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 claim(s)
Claims 1-13 have been examined.
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 1-13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim 1 recite(s) a medical examination support apparatus, which is within a statutory category (i.e. machine). Claim 12 recite(s) a medical examination support method, which is within a statutory category (i.e. process). Claim 13 recite(s) a non-transitory computer-readable medium, which is within a statutory category (i.e. manufacture).
Step 2A - Prong One:
Regarding Prong One of Step 2A (MPEP2106.04-.07), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The limitation of Independent claims 1 recites at least one abstract idea. Specifically, Claim 1 recites the steps of
A medical examination support apparatus comprising processing circuitry configured to:
obtain text information representing different kinds of medical records at different time points, the medical records associated with examination information of a patient,
set imperative-sentence information representing an imperative sentence to be input to a large language model, based on the text information,
determine tag candidate information representing tag candidate to be added to the text information, based on the imperative-sentence information, and
output the tag candidate information.
The limitations “obtain text information representing different kinds of medical records at different time points, the medical records associated with examination information of a patient, set imperative-sentence information representing an imperative sentence to be input to a large language model, based on the text information, determine tag candidate information representing tag candidate to be added to the text information, based on the imperative-sentence information, and output the tag candidate information”, describes managing persona behaviors and interactions between people, bur for the recitation of generic processor, but for the recitation of generic processor, a large language model. These steps of obtaining text information, setting imperative-sentence to be input to a large language model, determining tag candidate. These claim limitations, under broadest reasonable interpretation, covers the interaction between people. If a claim limitation, under broadest reasonable interpretation, covers managing personal behaviors and personal interactions but for the recitation of generic computer, then it falls within the “Certain Method of Organizing Human Activity Accordingly, the claim is directed toward at least one abstract idea.
Dependent Claims 2-11 add further limitations which are also directed to an abstract idea. For example, claim 2 includes the user data to include patient information. Claim 3 provides performing natural language to set imperative sentence in a large language model. Dependent claims 4 include data structured and unstructured formats. Claims 6-11 include the medical reports. These claims are directed to a certain method of organizing human activity for the same reason as described in the independent claims.
Furthermore, the abstract idea for claim 12 and claim 13 is identical as the abstract idea for claim 1, because the only difference between claim 1 and claims 12, 19 is that claim 1 recites an apparatus, whereas claims 12 recites a method, and whereas claim 13 recites a non-transitory computer-readable medium.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A (See MPEP2106.04-07), it must be determined whether the claim, as a whole integrates the abstract idea into a practical application. As noted in MPEP2106.04-07, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
For claims 1, 12-13 the judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – an apparatus/non-transitory computer-readable medium storing instructions, a large language model, and a processing circuitry to execute the instructions. The memory device/non-transitory computer-readable medium, a large language model in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite the additional elements of a computing device for presenting guidance comment, creating instruction command. The computing device is recited at a high-level of generality such that it is simply adding a general purpose computer after the fact to the abstract idea, as per MPEP 2106.05(f)(2), which amounts to mere instructions to apply the exception. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to implement and monitor a care plan, a productivity, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05).
For these reasons, representative independent claims 1, 8 do not recite additional elements that integrate the judicial exceptions into a practical application. (The Examiner notes the mere recitation of a processor, memory, a circuitry, non-transitory medium does not take the claim out of certain method of organizing human activity. Thus the claim recites an abstract idea)
Step 2B:
Regarding Step 2B, independent claims 1, 12-13 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
For claims 1, 12-13 limit the use of one or more processors, an apparatus, a circuitry, etc.... The specification merely describes the use of these computing components. The Examiner submits that these limitations amount to merely using these computer devices as well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018).), and MPEP 2106.05(d)(I)(2). Further the use of generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patient-ineligible abstract idea into a patent-eligible invention”).
For the reasons stated, the claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 USC 101. Therefore, claims 1-13 are rejected under 35USC101 as being held patent ineligible.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dandala et al (US20210057068A1 hereinafter Dandala) in view of Archuleta (US11,281,855B1).
With respect to claim 1, Dandala teaches a medical examination support apparatus comprising processing circuitry configured to:
obtain text information representing different kinds of medical records at different time points, the medical records associated with examination information of a patient (‘068; Abstract: Dandala describes automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags),
set imperative-sentence information representing an imperative sentence to be input to a large language model, based on the text information (‘068; Para 0056: identify features derived from the morpho-syntactic properties of verbs in the sentences. The primary properties and distinctions included the position of the verb (i.e. head of a main clause or an auxiliary, which marks tense and aspect) and the tense markings themselves (past, present or future). Features considered important in identifying treatment plan sentences as they are typically expressed using future tense or imperative verbs. (Example: The patient will be sent for an MRI to further evaluate the knee). On the other hand, an assessment sentences are typically written using past tense verbs. (Example: The patient was able to go through PT but at a much slower pace).Assertions: Identify clinical assertions on medical concepts. Specifically, adding negated and hypothetical assertions on clinical concepts as features. Features considered useful as disease assessments often have negations. (Example: No significant changes since the prior exam). Whereas, treatment plans often contain hypotheticals (Example: Call back if symptoms persist or worsen)Sentence Length: information, such as treatment plans, are expressed as multiple short sentences.Global Features: Utilizing section-labels identified using note section classifiers (introduced earlier), note type (progress note, discharge note etc.), note category (primary care, test reports, specialty category etc.), and provider type (physician, social worker, registered nurse practitioner etc.) as features.).
