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 .
DETAILED ACTION
This Office Action represents the first action on the merits.
Claim(s) 1-9 are pending
Priority
This Application claims priority to Foreign Application JP2024-027605 filed 27 February 2024.
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 19 February 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
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 1-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 8-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an apparatus, method and computer-readable non-transitory recording medium, which are within a statutory category. The limitations of:
Claims 1, 8-9 (Claim 1 being representative)
an acquiring process of acquiring utterance information which indicates content of an utterance of a patient during a test performed on the patient, state information regarding feelings of the patient during the test, and basic information regarding the test;
an interruption predicting process of predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, for predicting a probability of interruption of a test, in which samples of past test interruptions are used as training data;
and an outputting process of outputting the probability of interruption.
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a non-transitory computer-readable medium, computer and a test assist apparatus comprising a processor, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the non-transitory computer-readable medium, computer and a test assist apparatus comprising a processor, these claims encompass analyzing data to predict interruptions during a test in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a non-transitory computer-readable medium, a computer, a test assist apparatus comprising a processor, and a test assist program that implements the identified abstract idea. The non-transitory computer-readable medium, computer and a test assist apparatus comprising a processor are not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim further recites the additional element of using a prediction model to predict interruptions during a test. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to predict interruptions during a test merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (machine learning) and thus fails to add an inventive concept to the claims.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a non-transitory computer-readable medium, a computer, a test assist apparatus comprising a processor, and a test assist program comprising a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the prediction model to predict interruptions during a test was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (machine learning). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claims 2-7 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2 merely describe(s) generating text and recommendations, which further defines the abstract idea.
Claim(s) 2 also includes the additional element of “a language model” which is analyzed the same as the “a prediction model” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 3 merely describe(s) analyzing data, which further defines the abstract idea.
Claim(s) 4 merely describe(s) acquiring and converting data, which further defines the abstract idea.
Claim(s) 5 merely describe(s) the state information as feeling uneasy and predicant interruptions based on feeling uneasy, which further defines the abstract idea.
Claim(s) 6 merely describe(s) the state information, which further defines the abstract idea.
Claim(s) 7 merely describe(s) the application of the probability of interruption, which further defines the abstract idea.
Claim Rejections - 35 USC § 103
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.
The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1, 3-9 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Shriberg et al (US Publication No. 20220199205) in view of Amthor et al (US Publication No. 20230181074).
Regarding Claim 1
Shriberg teaches a test assist apparatus, comprising at least one processor, the at least one processor carrying out:
an acquiring process of acquiring utterance information which indicates content of an utterance of a patient during a test performed on the patient, state information regarding feelings of the patient during the test, and basic information regarding the test [Shriberg at Para. 0150 teaches the systems and methods disclosed herein may use natural language processing (NLP) to perform semantic analysis on patient speech utterances. Semantic analysis, as disclosed herein, may refer to analysis of spoken language from patient responses to assessment questions or captured conversations, in order to determine the meaning of the spoken language for the purpose of conducting a mental health screening or monitoring of the patient; Shriberg at Para. 0153 teaches first, the spoken conversation may provide the patient with less time to compose a disingenuous response to a question rather than simply responding honestly to the question. Second, the conversation may feel, to the patient, more spontaneous and personal and may be less annoying to the patient than a generic questionnaire, as would be provided by, for example, simply administering the PHQ-9.];
Shriberg does not teach an interruption predicting process of predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, with use of a prediction model for predicting a probability of interruption of a test, the prediction model being generated by machine learning in which samples of past test interruptions are used as training data;
and an outputting process of outputting the probability of interruption.
Amthor teaches an interruption predicting process of predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, with use of a prediction model for predicting a probability of interruption of a test [Amthor at Para. 0058 teaches in an example, the at least one machine learning algorithm comprises two parts. A first part of the machine learning algorithm is used to determine a stress state and movement state of a person. Here movement state can mean a likelihood of moving, which can have different levels even for a stationary patient (interpret to combine with information of Shriberg)], the prediction model being generated by machine learning in which samples of past test interruptions are used as training data [Amthor at Para. 0058; Amthor at Para. 0094 teaches patient feedback on the experienced stress and movement for that patient and other psatients can also be provided for training the internal machine learning algorithms, which can be combined with sensor data and scan parameter information and patient information for those patients undergoing those scans as part of that training];
and an outputting process of outputting the probability of interruption [Amthor at Para. 0058; Amthor at Para. 0076 teaches in an outputting step 250, also referred to as step e), outputting by an output unit information relating to the predicted stress level of the patient and/or the predicted motion state of the patient.].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine information of Shriberg with the interruption probability of Amthor with the motivation to improve MRI scans.
