DETAILED ACTION
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 .
Response to Arguments
Applicant's amendment and arguments regarding the 35 USC 101 and 112 rejections filed 1/21/2026 have been fully considered but they are not persuasive.
Regarding the 35 USC 101 rejection, Applicant argues “Claim 1 defines a specific technological implementation—a learning device with at least one processor configured to perform machine learning on time-series physiological signals measured by a wearable medical sensor. This ties the invention to tangible data sources and hardware interaction, rather than a mathematical idea.” The Examiner respectfully disagrees. As noted in the Non-Final Rejection, the structural aspects of memory, processor and sensed signals are additional element sunder Step 2A, Prong 1 that do not amount to integration of the abstract idea into a practical application and that do not amount to significantly more than the abstract idea itself under step 2B. The memory and processor are generic computer structure for generic computer implementation of the abstract idea. The data collected by sensors amounts to insignificant, extra-solution activity (mere data gathering). The recitation of a machine learning model trained on data does not overcome the rejection in that the model is generically claimed and machine learning models by definition are computer implementation of learning processes which are mental concepts performable by a human. A trained clinician could look at patient data and make assessments based on comparisons to patient population data.
Regarding the 35 USC 112(a) rejection of claims 1-13, Applicant does not provide any particular arguments and cites paragraphs stating the specification “conveys[s] with reasonable clarity to those skilled in the art that, as of the filing date sought, Applicant was in possession of the recitations of claims 1-13”. The Examiner respectfully disagrees. The rejection is maintained for the reasons set forth in the Non-Final Rejection, especially in view of the indefinite nature of the term “agitation”.
Applicant’s arguments regarding the 35 USC 112(b) rejection for the term “agitation” have been considered but are not persuasive. The limitation “determined agitation index for the target patient exceeds a predetermined threshold” does not add any clarity as it is unclear what the threshold value is that Applicant asserts distinguishes agitation from non-agitation. Without a clear understanding of what Applicant deems “agitation”, any determination of a “chance of becoming agitated” is extremely nebulous and unclear as well. The rejection is maintained.
Applicant’s amendment and arguments filed 1/21/2026, with respect to the 35 USC 102 and 103 rejections (other than that of Fornwalt and Kennedy) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kitahara Medical Strategies International Co Ltd. (JPWO2019044619A1), herein Kitahara.
The Examiner notes the 35 USC 103 rejection of Claims 1 and 10 as being unpatentable over Fornwalt et al. (2021/0076960) in view of Kennedy et al. have already addressed the amended limitations and therefore that rejection stands. Applicant has not provided any specific arguments with respect to this particular combination.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 11 and 13 recite products and Claim 12 recites a process.
Step 2A, Prong 1
Claim 1 recites the step of determining whether a target as become agitated based on patient biometric information and non-patient biometric information; and generating an alert when a determined agitation index exceeds a threshold. This limitation is broadly claimed and is capable of being performed in the human mind. A skilled clinician could look at patient data; compare it to data from a normal population or patient; determine a variance; indicate agitation and issue a voice command/alert if an agitation threshold is exceeded.
Additionally, generating an agitation determination model is a mathematical concept. Model generation amounts to establishing mathematical correlations between inputs and outputs to predict an outcome.
Claims 11, 12 and 13 recite the same limitations. Therefore, the claims recite both a mental process and mathematical concept abstract idea.
Step 2A, Prong 2
Claims 1 and 11-13 do not recite any additional elements that amount to integration of the abstract idea into a practical application.
Claim 1 recites a memory; at least one processor; acquiring patient and non-patient biometric information such as heart-rate, respiration, body motion, age, sex, etc. The memory and processor are recited at a high level of generality and amount to generic computer components for performing generic computer functions. Acquiring the data amounts to the insignificant extra-solution activity of data gathering. Generic computer implementation and insignificant extra-solution activity do not amount to integration of the abstract idea into a practical application.
Claim 11 does not include any additional elements.
Claims 12 only includes the additional element of acquiring biometric information which, like claim 1, is insignificant extra-solution activity.
Claim 13 includes generic computer structure (non-transitory recording medium) and acquiring biometric information which, like claim 1, is insignificant extra-solution activity and
Step 2B
Claims 1 and 11-13 do not recite any additional elements that, alone or in combination, amount to significantly more than the abstract idea itself.
