DETAILED ACTION
The present office action represents a final action on the merits.
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 .
Priority
This application claims the priority date of foreign application JP2021-065169 of April 7, 2021.
Status of Claims
Claims 1-6 and 8-12 are amended and Claims 1-12 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 1-12 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 1-12 are drawn to an information processing system, which is within the four statutory categories (i.e., machine).
Claims 1-12 recite an information processing system, comprising:
a sensor configured to acquire first biological information of a target biological object:
and
a processor configured to:
process the acquired first biological information of a target biological object;
generate a plurality of pieces of input information with respect to a plurality of machine learning models based on the process of the first biological information;
input each piece of the plurality of pieces of the input information to a respective machine learning model of the plurality of machine learning models, wherein
a first piece of the plurality of pieces of the input information is input to a first machine learning model of the plurality of machine learning models,
a second piece of the plurality of pieces of the input information is input to a second machine learning model of the plurality of machine learning models, and
the first piece of the input information is different from the second piece of the input information;
obtain an estimation result from each machine learning model of the plurality of machine learning models based on the input of the each piece of the plurality of pieces of the input information to the respective machine learning model; and
determine an arousal of the target biological object based on the estimation result.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and mathematical concepts, but for the recitation of generic computer components (e.g., information processing system, sensor, processor, etc.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity and mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-12 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 an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claim 1.
The dependent claims recite additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claim 1.
The additional elements from claim 1 include:
an information processing system comprising (apply it, MPEP 2106.05(f)).
a sensor (apply it, MPEP 2106.05(f)).
a processor (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception – for example, the recitation of “an information processing system”, “a sensor”, and “a processor”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g., see Specification Paragraphs [0005], [0011], [0033]-[0034], [0040], and [0069] (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification Paragraphs [0005], [0011], [0033]-[0034], [0040], and [0069] discloses that the additional elements (i.e., an information processing system, a sensor, a processor) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., a computer);
Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving medication use data, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention receives patient biological information.
Dependent claims 2-12 include other limitations, but none of these functions are deemed significantly more than the abstract idea. Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity or mathematical concepts, using an information processing system, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-12 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Harper (U.S. Pub. No. 2023/0032131 A1) in view of Chappell (U.S. Pub. No. 2020/0296458 A1).
Regarding claim 1, Harper discloses an information processing system, comprising:
a sensor configured to acquire first biological information of a target biological object (Paragraph [0083] discusses a wearable device with a sensor to obtain biometric data of a wearer.); and
process the acquired first biological information of the target biological object (Paragraph [0077] discusses user emotional states and/or mental states are determined from a user wearable device obtaining biometric data from the user and a remote system processes this data to determine one or more emotional states and/or mental states of the user wearing the device.);
generate a plurality of pieces of input information with respect to a plurality of machine learning models based on the process of the first biological information (Paragraphs [0083]-[0084], [0094], and [0101]-[0103] discuss user biometric data, physiological input signals received from wearable device sensors or a secondary device can be transmitted to the remote system for analysis by the computer-based models and/or for training computer-based models, devices are provided with processing capabilities and can process the data collected by the sensors and the processing within the cloud system that can communicate with various devices providing user interfaces including those providing a patient user interface and a clinician user interface.);
input each piece of the plurality of pieces of the input information to a respective machine learning model of the plurality of machine learning models, wherein (Paragraph [0022] discusses the at least one mental state of the user is determined using a computer based model: comprising one or more machine learning algorithms, wherein the one or more machine learning algorithms process any or any combination of: user biometric data; supplementary data; continuous data; speech; and/or text.)
a first piece of the plurality of pieces of the input information is input to a first machine learning model of the plurality of machine learning (Examiner notes that the prior art does not explicitly state “first machine learning” however reference is made to one or more learned models.) (Paragraphs [0023]-[0024], [0038]-[0039], [0042] discuss using of one or more learned models, responses to the dynamic prompts can be used to further train models in order to create a system tailored to determining the mental state of the user based on any one or more of: the user biometric data; the one or more user inputs; the emotional state of the user; the at least one pre-determined mental state of the user and/or the at least one pre-determined emotional state of the user.)