Regarding Claim 3
Shriberg/Amthor teach the test assist apparatus according to claim 1,
Shriberg/Amthor further teach wherein the at least one processor further carries out a feelings analyzing process of analyzing feelings of the patient in the test, and in the acquiring process, the at least one processor acquires the state information which includes an analysis result provided by the feelings analyzing process [Shriberg at Para. 0150 (see Claim 1 for explanation); Shriberg at Para. 0174 teaches in some cases, the electronic report may include one or more descriptors about the patient's mental state. The descriptors can be a qualitative measure of the patient's mental state (e.g., “mild depression”). Alternatively or additionally, the descriptors can be topics that the patient mentioned during the screening. The descriptors can be displayed in a graphic, e.g., a word cloud].
Regarding Claim 4
Shriberg/Amthor teach the test assist apparatus according to claim 1,
Shriberg/Amthor further teach wherein in the acquiring process, the at least one processor acquires speech data representing a speech picked up during the test performed on the patient, and converts the speech data into the utterance information in text format [Shriberg at Para. 0169 teaches the system may provide the clinician with the dialogue between itself and the patient. This dialogue may be a recording of the screening or monitoring process, or a text transcript of the dialogue (text transcript interpreted as text format)].
Regarding Claim 5
Shriberg/Amthor teach the test assist apparatus according to claim 1,
Shriberg/Amthor further teach wherein in the interruption predicting process, the at least one processor refers to the state information to determine whether the patient is feeling uneasy [Shriberg at Para. 0355 teaches a pose tracker 2612 is capable or looking at larger body movements or positions. A slouched position indicates unease, sadness, and other features that indicate depression], and predicts the probability of interruption in a case of a determination that the patient is feeling uneasy [Amthor at Para. 0012 teaches “In this manner, a patients' stress level and/or likelihood of movement is determined by analyzing the emotional and physiological state of the patient from sensor data, data about the scan being conducted, and from data about the patient and the development of these states is predicted into the future. Thus, real-time feedback can be provided to the technologist, who can then decides that a scan should be stopped and indeed the apparatus can automatically initiated such a stop if it is predicted that the patient is about to enter an anxiety state or movement state that is not consistent with the scan protocol (anxiety state interpreted as feeling uneasy)].
Regarding Claim 6
Shriberg/Amthor teach the test assist apparatus according to claim 1,
Shriberg/Amthor further teach wherein the state information includes information which indicates at least one selected from the group consisting of a facial expression, a manner of speaking, a vital sign, and a feelings analysis result of the patient in the test [Shriberg at Para. 0296 teaches through runtime models 1802, runtime model server logic 504 estimates a health state of a patient using what the patient says, how the patient says it, and contemporaneous facial expressions, eye expressions, and poses in combination and stores resulting data representing such estimation as results 1820. Such provides a particularly accurate and effective tool for estimating the patient's health state].
Regarding Claim 7
Shriberg/Amthor teach the test assist apparatus according to claim 1,
Shriberg/Amthor further teach wherein the probability of interruption is used in decision-making by a medical service worker who performs a test on the patient [Shriberg at Para. 0276 teaches while assessment test administrator 2202 is described as conducting an interactive spoken conversation with the patient to assess the mental state of the patient, in other embodiments, assessment test administrator 2202 passively listens to the patient speaking with the clinician and assesses the patient's speech in the manner described herein. The clinician may be a mental health professional, a general practitioner or a specialist such as a dentist, cardiac surgeon, or an ophthalmologist (interpret to combine with probabilities of interruption of Amthor)].
Regarding Claim 8
Shriberg teaches a test assist method, comprising:
at least one processor [Shriberg at Para. 0031 teaches another aspect of the present disclosure provides a system comprising one or more computer processors and memory comprising machine-executable instructions that, upon execution by the one or more computer processors, implements any of the methods foregoing described in the above or elsewhere herein] acquiring utterance information which indicates content of an utterance of a patient during a test performed on the patient, state information regarding feelings of the patient during the test, and basic information regarding the test [Shriberg at Para. 0150, 0153 (see Claim 1 for explanation)];
Shriberg does not teach the at least one processor predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, with use of a prediction model for predicting a probability of interruption of a test, the prediction model being generated by machine learning in which samples of past test interruptions are used as training data;
and the at least one processor outputting the probability of interruption.
Amthor teaches the at least one processor predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, with use of a prediction model for predicting a probability of interruption of a test [Amthor at Para. 0058 (see Claim 1 for explanation)], the prediction model being generated by machine learning in which samples of past test interruptions are used as training data [Amthor at Para. 0058, 0094 (see Claim 1 for explanation)];
and the at least one processor outputting the probability of interruption [Amthor at Para. 0058, 0076 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine information of Shriberg with the interruption probability of Amthor with the motivation to improve MRI scans.