Claim 1 recites a memory; at least one processor; acquiring patient and non-patient biometric information such as heart-rate, respiration, body motion, age, sex, etc. The memory and processor are recited at a high level of generality and amount to generic computer components for performing generic computer functions. Acquiring the data amounts to the insignificant extra-solution activity of data gathering. Generic computer implementation and insignificant extra-solution activity do not amount to significantly more than the abstract idea itself.
Claim 11 does not include any additional elements.
Claims 12 only includes the additional element of acquiring biometric information which, like claim 1, is insignificant extra-solution activity.
Claim 13 includes generic computer structure (non-transitory recording medium) and acquiring biometric information which, like claim 1, is insignificant extra-solution activity and
Claims 2-10 only recite further details regarding the mathematical concept of model generation.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding compute-implemented functional limitations, MPEP §2161.01 states:
“Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”
Applicant’s claims 1, 11, 12 and 13 require the computer-implemented steps of generating an agitation model and determining, with the model, when a patient has become agitated. Applicant’s claims and specification only broadly repeat this function without any particular steps indicating how the model is generated or how the decisions are made by the model. Par. [0031-0033] broadly defines the types of machine learning algorithms that can be used and broadly recite a supervised learning process (feeding the model labeled training data) but does not detail any particular steps on how “agitation” is distinguished from a non-agitated state (see 112b rejection below for more details regarding this term) or what metrics are particularly used to determine agitation. The model is essentially a black box model that has no clear steps for making the claimed functional predictions. The claims therefore lack adequate written description support.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “in agitation” in Claims 1 and 11-13 is a relative term which renders the claim indefinite. The term “in agitation” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specifically, while par. [0018] of the specification states: “The agitated state indicates a state in which the patient is uneasy. The agitated state may include a state in which his/her mind cannot be normally controlled. In addition, the agitated state may include a state caused by delirium of the patient”, the Examiner notes the relative term of “in agitation” is modified by other relative terms: “uneasy”, “mind cannot be normally controlled” and “delirium”. All of these terms are subjective determinations and not a firm, clinical diagnosis defined by a clear standard. Identifying a state of uneasiness is up to the opinion of an observer. Applicant has not defined a standard for what is or is not uneasy. Same with a “normally controlled” mind and what is or is not a state of delirium.
Claim 8 states “the non-patient is a person whose chance of becoming agitated is equal to or less than a predetermined probability”. This limitation is unclear since the step of determining probability is not positively recited and it is unclear how one can predict a future chance of some form of agitation with any certainty. The broadest reasonable interpretation of “agitated” is any state that is not a rets state. Every human would therefore appear to have a 100% chance of becoming agitated at some point in the future. The meets and bounds of this limitation are ultimately unclear.
Claim 9 includes the limitations “daily life activities”. It is unclear what activities this encompasses and to what extent a patient must be able to perform them to be considered performing daily life activities. This is an overly broad term without clear metes and bounds.
Claim 11 was amended to add “at least one processor” but the steps performed by this new processor have already been associated with “at least one processor” of Claim 1, from which Claim 11 depends. It is unclear if Applicant is intending to refer to the previous processor or claim a new processor. Appropriate clarification and/or correction is respectfully requested.
Claims 2-7 and 10 are rejected as being dependent on indefinite claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 6, 7 and 11-14 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Kitahara Medical Strategies International Co Ltd. (JPWO2019044619A1), herein Kitahara.
