DETAILED ACTION
Claims 1-10 & 13-14 are currently pending and have been examined.
This action is in response to the amendment filed on 7/6/2026.
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 .
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 1-10 & 13-14 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1-10 & 13-14 are directed to a system (i.e., a machine); Claim 10 is directed to a CRM (i.e., a manufacture). Accordingly, Claims 1-10 & 13-14 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, 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. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 9 includes limitations that recite at least one abstract idea. Specifically, independent claim 9 recites:
9. A medical information processing system, comprising:
a memory storing, in electronic form, voice data of a medical worker;
a first information processing device having first processing circuitry configured to acquire, from the voice data of the medical worker, a state of the medical worker based on a database; and
a second information processing device having second processing circuitry for automatically determining a mental state of the medical worker from the voice data of the medical worker, the voice data relating to the patient, regardless of a content of the voice data of the medical worker, and configured to
electronically retrieve the voice data stored in the memory and perform voice recognition processing on the electronically received voice data of the medical worker during input of the medical information of the patient, the voice data being obtained during a course of a medical practice of the medical worker,
obtain the medical information of the patient based on a result of the voice recognition processing,
input the electronically retrieved voice data into a trained machine- learning model, which, in response, outputs a value indicating the mental state of the medical worker, and
store the value indicating the mental state of the medical worker in a memory,
wherein the memory stores the database,
wherein the first information processing device determines the mental state of the medical worker by using acquired second data based on the database, which is generated by the second information processing device,
wherein the voice data of medical worker is stored in the memory in association with at least one piece of information among:
a place where the medical worker inputs the voice data;
a time zone in which the medical worker inputs the voice data;
a device to which the medical worker inputs the voice data; and
a special input circumstance at a timing when the medical worker inputs the voice data;
the machine-learning model was previously trained using training voice data and target output data;
wherein the second processing circuitry is further configured to input, to the machine- learning model, the at least one piece of information among the place where the medical worker inputs the voice data, the time zone in which the medical worker inputs the voice data, the device to which the medical worker inputs the voice data, and the special input circumstance; and
the value output by the machine-learning model is information of at least one of a pitch of voice, a tone, an utterance speed, or a magnitude of voice of the medical worker.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because acquiring voice data of a medical worker, analyzing the voice data to determine a mental state of the medical worker, storing the mental state and voice information are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). 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 “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Accordingly, independent claim 9 and analogous independent claims 1 & 10 recite at least one abstract idea.
Furthermore, dependent claims 2-8 & 13-14 further narrow the abstract idea described in the independent claims. Claim 2 recites storing various user data, Claim 3 recites the state of the user, Claim 4 recites storing a result, Claim 5 recites performing the steps on a second user, Claims 6-7 recite using a threshold and temporal tendency to determine user state, Claim 8 recites outputting reference information. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 9 and analogous independent claims 1 & 10, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), 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.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
9. A medical information processing system, comprising:
a memory storing, in electronic form, voice data of a medical worker;
a first information processing device having first processing circuitry configured to acquire, from the voice data of the medical worker, a state of the medical worker based on a database; and
a second information processing device having second processing circuitry for automatically determining a mental state of the medical worker from the voice data of the medical worker, the voice data relating to the patient, regardless of a content of the voice data of the medical worker, and configured to
electronically retrieve the voice data stored in the memory and perform voice recognition processing on the electronically received voice data of the medical worker during input of the medical information of the patient, the voice data being obtained during a course of a medical practice of the medical worker,
obtain the medical information of the patient based on a result of the voice recognition processing,
input the electronically retrieved voice data into a trained machine- learning model, which, in response, outputs a value indicating the mental state of the medical worker, and
store the value indicating the mental state of the medical worker in a memory,
wherein the memory stores the database,
wherein the first information processing device determines the mental state of the medical worker by using acquired second data based on the database, which is generated by the second information processing device,
wherein the voice data of medical worker is stored in the memory in association with at least one piece of information among:
a place where the medical worker inputs the voice data;
a time zone in which the medical worker inputs the voice data;
a device to which the medical worker inputs the voice data; and
a special input circumstance at a timing when the medical worker inputs the voice data;
the machine-learning model was previously trained using training voice data and target output data;
wherein the second processing circuitry is further configured to input, to the machine- learning model, the at least one piece of information among the place where the medical worker inputs the voice data, the time zone in which the medical worker inputs the voice data, the device to which the medical worker inputs the voice data, and the special input circumstance; and
the value output by the machine-learning model is information of at least one of a pitch of voice, a tone, an utterance speed, or a magnitude of voice of the medical worker.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the first and second information processing device, circuitry, storage circuitry, memory, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of performing voice recognition to analyzed a voice of a user, the Examiner submits that these limitations amount to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding the additional limitations of inputting voice data into a trained machine-learning model which outputs a value indicating the mental state of the medical worker; the machine-learning model was previously trained using voice and output data, inputting other data into the machine-learning model and receiving voice information output by the machine-learning model, the Examiner submits that these limitations amount to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
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 with the abstract idea, 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 effect a particular treatment or prophylaxis for a disease or medical condition, 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 does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 9 and analogous independent claim 1 & 10 do not recite additional elements that integrate the judicial exception into a practical application.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Dependent claim 13 recites performing learning for classifying the mental state of a user and generating a database; however this limitation amounts to no more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (see MPEP 2106.07(a)).
Dependent claim 14 recites an input interface including microphone to receive voice data; however this limitation merely represent insignificant extra-solution activity (e.g., receiving and transmitting data)(see MPEP § 2106.05(g)) and conventional activities as they merely consist of receiving and transmitting data over a network (see MPEP § 2106.05(d)(II)).
Accordingly, the claims recite at least one abstract idea.
