DETAILED ACTION
This communication is in response to the Arguments/Remarks filed on 11/19/2025. Claims 1-3 and 5-9 are pending and have been examined. Hence, this Action has been made FINAL.
Any previous objection/rejection not mentioned in this Office Action has been withdrawn by the examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 13, 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. JP2022-186502, filed on 11/22/2022.
Response to Arguments
Applicant's arguments filed 11/19/2025 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 101 rejections for claims 1-9, the applicant asserts that Claim 1 is patent eligible under Prong Two of the revised Step 2A of the Alice test. Applicant respectfully submits that claim 1 is patent eligible under Prong Two of the revised Step 2A of the Alice test. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. If the recited exception is integrated into a practical application of the exception, then the claim is eligible at Prong Two of revised Step 2A. Even if it is assumed claim 1 recites a judicial exception, which Applicant does not concede, Applicant respectfully submits that the claim is patent eligible under prong two of the revised Step 2A of the Alice test because the claim integrates the alleged judicial exception into a practical application.
MPEP 2106.04(II)(A)(2) provides that in "Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception."
Examiner respectfully disagrees, the components appearing in the amended independent claim do not satisfy step 2A as they are merely generic components that the method is being applied with. The processor, memory, and non-transitory storage medium are generic computer components that occur when applying the method via a computer rather than via the human mind. The artificial intelligence as claimed is also just applying the method as its merely stated that the apparatus “uses an artificial intelligence”. Overall, none of these elements have a notable impact on how the limitations are performed. Rather, a method that could be done by the human mind is instead being done by a computing device and the listed elements are how its being applied to said device.
Applicant further asserts that Claim 1 is patent eligible of Step 2B of the Alice test
Moreover, Applicant respectfully submits that even if it is assumed the claim is directed to an abstract idea, which is not conceded, independent claim 1 recites significantly more than any allegedly abstract idea.
In particular, MPEP 2106.05(I)(A)(v) indicates that in evaluating Step 2B, an additional element or combination of elements "[adds] a specific limitation other than what is well- understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application," has been found to qualify as "significantly more" when recited in a claim with a judicial exception.
Applicant submits that claim 1, as amended, provides an "inventive concept," and does not simply append well-understood, routine or conventional activities.
Examiner respectfully disagrees, each of the additional components is shown to be general purpose in the specification (specific passages are provided in the rejection below). The overall method of extracting expressions from a medical document and performing language processing is not specific to a computer functionality or improvement thereof. The general-purpose components presented are not essential to the method and would be used to perform any number of tasks on a computing device.
With respect to the 35 U.S.C. 102 rejections for claims 1-9, the applicant asserts that Hartman does not teach or suggest a processor configured to "extract a unique expression related to medical care from the string." Because Hartman performs abstractive summarization that understands the intent of sentences to generate new sentence, it does not teach or suggest "extract[ing] a unique expression related to medical care from the string."
Hartman discloses: "Abstractive text summarization, or abstractive summarization, has been proposed as a means to alleviate clinical documentation burden by summarizing, i.e. condensing, clinical notes. Abstractive text summarization is the task of generating a short summary consisting of a few sentences that captures the salient ideas of a note, article or a passage.", "In certain embodiments, the invention provides a machine learning (ML) architecture that generates an abstractive summary of the hospital course section of a discharge summary...", and "Each of the three sections are textual abstractive summaries of a larger volume of electronic medical records that are entered by medical personnel, typically in a hospital setting.". BART, a type of generative model, appears in Figs. 5A - 5C.
The configuration disclosed in Hartman differs from claim 1.
Examiner respectfully disagrees, Hartman et al. does read on the independent claims as currently presented. Hartman et al. has the goal of summarizing medical documents; in order to achieve that goal, it isolates specific phrases within the text and performs a beam search to determine if the statements are factual or non-factual. This is a form of language processing being performed on strings found within the medical document. Furthermore, the reference states that machine learning models such as BERT are utilized in order to perform this process and create an output summary. Further details and mapping for this rejection can be found below.
