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
In the amendments filed 28 April 2026:
Claims 1-4,6,8-9 are amended
Claims 1-9 are pending
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are 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.
Step 1
The claim recites an apparatus, method and computer-readable non-transitory recording medium, which are within a statutory category.
Step 2A1
The limitations of:
Claims 1, 8-9 (Claim 1 being representative)
an acquiring process of acquiring speech data representing a speech picked up, and image data captured during a test performed on a patient;
an extracting process of converting the speech data into utterance data in text format and extracting the utterance data of the patient by speaker recognition on the speech data;
an analyzing process of analyzing feelings of the patient in the test based on the image data and the utterance data of the patient;
an interruption predicting process of predicting, in a case of a determination that the patient is feeling uneasy, a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, with use of a prediction model for predicting a probability of interruption of a test, generated in which samples of past test interruptions are used as training data;
and an outputting process of outputting the probability of interruption,
wherein the state information includes a facial expression, a manner of speaking, a vital sign, and a feeling analysis result of the patient in the test,
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. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to predict an interruption during a medical test in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “acquiring, extracting, analyzing” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. 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.
Step 2A2
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, computer and a test assist apparatus comprising a processor 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 computer performing generic computer functions) such that it amounts 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 are directed to an abstract idea.
The claims further recite the additional element of using a prediction model generated by machine learning 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 and thus fails to add an inventive concept to the claims.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a non-transitory computer-readable medium, computer and a test assist apparatus 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 a prediction model generated by machine learning 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. 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 speech data representing a speech picked up, and image data captured during a test performed on a patient [Shriberg at Para. 0240 teaches in step 1502, assessment test administrator 2202 initiates the spoken conversation with the subject patient. In this illustrative embodiment, assessment test administrator 2202 initiates a conversation by asking the patient the initial question of the assessment test. The initial question is selected in a manner described more completely below. The exact question asked isn't particularly important. What is important is that the patient responds with enough speech that assessment test administrator 2202 may evaluate the quality of the video and audio signal received from patient device 312];
an extracting process of converting the speech data into utterance data in text format and extracting the utterance data of the patient by speaker recognition on the speech data [Shriberg at Para. 0170 teaches the system may additionally provide the clinician with a “word cloud” or “topic cloud” extracted from a text transcript of the patient's speech. A word cloud may be a visual representation of individual words or phrases, with words and phrases used most frequently designated using larger font sizes, different colors, different fonts, different typefaces, or any combination thereof. Depicting word or phrase frequency in such a way may be helpful as depressed patients commonly say particular words or phrases with larger frequencies than non-depressed patients];
an analyzing process of analyzing feelings of the patient in the test based on the image data and the utterance data of the patient [Shriberg at Para. 0292 teaches descriptive features or descriptive analytics are interpretable descriptions that may be computed based on features in the speech, language, video, and metadata that convey information about a speaker's speech patterns in a way in which a stakeholder may understand. For example, descriptive features may include a speaker sounding nervous or anxious, having a shrill or deep voice, or speaking quickly or slowly. Humans can interpret “features” of voices, such as pitch, rate of speaking, and semantics, in order to mentally determine emotions];
an interruption predicting process of predicting, in a case of a determination that the patient is feeling uneasy [Shriberg at Para. 0328 teaches the high level feature representor 2320 includes leveraging existing models for frequency, pitch, amplitude and other acoustic features that provide valuable insights into feature classification. A number of off-the-shelf “black box” algorithms accept acoustic signal inputs and provide a classification of an emotional state with an accompanying degree of accuracy. For example, emotions such as sadness, happiness, anger and surprise are already able to be identified in acoustic samples using existing solutions. Additional emotions such as envy, nervousness, excited-ness, mirth, fear, disgust, trust and anticipation will also be leveraged as they are developed], … [ … ]
[ ... ] … wherein the state information includes a facial expression, a manner of speaking, a vital sign, and a feeling 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].
Shriberg does not teach [ … ] … a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, 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 [ … ] … a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, 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 (interpreted as state information); Amthor at Para. 0063 teaches According to an example, the at least one scan parameter of the MRI scanner comprises one or more of: duration of scan, duration of scan remaining, current gradient strengths, future gradient strengths, type of contrast, timing parameters, SAR (RF-settings), k-space sampling pattern (scan parameter interpreted as basic information; 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 feeling analyzing process of analyzing the 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 feeling 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 data 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 the feeling 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:
acquiring, by at least one processor, speech data representing a speech picked up, and image data captured during a test performed on a patient [Shriberg at Para. 0240 (see Claim 1 for explanation)];
converting, by the at least one processor, the speech data into utterance data in text format and extracting the utterance data of the patient by speaker recognition on the speech data [Shriberg at Para. 0170 (see Claim 1 for explanation)];
analyzing, by the at least one processor, feelings of the patient in the test based on the image data and the utterance data of the patient [Shriberg at Para. 0292 (see Claim 1 for explanation)];
predicting, by the at least one processor, in a case of a determination that the patient is feeling uneasy [Shriberg at Para. 0328 (see Claim 1 for explanation)], … [ … ]
[ … ] … wherein the state information includes a facial expression, a manner of speaking, a vital sign, and a feeling analysis result of the patient in the test [Shriberg at Para. 0296 (see Claim 1 for explanation)].