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 9 does 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, regarding the additional limitations of the first and second information processing device, circuitry, storage circuitry, memory, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitations of performing voice recognition to analyzed a voice of a user, the Examiner submits that these limitations amount to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Regarding the additional limitations of inputting voice data into a trained machine-learning model which outputs a value indicating the mental state of the medical worker; the machine-learning model was previously trained using voice and output data, inputting other data into the machine-learning model and receiving voice information output by the machine-learning model, the Examiner submits that these limitations amount to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The dependent claims also 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 dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-10 & 13-14 are ineligible under 35 USC §101.
Prior Art Rejection
All of the cited references fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims. In particular, the cited prior art of record fails to expressly teach or suggest the combination of: a first information processing device having first processing circuitry configured to acquire, from acquired voice data of a medical worker, a state of the medical worker based on a database; a second information processing device having second processing circuitry for automatically determining a mental state of the medical worker from the voice data of the medical worker, the voice data relating to the patient, regardless of a content of the voice data of the medical worker, configured to perform voice recognition processing on the voice data of the medical worker during input of the medical information of the patient during a course of a medical practice of the medical worker, obtain the medical information of the patient based on a result of the voice recognition processing, determine the mental state of the medical worker by analyzing the voice data on which the voice recognition was performed, irrespective of the content of the voice data, and store the determined mental state of the medical worker in a memory; and
storage circuitry that stores the database, wherein the first information processing device acquires the state of the user by using acquired second data based on the database, which is generated by the second information processing device
The most relevant prior art of record includes:
Shriberg (US20210110894) teaches to systems and methods for assessing a mental state of a subject in a single session or over multiple different sessions, using for example an automated module to present and/or formulate at least one query based in part on one or more target mental states to be assessed. Jaitly (US20210090724) teaches to obtaining an input acoustic sequence, the input acoustic sequence representing one or more utterances; processing the input acoustic sequence using a speech recognition model to generate a transcription of the input acoustic sequence, wherein the speech recognition model comprises a domain-specific language model; and providing the generated transcription of the input acoustic sequence as input to a domain-specific predictive model to generate structured text content that is derived from the transcription of the input acoustic sequence. Khanzada (US20230411008) teaches to a process, including: obtaining data from a sensor or user-interface of a mobile computing device gathered during use of the mobile computing device by a user; inferring, from the data, with a trained machine learning model, a mental-health state of the user; and storing the mental health state in memory.
Response to Arguments
Applicant’s arguments on pages 9-14 regarding claims 1-10 & 13-14 being rejected under 35 USC § 101 have been fully considered but they are not persuasive. Applicant claims that:
The claims are not directed to a method of organizing human activity.
The Examiner, however, asserts that Applicant has made conclusory statements regarding how the recited steps amount to an abstract idea. The Examiner argues that the present claims are similar to the example found in In re Meyer - a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – see MPEP 2106.04(a)(2)(II)(C). Here, the present claims encompass a person acquiring voice data from a medical worker and using rules to determine a mental state of the medical worker based on voice data and other information regarding the medical worker. 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.
The claims recite a specific technical solution to a specific technical problem of determining a mental state of a medical worker.
The Examiner, however, asserts that the Applicant has not provided any description as to how the claims are directed to an improvement in the functioning of computers or a technical field. The Examiner notes that the Applicant’s Specification recites, in part, “…there is no time to sufficiently acquire information relating to the medical worker satisfaction, stress and the like.” [¶5 of Applicant’s Specification]. Moreover, it is respectfully submitted that the Applicant’s claims are not directed to an improvement to the functioning of a computer itself but an improvement to the abstract idea; and the claims require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. In other words, it is respectfully submitted that the focus of the Applicant’s claims is not on any improvement in computers as tools, but on certain independently abstract ideas that use computers as tools
None of the steps recited in Claim 1 can be practically performed in the human mind – such as electronically retrieving voice data stored in the memory.
The Examiner, however, argues that the memory limitation was considered an additional element and not part of the abstract idea as stated above in the 101 rejection.
The claims are similar to the Desjardin case by providing an improvement to the machine-learning model.
The Examiner, however, asserts that Desjardins decision cited to specific recitations of a technical problem and a technical solution where the Specification recited that the claimed improvement allows artificial intelligence (AI) systems to ‘us[e] less of their storage capacity’ and enables ‘reduced system complexity.’… The same cannot be said here. The instant application fails to provide any description of a technical problem and a technical solution. Applicant’s specification presents a non-technical problem - , “…there is no time to sufficiently acquire information relating to the medical worker satisfaction, stress and the like.” [¶5 of Applicant’s Specification]. Merely reciting applying a machine learning algorithm to solve a problem does not result in patent eligibility under Step 2A Prong 2. The instant claims and instant disclosure do not attempt to improve on or invent a new machine learning algorithm, but instead employs a machine learning model recited at a high level to solve a problem with the particular judicial exception of mental state determination of a worker using voice and other data. The solution to the problem is rooted in an improvement to the abstract idea itself and not a technical failure of a computer system. The additional elements can best be characterized as tools to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the ""Other examples., v."")."
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Omiya (US20230113656) teaches to a pathological condition analysis system using voice, the pathological condition analysis system allowing anyone to perform measurement and estimate a disease anywhere, in a short time, non-invasively, and without being known to others.
Kim (US20230352194) teaches to A query module is configured to audibly question a user from an electronic display screen and/or a speaker of a computing device with one or more open ended questions. A response module is configured to receive a conversational verbal response of a user from a microphone of a computing device in response to one or more open ended questions. A detection module is configured to provide a machine learning assessment for a user of a medical condition based on a machine learning analysis of a received conversational verbal response of the user.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/ Primary Examiner, Art Unit 3619
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