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 and 5-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 9 recite at least one [memory] storing instructions; and at least one [processor] configured to execute the instructions to: input text information; convert the text information into a string; extract a unique expression related to medical care from the string; and perform language processing related to medical care based on the unique expression. wherein the language processing apparatus uses an [artificial intelligence model] constructed through machine learning, and wherein the at least one processor is configured to: select the unique expression related to an event related to the language processing; perform the language processing based on the selected unique expression; and output a result of the language processing.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of reading a document, converting into a string by isolating specific words or phrases, and identifying expressions in those words or phrases related to medical care. A human can do language processing on the expression. Language processing in this case would represent the human mind interpreting the expression and surrounding context and then making a decision based on their knowledge of the patient/term. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite the additional components of a memory, a processor, and an artificial intelligence model. The memory is merely a component that the method is being applied with. The memory is detailed on Page 9, Lines 20-35 of the specification with a generic description/example of the component. The processor is merely a component that the method is being applied with. The processor is detailed on Page 4, Lines 1-11 of the specification with a generic description/example of the component. The artificial intelligence model is merely a component that the method is being applied with. The artificial intelligence model is detailed on Page 8, Lines 28-35 of the specification with a general-purpose implementation of a BERT model. Claim 9 lists the additional component non-transitory computer readable medium. The non-transitory computer readable medium is merely a component that the method is being applied with. The non-transitory computer readable medium is detailed on Page 9, Lines 20-35 of the specification with a generic description/example of the component. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim 2 recites wherein information on a patient is divided into and separately managed as information recorded in a structured table and the text information, the at least one processor is further configured to execute the instructions to perform the language processing based on the structured table and the unique expression.
The limitations in this claim, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of both creating and interpreting data that is in a structured table and plain text information. For example, writing a log for things that occur to a patient in a hospital where columns of the log represent times, dates, symptoms, or names. Then reading hand written notes relating to the patient’s current status and referencing the table for history related context to their current state. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not present in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 3 recites wherein the text information indicates an event that occurred in the patient in a medical institution.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Following the previous example a human is capable of writing/reading a note about what has recently happened to a patient in a hospital. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not present in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 5 recites wherein the at least one processor is further configured to execute the instructions to extract the unique expression from the text information expressed by a vector.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human mind could represent text in the form of a vector by providing a number to each word and using those numbers for categorization later. For example, 0=name, 1=symptom, 2=medication, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not present in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 6 recites wherein the at least one processor is further configured to execute the instructions to extract the unique expression related to nursing.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human can read text and identify terms that are related to nursing. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not present in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 7 recites wherein the at least one processor is further configured to execute the instructions to prepare a medical document by using a template.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human can create a medical document using a template. For example, following an example document for guidance while filling out a new document. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not present in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 5-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Patent Publication 12176083 B2 (Hartman et al.).
Regarding Claims 1, 8, and 9, Hartman et al. teaches A language processing apparatus comprising: at least one memory storing instructions;
(MRAS server 320 includes a processor, data storage for storing video clips and intermediate results, and a non-volatile memory for storing program code and data.) (Column 5, Lines 60-67).The MRAS system is the name for the system of Hartman et al. and includes a memory.
and at least one processor configured to execute the instructions to:
(MRAS server 320 includes a processor, data storage for storing video clips and intermediate results, and a non-volatile memory for storing program code and data.) (Column 5, Lines 60-67).The MRAS system is the name for the system of Hartman et al. and includes a processor.
input text information;
(Clinical notes, as defined hereinabove, for a patient are the primary source of data for constructing the narrative summary of the course of treatment for the patient, referred to as a daily course. Clinical notes can be accessed in the EMR system in either the format of text, audio, or video.) (Col. 6, Lines 60-67)
The system accesses clinical notes as a form of input to the method.
convert the text information into a string;
(EMR data 402 is retrieved by an API interface engine 404 through an API layer. An international standard, HL7, for exchanging data from one healthcare entity to another is used in certain embodiments. Use of HL7 facilitates transfer of data between the multiple different entities, enabling all parties to format and parse the data. FHIR is a recent version of the HL7 standard that is implemented with a RESTful web service and data formats such as XML and JSON. API interface engine 404 is designed to receive messages from the healthcare entity and translate the messages into a known format) (Col. 8-18)
The system converts the text into a known format such as XML or JSON which both store text information in a string format.
extract a unique expression related to medical care from the string;
(steps 424, 436 and 442, that summarizes or extracts the most important sentences or phrases from clinical notes) (Column 7, Lines 32-42). Hartman et al. creates summaries of clinical notes by extracting important sentences or phrase.
and perform language processing related to medical care based on the unique expression.
(At step 426 factuality analysis is performed to eliminate non-factual words or phrases from the summary.) (Column 9, Lines 53-54).
(Constrained beam search eliminates from summaries any words or phrases that are not present in the original source documents.) (Column 8, Lines 16-18).
After identifying the important phrase, further language processing is done on them to determine if they are factual to the clinical notes.
wherein the language processing apparatus uses an artificial intelligence model constructed through machine learning, and wherein the at least one processor is configured to:
(The machine learning models used in steps 424, 434, 436, and 442 of FIG. 4 are all encode-decoder transformer models. In other embodiments, other machine learning models may be used for one or more of these steps.) (Col. 4, Lines 45-49)
The entire method is shown in Fig. 4 and it is stated that machine learning models are used for summarization, identifying significant notes, and extracting follow ups.
select the unique expression related to an event related to the language processing;
(Abstractive summarization generates new sentences through synthesis to form the summary. … generate an abstractive summary from a clinical note … potential or candidate summaries generated by transformer models at steps 424, and 436 are subjected to an additional processing step that eliminates non-factual words or phrases.) (Column 7, Line 66 – Column 8, Line 13).
