an output device to generate a user interface displaying the evaluation and a list of one or more recommended words for inclusion in the nursing assessment, wherein the one or more recommended words is selected from the predetermined candidate words based on a comparison between the ideal word vector and the described word vector.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of evaluating the adequacy of a nursing assessment by comparing identified assessment terms with expected terms derived from patient information in a computer environment. The claimed computer components (i.e., at least one memory configured to store instructions; at least one processor configured to execute the instructions to; access a regression model that has been trained, by machine learning on past patient information including nursing records and patient condition information; by inputting the acquired patient condition into the regression model) are recited at a high level of generality and are merely invoked as tools to perform an existing process of reviewing and grading a written assessment based on whether appropriate topics were discussed. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional elements of acquiring a patient condition of a target patient, wherein the patient condition comprising at least one of attribute information, examination results, and past nursing records of the target patient; and causing an output device to generate a user interface displaying the evaluation and a list of one or more recommended words for inclusion in the nursing assessment, wherein the one or more recommended words is selected from the predetermined candidate words based on a comparison between the ideal word vector and the described word vector. These limitations are recited at a high level of generality (i.e., as a general means of obtaining data input and presenting the results of the analysis), and amounts to merely data gathering, displaying a result, and insignificant application which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of evaluating the adequacy of a nursing assessment by comparing identified assessment terms with expected terms derived from patient information in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 1, under step 2B, the additional elements of acquiring a patient condition of a target patient, wherein the patient condition comprising at least one of attribute information, examination results, and past nursing records of the target patient; and causing an output device to generate a user interface displaying the evaluation and a list of one or more recommended words for inclusion in the nursing assessment, wherein the one or more recommended words is selected from the predetermined candidate words based on a comparison between the ideal word vector and the described word vector have been evaluated. The assessment evaluation device comprising at least one memory configured to store instructions and at least one processor performs a general function of receiving patient data for analysis and evaluation of the nursing assessment, which represents a well-understood, routine, and conventional activity in the field of medical record processing and computerized data analysis systems. The specification discloses that the processor is used in its ordinary capacity as a data input device and does not describe any improvement to the computer itself or to the functioning of the overall computer system (see [0049]). Also, as noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis without a technological improvement does not add significantly more to an abstract idea. The use of the assessment evaluation device is no more than collecting information before evaluating the adequacy of the nursing assessment and does not integrate the abstract idea into a practical application. Additionally, as noted in In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016), merely displaying the evaluation results and suggested words to a user represents an insignificant application of the underlying mental process, as the user interface simply communicates the result of the evaluation and recommended words and does not impose any meaningful limitation or add any technological improvement. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claims 3 and 9 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 5-6, and 10 recite the additional elements of the at least one processor is configured to execute the instructions (claim 5, 6, 10), to cause the output device to output in addition to the list of one or more recommended words, one or more words which is not included in the ideal words among words described in the nursing assessment (claim 10). However, these additional elements amount to implementing an abstract idea on a generic computing device or insignificant application (i.e., an insignificant extra-solution activity). As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
Regarding the rejection of claim 1, 3, 5-6, and 9-12, the Examiner has considered Applicant’s amendments and arguments in light of the present amendments and withdraws the prior art rejection.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 12/24/2025 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Applicant states that the claims are patent-eligible because they recite specific data processing operations such as vector generation, machine-learned regression models, and similarity calculations, and therefore allegedly improve the functionality of an electronic nursing record system. This argument is not persuasive.
Claim 1 is directed to the evaluation of the adequacy of a nursing assessment by identifying terms in a written record, determining expected terms based on patient information, comparing the two, and presenting feedback. This is considered a mental evaluation process (i.e., reviewing a document to determine whether appropriate topics were discussed) that implements using mathematical tools (vectorization, probability estimation, and similarity calculation). The recited mathematical operations merely formalize the reasoning a reviewer would perform mentally when assessing documentation quality and therefore do not remove the claim from the abstract idea grouping.
Applicant also states that the use of machine learning and vector representations constitutes a technical improvement. However, the claims do not recite any improvement to machine learning technology, natural language processing technology, computer architecture, or data storage. The regression model is used only as a tool to evaluate informational content of a record, rather than to improve the functioning of the computer itself. The use of a trained model to automate decision-making does not suggest eligibility where the claim is directed to analyzing and evaluating information.
Applicant also states that the invention cannot be performed mentally because it uses machine learning and probability calculations. However, eligibility is determined by the scope of the claim, not whether a human could practically perform each calculation. The scope of the claim is the automated review and grading of documentation based on expected content in the documents. The mathematical processing merely implements that evaluation using a computer and does not alter it as an abstract mental process.
Lastly, the additional elements (acquiring patient data and displaying recommended words) amount to data gathering, output of results, and insignificant application. Such pre-solution and post-solution activity does not integrate the abstract idea into a practical application or provide an inventive concept (see Electric Power Group; In re Brown, 645 F. App’x 1014 (Fed. Cir. 2016)).
Accordingly, claim 1, 3, 5-6, and 9-12 remain directed to an abstract idea without significantly more, and the rejection under 35 U.S.C. §101 is maintained.
Claim Rejections - 35 USC § 103
Regarding the rejection of claim 1, 3, 5-6, and 9-12, the Examiner has considered Applicant’s amendments and arguments in light of the present amendments and withdraws the prior art rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
HAYAKAWA et al. (International Publication No. WO2014076777A1) teaches a method that identifies patients with similar medical records and extracts clinically relevant information from those cases to support diagnosis and treatment of a target patient.
Tao (U.S. Patent No 9984682 B1) teaches an automatic speech recognition system that uses a trained neural network to generate transcription and score oral language assessments responses by comparing neural network output vectors with trained score reference vectors, eliminating the need for human transcription and scoring.
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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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685