Regarding Claim 9
Shriberg teaches a computer-readable non-transitory recording medium having recorded thereon a test assist program for causing a computer to function as a test assist apparatus, the test assist program causing the computer to carry out:
an acquiring process of acquiring utterance information which indicates content of an utterance of a patient during a test performed on the patient, state information regarding feelings of the patient during the test, and basic information regarding the test [Shriberg at Para. 0150, 0153 (see Claim 1 for explanation)];
Shriberg does not teach an interruption predicting process of predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, with use of a prediction model for predicting a probability of interruption of a test, the prediction model being generated by machine learning in which samples of past test interruptions are used as training data;
and an outputting process of outputting the probability of interruption.
Amthor teaches an interruption predicting process of predicting a probability of interruption of the test performed on the patient, from the utterance information, the state information, and the basic information, with use of a prediction model for predicting a probability of interruption of a test [Amthor at Para. 0058 (see Claim 1 for explanation)], the prediction model being generated by machine learning in which samples of past test interruptions are used as training data [Amthor at Para. 0058, 0094 (see Claim 1 for explanation)];
and an outputting process of outputting the probability of interruption [Amthor at Para. 0058, 0076 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine information of Shriberg with the interruption probability of Amthor with the motivation to improve MRI scans.
Claim 2 rejected under 35 U.S.C. 103(a) as being unpatentable over Shriberg, Amthor as applied to claim 1 above, and further in view of Palanisamy et al (US Publication No. 20240008783) in view of YE et al (Foreign Publication CN-116720004-B).
Regarding Claim 2
Shriberg/Amthor teach the test assist apparatus according to claim 1,
Shriberg/Amthor further teach wherein the at least one processor further carries out a text generating process of generating, from the basic information, the utterance information, and the state information [Shriberg at Para. 0169 teaches the system may provide the clinician with the dialogue between itself and the patient. This dialogue may be a recording of the screening or monitoring process, or a text transcript of the dialogue], … [ … ]
[ … ] … and in the outputting process, the at least one processor outputs the text in addition to the probability of interruption [Amthor at Para. 0076 (interpret to combine with text of Palanisamy)].
Shriberg/Amthor do not teach [ … ] … text which represents at least one selected from the group consisting of basis for an interruption of a test performed on the patient and advice on dealing with the interruption, … [ … ]
[ … ] … with use of a language model generated by machine learning, … [ … ]
Palanisamy teaches [ … ] … text which represents at least one selected from the group consisting of basis for an interruption of a test performed on the patient [Palanisamy at Para. 0039 teaches vi) The output of each sensor module is analyzed by an AI module to determine the patient's psychological and physical condition; Palanisamy at Para. 0043 teaches according to an exemplary embodiment of the present invention, the algorithm may be a combination of a machine learning approach for the estimation of the current stress level (like SVM, CNN, etc.,), and a machine learning approach for predicting the development of the stress level during the next few minutes (such as RNN or LSTM) (interpret to combine with information of Shriberg)] and advice on dealing with the interruption [Palanisamy at Para. 0073 teaches according to an exemplary embodiment of the present invention, for instance, for a complete spine scan once the neck and upper back is over a question can be generated like “Do you want to relax/move your neck a little bit” as the patient would be trying to be still during the initial part of the scan and may need to relax a little bit. If the patient psychological condition is shown as “stress”, the dialog generator will generate question related to stress, e.g. are you under stress? And if the question is affirmative, can also do the action generation, such as action to reduce stress by playing music etc], … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Shriberg, Amthor with the advice of Palanisamy with the motivation to improve medical imaging systems.
Shriberg/Amthor/Palanisamy do not teach [ … ] … with use of a language model generated by machine learning, … [ … ]
YE teaches [ … ] … with use of a language model generated by machine learning [YE at Page 10-11 Para 9, 1 teaches when the recommendation reason text is generated, an artificial neural network model based on deep learning is needed, namely, the embodiment of the application adopts a machine learning and a method for prompting and learning correlation aiming at a pre-training model to obtain a target language model capable of generating the recommendation reason text of the I2I recommendation scene, and the target language model is used for generating the recommendation reason text of one article recommended to another article based on the capability of machine learning to realize processing and understanding of the correlation and semantic relation of two articles in the I2I recommendation scene based on the article attribute], … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Shriberg, Amthor, Palanisamy with the language model of YE with the motivation to improve the generation quality and efficiency of a finally generated model.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Dosenbach et al (US Publication No. 20230121804) discloses methods, computer-readable storage devices, and systems for reducing movement of a patient undergoing a magnetic resonance imaging (MRI) scan.
Vignisson et al (US Publication No. 20230148945) discloses a tool to assess whether a patient is at risk for a neurological disorder
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683