Regarding Claims 1, 2, 4, 6, 7 and 11-13, Kitahara discloses a device having memory and a processor (p. 5 of the translation, “In FIG. 1, for convenience of explanation, the feature amount calculation unit 11 and the unrest detection unit 12 are described separately, but the feature amount calculation unit 11 and the unrest detection unit 12 individually perform the above-described processing. It may be configured by a single processor operated by a computer program that performs the above processing.”), wherein the processor acquires patient biometric information (such as heart rate, respiration, body-motion, see p. 5 of the translation “Here, the "biological information" is information about the living body obtained by a sensor or the like. Further, the "biological information" is, for example, biosensed data (vital signs). Specifically, "biological information" includes heart rate (pulse), respiration, blood pressure, core body temperature, consciousness level, skin body temperature, skin conductance response (Galvanic Skin Response (GSR)), skin potential, myoelectric potential, and electrocardiographic waveform. , Brain wave waveform, sweat volume, blood oxygen saturation, pulse wave waveform, photobrain function mapping (Near-infrared Spectroscopy (NIRS)), urine volume, and at least one biometric information such as pupillary reflex. , Not limited to these.”) and non-biometric information (such as age and sex among a plurality of patients which are items on electronic medical records, see pp. 11-12 of the translation, “A1. Check items on the regular behavioral assessment sheet in the nursing record (part of the electronic medical record).”; “These additional information are selectively or combined and given to the restless state identification unit 42. That is, the additional information given to the restless identification unit 42 does not have to include all of the patient's response record information, the drug administration record information, the weight information, and the age information, but may include at least one. In addition, the additional information may include additional information (information on patients admitted to the same room, body temperature, vocalization, etc.) related to the above A1 to A7 other than the information recorded in the recording units 401 to 405.”); trains a machine learning model to account for those attributes of the biometric and non-biometric information (the model uses the information to make predictions and it can only account for information it has been trained on); and produces an indication and alert of agitation (e.g. unrest, restlessness, delirium, etc., see p. 12 of the translation “The restless state identification unit 42 calculates the current restlessness score based on not only the calculation result from the heart rate section variability calculation unit 41 but also the additional information from the recording units 401 to 405, and the current restlessness / non-restless state. Outputs a disturbing state notification signal indicating. In this case, the restless state identification unit 42 receives not only the time-series data Y (t) for the detection process related to the heartbeat but also the time-series data for the detection process related to the additional information as the feature vector, and these feature vectors and the past The current restlessness score is calculated based on pre-learned discriminative parameters based on restlessness / non-restlessness data.”).
In regard to Claim 14, Kitahara discloses displaying an image to a caregiver indicating the state of agitation (pp. 12-13 of the translation, “In addition, when the disturbing score from the disturbing state identification unit 42 notifies that the target patient is in a disturbing state, the image of the target patient is displayed on the monitor of the mobile terminal owned by the nurse / caregiver or the monitor in the nurse station. If shown, the nursing / caregiver can visually confirm the state of the target patient. Therefore, even if the restless state notification signal is erroneously output from the restless state identification unit 42, the nurse / caregiver can take measures for the target patient after visual confirmation. Therefore, the work load of the nurse / caregiver can be further reduced.”)
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.
Claims 1-4, 6-9 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Van Wolferen (PGPUB 2021/0316098) in view of Kitahara Medical Strategies International Co Ltd. (JPWO2019044619A1), herein Kitahara.
Regarding Claims 1, 2 and 11-13, Van Wolferen discloses a system for predicting/detecting agitation comprising memory and a processor (par. [0038]) for executing a trained algorithm (model) that is trained on non-patient biometric information (such as from raw data of intubated patients, see par. [0038]) as well as patient biometric information (motion data from the patient’s own endotracheal tube (par. [0040]). Van Wolferen is silent regarding using age and sex information from the patient and other patients to create the machine learning model. However, in the same field of endeavor of determining agitation states, Kitahara discloses utilizing additional non-biometric information from the patient and groups of patients to establish an agitation model for the purpose of more accurately accounting for conditions that arise due to multiple, disparate factors (pp. 10-11 of the translation, “The bio-information processing system 100B according to the second embodiment of the present invention will be described with reference to FIG. According to the observations of the present inventors, it has been found that the problem behavior of the patient is caused by various factors. For example, a patient often wants to return home when he / she meets a person who has lived with him / her within 24 hours, and the patient may behave problematic due to the visit. That is, the patient's problem behavior does not occur only due to the information described in the electronic medical record. In this way, it may be possible to predict the occurrence of problem behavior of a patient by using information that is not described in the electronic medical record, for example, information indicating the presence or absence of a visit. Furthermore, by combining the above-mentioned information (information about heartbeat, information written in an electronic medical record, and information indicating the presence or absence of a visit) with the following additional information (A1 to A7), the occurrence of problem behavior of the patient is predicted. You may be able to do it.”). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Van Wolferen reference to include age and sex data (electronic medical record data) from the patient and a group of patients, as taught and suggested by Kitahara, for the purpose of more accurately accounting for conditions that arise due to multiple, disparate factors.