Dandala discloses the cognitive system 100 is configured to implement a request processing pipeline 108 that receive inputs from various sources. The requests may be posed in the form of a natural language question, natural language request for information, natural language request for the performance of a cognitive operation, or the like. For example, the cognitive system 100 receives input, from the network 102, a corpus or corpora of electronic documents 106, cognitive system users, and/or other data and other possible sources of input (‘068; Para 0041). Dandala further discloses the cognitive system training engine 134 trains cognitive system 100 to classify sentences within clinical notes of the EMRs in the corpus or corpora of data 106 identified by NLP engine 122 utilizing machine learning features such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), or the like, for identification and classification of sentences within clinical notes of the EMRs in the corpus or corpora of data (‘068; Para 0055).
Archuleta discloses an imperative sentence to be input to a large language model, based on the text information (‘855; page/lines 21/1-12: a way in which to map a sentence to its word-embedding vector. Word embedding comes from language modeling in which feature learning techniques map words to vectors of real numbers. Word embedding allows words with similar meaning to have similar representation in a lower dimensional space. Converting words to word embeddings is a necessary pre-processing step in order to apply machine learning algorithms which will be described in the accompanying drawings and descriptions. A language model is used to train a large language corpus of text in order to generate word embeddings).
It would have been obvious to one of ordinary skill in the art before the effective filing of claimed invention to modify the method, system of identifying information in narratives EMR of Dandala with the technique of reinforcement learning approach to decode sentence ambiguity as taught by Archuleta in order to obtain an imperative sentence to be input to a large language model
Dandala in view of Archuleta discloses
determine tag candidate information representing tag candidate to be added to the text information, based on the imperative-sentence information, and output the tag candidate information (‘068; Abstract: The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The mechanism performs statistical analysis of the identified sections that contain tags identified in the tag data structure to identify one or more valid stop/start conditions; extracts a first set of positive examples of sentences for a selected type of information, and then trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information).
Claims 12 and 13 are rejected as the same reason with claim 1.
With respect to claim 2, the combined art teaches the medical examination support apparatus according to claim 1, Dandala discloses wherein the processing circuitry is configured to obtain patient information of the patient, and obtain the examination information associated with the patient information, based on the patient information (‘068; Para 0023: providing mechanisms for training supervised machine learning models to automatically identify information in plain text narratives of patients' electronic medical records (EMRs), such as assessments, treatment plans, or the like. Using clinical notes, tagged sections from clinical notes serve are utilized as a source of high-precision training data to extract the sentences within the scope of the tag).
With respect to claim 3, the combined art teaches the medical examination support apparatus according to claim 1, Archuleta discloses wherein the processing circuitry is configured to: perform natural language processing to the text information to generate brief-history information based on the text information, and set the imperative-sentence information representing the imperative sentence to be input to the large language model, based on the brief-history information (‘855; page/lines 14/8-26; page/lines 21/1-12: One of the embodiments provides a way in which to map a sentence to its word-embedding vector. Word embedding comes from language modeling in which feature learning techniques map words to vectors of real numbers. Word embedding allows words with similar meaning to have similar representation in a lower dimensional space. Converting words to word embeddings is a necessary pre-processing step in order to apply machine learning algorithms which will be described in the accompanying drawings and descriptions. A language model is used to train a large language corpus of text in order to generate word embeddings).
With respect to claim 4, the combined art teaches the medical examination support apparatus according to claim 1, Dandala discloses wherein the examination information is in the form of structured data or non-structured data, the structured data is data in a defined input format, and the non-structured data is data in a non-defined input format (‘068; Para 0025: These requests may be provided as structure or unstructured request messages, natural language questions, or any other suitable format for requesting an operation to be performed by the healthcare cognitive system).
With respect to claim 5, the combined art teaches the medical examination support apparatus according to claim 3, Archuleta discloses wherein the brief-history information includes clinical history information representing a clinical history of the patient, attitude information representing a manner of thinking of the patient, and examination-phase information representing an examination phase of the patient (‘855; page/lines 2/50-24/10).
With respect to claim 6, the combined art teaches the medical examination support apparatus according to claim 5, Dandala discloses wherein the tag candidate information represents tag candidates associated with the clinical history information, the attitude information, and the examination-phase information (‘068; Abstract; Para 0035).
With respect to claim 7, the combined art teaches the medical examination support apparatus according to claim 5, Dandala discloses wherein the clinical history information includes at least one of a current clinical history, a past clinical history, a clinical history related to a surgical history and a family history, and the clinical history includes at least one of a date and time, a disease name, a method of treatment, and medication information about drug administration (‘068; Abstract; Para 0099: cardiac disease).
With respect to claim 8, the combined art teaches the medical examination support apparatus according to claim 5, Dandala discloses wherein the attitude information is information related to an external change of the patient (‘068; Para 0118).
With respect to claim 9, the combined art teaches the medical examination support apparatus according to claim 5, Dandala discloses wherein the examination-phase information includes information related to a treatment, and the information related to a treatment includes at least one of a disease, recurrence, examination, a notification, hospitalization, a surgical history, radiation, pharmacotherapy, hormonal therapy, and genetic testing (‘068; Para 0056).
With respect to claim 10, the combined art teaches the medical examination support apparatus according to claim 6, Dandala discloses wherein the tag candidate information includes the text information, the tag candidates, and a check box for allowing a user selection (‘068; Abstract; Paras 0035-0036, 0039).
With respect to claim 11, the combined art teaches the medical examination support apparatus according to claim 3, Dandala discloses wherein the processing circuitry is configured to: obtain feedback information representing feedback about the tag candidate information, and set the imperative-sentence information again based on the brief-history information and the feedback information (‘068; Para 0036: The statistical model is used to summarize a level of confidence that the QA pipeline has regarding the evidence that the potential response, i.e. candidate answer, is inferred by the question. This process is repeated for each of the candidate answers until the QA pipeline identifies candidate answers that surface as being significantly stronger than others and thus, generates a final answer, or ranked set of answers, for the input question.).
Conclusion
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686