Shriberg does not teach [ … ] … a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, 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 outputting, by the at least one processor, the probability of interruption, … [ … ]
Amthor teaches [ … ] … a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, 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 [Amthor at Para. 0058, 00630094 (see Claim 1 for explanation)];
and outputting, by the at least one processor, 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 speech data representing a speech picked up, and image data captured during a test performed on a patient [Shriberg at Para. 0240 (see Claim 1 for explanation)];
an extracting process of converting the speech data into utterance data in text format and extracting the utterance data of the patient by speaker recognition on the speech data [Shriberg at Para. 0170 (see Claim 1 for explanation)];
an analyzing process of analyzing feelings of the patient in the test based on the image data and the utterance data of the patient [Shriberg at Para. 0292 (see Claim 1 for explanation)];
an interruption predicting process of predicting, in a case of a determination that the patient is feeling uneasy [Shriberg at Para. 0328 (see Claim 1 for explanation)], … [ … ]
[ … ] … wherein the state information includes a facial expression, a manner of speaking, a vital sign, and a feeling analysis result of the patient in the test [Shriberg at Para. 0296 (see Claim 1 for explanation)].
Shriberg does not teach [ … ] … a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, 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 teches [ … ] … a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, 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 [Amthor at Para. 0058, 00630094 (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 data, 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.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-9, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
The memorandum issued by the office on August 4, 2025 confirms that the mental process grouping is not without limit. The memorandum further explains that claims reciting an exception should be distinguished from claims that merely involve an exception, which are eligible and do not require further eligibility analysis. The claims do not recite any mathematical concept or mental process such as comparing or categorizing information that can be performed in the human mind. Moreover, the claims do not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. Thus, claims are eligible because they do not recite a judicial exception.
Regarding (a), the Examiner respectfully disagrees. The claims recite certain methods of organizing human activity because the claims recite rules or instructions for a person to follow. The process of acquiring data, converting data, analyzing data, and outputting data represent the abstract idea. Examiner respectfully points to the 101 section of the rejection for full explanation.
The features of "an extracting process of converting the speech data into utterance data in text format and extracting the utterance data of the patient by speaker recognition on the speech data," "an analyzing process of analyzing feelings of the patient in the test based on the image data and the utterance data of the patient," and "an interruption predicting process of predicting, in a case of a determination that the patient is feeling uneasy, a probability of interruption of the test performed on the patient, from the utterance data, state information, and basic information of the test, with use of a prediction model for predicting a probability of interruption of a test" reflect technical improvements in the technical field of test assist apparatus and method. Conventional technology analyzes patient information to identify psychological and social characteristics. However, as discussed in paragraphs 0003-0006, conventional technology does not execute processing for future events such as prediction of procedure interruptions due to emotional factors. Thus, conventional technology does not generate timely outputs, and reduces the effectiveness of automated test assistance during a test. In addition, conventional technology does not handle dynamically changing patient states. To address these problems, the claimed apparatus and method implement machine learning based prediction of test interruption based on patient emotional state and behavior during the procedure. This configuration enables real-time predictive processing, thereby improving system responsiveness and enhancing the efficiency and reliability of test assist operation. Such a technical solution to a technical problem was found to be patent eligible.
Regarding (b), the Examiner respectfully disagrees. There is no physical improvement to the computer recited in the claims, nor would a person having skill in the art recognize that there is an improvement present. Extracting data, analyzing data, and making a prediction based on data is by no means an improvement to the computer. The problem of patient interruption is not a problem caused by the computer (the technological environment). This is, at best, a medical problem. Therefore, there is not technical problem found and therefore, no practical application found.
The claimed invention provides a non-conventional and inventive combination of known elements of the test assist method and system, which constitutes "inventive concept" under Step 2B. The conventional method analyzes patient information to identify psychological and social characteristics without procedure interruption prediction due to emotional factors. In contrast, claims employ a coordinated sequence of data acquisition, feature extraction, emotional state analysis, and machine- learning-based prediction processing which is not taught by the prior art. Taking all the additional elements individually, and in combination, claims as a whole amount to significantly more than the abstract idea. Accordingly, the pending claims should be patentable in view of the additional elements of the pending claims.
Regarding (c), the Examiner respectfully disagrees. Whether or not the claims are taught in the prior has no relevance as to whether the claims provide an “inventive concept” or are non-conventional. The claims equate to mere data gathering and analysis using machine learning, which does not provide a practical application nor inventive concept for reasons pointed out in the 101 section of the rejection.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-9, the Examiner has considered the Applicant' s arguments; however the arguments are not persuasive. Applicant argues:
Applicant respectfully submits that Shriberg and Amthor fail to disclose each and every feature of pending claims 1, 8, and 9, including "predicting a probability of interruption of the test performed on the patient from the utterance data, state information, and basic information of the test, with use of a prediction model for predicting a probability of interruption of a test." As admitted on page 7 of the office action, Shriberg fails to teach this feature. Amthor fails to remedy the deficiency in Shriberg because it merely discloses the inference of the stress level of a patient by implementing a sensor in MRI.
Regarding (a), the Examiner respectfully disagrees. State information and basic information are not defined in the claims and are therefore given their broadest reasonable interpretation. Amthor in combination with Shriberg teaches these features as described in the basis of rejection.. Examiner has updated to rejection to provide further explanation.
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:
FEIWEIER et al (US Publication No. 20150265219) discloses a method and system for adapting a medical system to an object movement during medical examination of the object.
Farinha et al (“Individual and Contextual Variables as Predictors of MRI-Related Perceived Anxiety”) discloses exploring those anxiety predictors and examining the effect of the experience of MRI on PA comparing anxiety pre- to post-MRI.
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 extension fee 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 JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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