In regards to Claims 3, 4 and 6, Van Wolferen discloses obtaining raw data that is labeled comfort and raw data that is labeled discomfort wherein the raw data labeled comfort would relate to a resting state of a patient with an endotracheal tube and the data labeled discomfort would be an agitated state (par. [0038]).
Regarding Claim 7, Van Wolferen discloses training the AI with motion data similarly to that of the raw data from other paints, which would involve labeling comfort/discomfort events with the motion data much like with the raw data from other patients (par. [0040]).
In regard to Claim 8, the Examiner notes any paint with an endotracheal tube will be considered as a person whose chance of becoming agitated is above a threshold probability. Hence the need to monitor for agitation.
Regarding Claim 9, the Examiner is interpreting “daily life activities” as actions such as a beating heart, volitional arm movement, etc. Van Wolferen discloses the patient’s using the predictive system are those with ventilators (no mention of cardiac augmentation is made in the disclosure) and thus it is presumed the patient’s heart is beating on its own. The disclosure also indicates the patients would be capable of self-extubation meaning they could move their arms on their own (par. [0040]).
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Yamaoka et al. (2019/0059802) in view of Kitahara Medical Strategies International Co Ltd. (JPWO2019044619A1), herein Kitahara. Yamaoka discloses utilizing a predictive algorithm to determine abnormal cognitive states, see par. [0037-0039] (in agitation as defined by Applicant in par. [0018] of the specification, “The agitated state may include a state in which his/her mind cannot be normally controlled”. The algorithm is trained on physical condition data and sleep state data of the user/patient (par. [0090]). Yamaoka is silent regarding the use of data from people other than the patient. However, in the same field of endeavor of determining agitation states, Kitahara discloses utilizing additional non-biometric information from the patient and groups of patients to establish an agitation model for the purpose of more accurately accounting for conditions that arise due to multiple, disparate factors (pp. 10-11 of the translation, “The bio-information processing system 100B according to the second embodiment of the present invention will be described with reference to FIG. According to the observations of the present inventors, it has been found that the problem behavior of the patient is caused by various factors. For example, a patient often wants to return home when he / she meets a person who has lived with him / her within 24 hours, and the patient may behave problematic due to the visit. That is, the patient's problem behavior does not occur only due to the information described in the electronic medical record. In this way, it may be possible to predict the occurrence of problem behavior of a patient by using information that is not described in the electronic medical record, for example, information indicating the presence or absence of a visit. Furthermore, by combining the above-mentioned information (information about heartbeat, information written in an electronic medical record, and information indicating the presence or absence of a visit) with the following additional information (A1 to A7), the occurrence of problem behavior of the patient is predicted. You may be able to do it.”). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Van Wolferen reference to include age and sex data (electronic medical record data) from the patient and a group of patients, as taught and suggested by Kitahara, for the purpose of more accurately accounting for conditions that arise due to multiple, disparate factors.
Claims 1 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Fornwalt et al. (2021/0076960) in view of Kennedy et al. (2021/0145369). Fornwalt discloses utilizing a machine learning algorithm to predict atrial fibrillation (a patient experience afib would be considered “uneasy”) wherein the algorithm is trained with patient data including ECG traces, age and sex (par. [0053, 0140]). Fornwalt fails to disclose including patient data in the training. However, in the same field of endeavor of using predictive models to make biological assessments of living creatures, Kennedy discloses in par. [0038], “In some embodiments, neural network technology may be employed in determining the neuro-cognitive and physiological condition of the animal. For example, a neural network may be trained with data collected from the animal, with data collected from other animals, or a combination thereof. In embodiments that employ data collected from other animals, the data may be limited to data collected from animals that are similar in various aspects to the animal being monitored. For example, the training data may be limited to animals of the same species, breed, age, geographic location, and the like. In this way, individual animal differences can be compared with population means and variances to provide assessment tools for various applications. In some embodiments, the measurements of other animals may be used as population mean or “group normal” values for comparison and evaluative purposes. In some embodiments, the other animals may be chosen to be similar in one or more aspects to “the subject” animal.” Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Yamaoka reference to include relevant data from both the patient and other people, as taught and suggested by Kennedy, for the purpose of establishing norms that can be used to fine-tune the analysis.
Conclusion
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 ALLEN PORTER whose telephone number is (571)270-5419. The examiner can normally be reached Mon - Fri 9:00-6:00 EST.
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/ALLEN PORTER/Primary Examiner, Art Unit 3796
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