Hartman et al. uses both extractive and abstractive summarization. When abstractive summarization is used individual words or phrases have additional language processing done on them to determine the correctness. By doing this process for each word or phrase, it is selecting a unique expression.
perform the language processing based on the selected unique expression;
(the potential or candidate summaries generated by transformer models at steps 424, and 436 are subjected to an additional processing step that eliminates non-factual words or phrases. In one embodiment, a novel algorithm, referred to herein as constrained beam search, analyzes summaries for factuality. Constrained beam search eliminates from summaries any words or phrases that are not present in the original source documents. This effectively reduces the likelihood that extraneous, non-factual statements, which have no basis in the source text of a corpus of clinical notes will be introduced into a resulting summary.) (Column 8, Lines 10-24).
As seen above, individual words or phrases have further language processing done to determine correctness and decide if they should be included or excluded from the output.
and output a result of the language processing.
(In contrast, standard beam search considers multiple alternatives based on the “beam width”, i.e. the number of alternative output sequences that will be considered as each position in the sequence of words. Each generated token, i.e. potential sequence of output words, is saved as a candidate, and the top beam widths are saved after each time-step. Once the <end> token is predicted, the best beam is chosen as the output, where the final beam selected is taken as the text summary of the input text sequence.) (Col 14, Lines 40-48).
An output is created from the language processing in the form of a summary.
Regarding Claim 2, Hartman et al. teaches the system of claim 1, wherein information on a patient is divided into and separately managed as information recorded in a structured table and the text information,
(EMR data 402 typically includes both structured and unstructured data from the patient. Structured data is defined as data that is organized, categorical, and formatted for search within a database, while unstructured data, typically in the form of text, is not. Examples of unstructured data include text files) (Column 7, Lines 1-6).
The clinical notes can be in the form of structured data which would encompass a structured table. They can also include unstructured data such as text files.
the at least one processor is further configured to execute the instructions to perform the language processing based on the structured table and the unique expression.
(if an original clinical note states: “Patient is a 47 year old white male with a history of epilepsy who presents to Weill Cornell Medicine.”, then structured data may be added at the beginning of the note to help with processing. Continuing the example, the edited note may read: “Admit Diagnosis: Epilepsy; Age: 47; Note Date: Jul. 21, 2022, Note Text: Patient is a 47 year old white male with a history of epilepsy who presents to Weill Cornell Medicine . . .”. This step enables, the machine learning model, which executes at step 426 to process the unstructured data.) (Column 9, Lines 29-39).
In the above quote it can be seen that unstructured data will be reformatted into a structured format. The individual structured components represent important parts of the medical text and are equivalent to the unique expressions being identified.
Regarding Claim 3, Hartman et al. teaches the system of claim 2, wherein the text information indicates an event that occurred in the patient in a medical institution.
(The primary data source selected for use by section classifier 410 are physician notes provided by physicians that describe what is happening or recently happened to a patient) (Column 10, Lines 3-6). The clinical notes used in Hartman et al. are related to events that occur to patients in a medical institution.
Regarding Claim 5, Hartman et al. teaches the system of claim 1, wherein the at least one processor is further configured to execute the instructions to extract the unique expression from the text information expressed by a vector.
(Once preprocessed at step 422, the text is input as the source content to a transformer model at step 424 to generate a table or matrix that includes potential summaries, each assigned a probability.) (Column 9, Lines 40-44). The use of a transformer model, matrix, and probabilities are all processes that could be considered vectorizing the text and expressions.
Regarding Claim 6, Hartman et al. teaches the system of claim 1, wherein the at least one processor is further configured to execute the instructions to extract the unique expression related to nursing.
(The clinical note author may be, for example, a MD, RN, NP. And common clinician notes types, as previously discussed, include Admission Note, History & Physical Note, and Progress Note.) (Column 8, Lines 52-55). The clinical notes used in Hartman et al. can be written by nurses or contain information pertaining to them.
Regarding Claim 7, Hartman et al. teaches the system of claim 1, wherein the at least one processor is further configured to execute the instructions to prepare a medical document by using a template.
(The three sections addressed herein, namely HPI section 202, daily course section 204 and follow-up section 206, when taken together form an automated narrative summary of a patient's hospital stay.) (Column 11, Lines 57-63). The outputted summary is broken into three section which is equivalent to a template being used.
Conclusion
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 NICHOLAS DANIEL LOWEN whose telephone number is (571)272-5828. The examiner can normally be reached Mon-Fri 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D Shah can be reached at (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
01/27/2026