a second piece of the plurality of pieces of the input information is input to a second machine learning model of the plurality of machine learning models, and the first piece of the input information is different from the second piece of the input information (Examiner notes that the prior art does not explicitly state “second machine learning” however reference is made to one or more learned models.) (Paragraphs [0023]-[0024], [0038]-[0039], [0042] discuss using of one or more learned models, responses to the dynamic prompts can be used to further train models in order to create a system tailored to determining the mental state of the user based on any one or more of: the user biometric data; the one or more user inputs; the emotional state of the user; the at least one pre-determined mental state of the user and/or the at least one pre-determined emotional state of the user.):
obtain an estimation result from each machine learning model of the plurality of machine learning models based on the input of the each piece of the plurality of pieces of the input information to the respective machine learning model (Paragraphs [0109]-[0112] discuss user input data, the continuously monitored emotional states from the trained models, the dynamic prompts, and the responses corresponding to the prompts can all be stored in a data store to be provided to a clinical or mental health professional on demand or as set pieces of information (e.g. as a regular report), for example, a process, where input data is pre-processed and a model used to infer mental state information and a prompt is shown to a user on the user device, the pre-processed data is received by a probabilistic model to output a probability distribution over emotional and/or mentals states, the estimated state can be a range of emotional and/or mental states such as, happiness, sadness, surprise, fear, anger, disgust, depression, anxiety, social phobia, OCD, trauma, PTSD, etc.); and
determine an arousal (Examiner interprets arousal to include emotion or mental state.) of the target biological object based on the estimation result (Paragraph [0103] discusses the input data is provided to a trained model that is suitable for processing user data to determine one or more emotional states, the output determined emotional state or states may also have an associated weighting or probability score, or a confidence score, the cloud system carries out background substantially continuous monitoring of user data from the wearable device and using the substantially continuous stream of data from the user's wearable device is substantially constantly assessing the user's mental condition or state using the trained computer-based models.).
Harper does not explicitly disclose:
a processor configured to:
Chappell teaches:
a processor configured to (Paragraph [0007] discusses receiving, by at least one processor, neuro-physiological data from one or more sensors.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Harper to include, a processor configured to, as taught by Chappell, in order to obtain accurate audience response data by rating user engagement. (Chappell Paragraphs [0005]-[0006]).
Regarding claim 2, Harper discloses wherein a first arousal baseline of the first machine learning model is different from a second arousal baseline of the second machine learning model (Paragraphs [0044]-[0050] discuss using probabilistic models can allow the determination of a confidence value for the determined one or more mental states, and various probabilistic models can be used, for example, the model may predict symptoms of depression and/or low mood in a user, however the model confidence in that prediction is below a threshold for the system to take any further action, and so no dynamic prompting of the user will occur and in another example, the model may predict symptoms of depression and/or low mood in a user, and the model confidence is above the threshold for the system to take a further action, thus the system consequently might aim to collect clinically meaningful data from the user (for example, associated thoughts, feelings, and behaviors) in order to gather the most relevant high-quality information for use in a clinical setting.).
Regarding claim 3, Harper discloses wherein the first machine learning model is generated based on a first piece of data (Paragraph [0038] discusses training a computer-based model for determining the mental state of the user based on any one or more of: the user biometric data; the one or more user inputs; the emotional state of the user; the at least one pre-determined mental state of the user and/or the at least one pre-determined emotional state of the user.),
the second machine learning model is generated based on a second piece of data (Examiner interprets response to dynamic prompt and response and the use of one or more models anticipates this limitation.) (Paragraph [0042] discusses determining at least one mental state of the user based on the received user biometric data; and/or (b) on determining the at least one mental state of the user is a predetermined mental state, outputting one or more dynamic prompts for display to the user based on the at least one mental state of the user; comprise using one or more probabilistic models.), and
a first arousal level for acquisition of the first piece of data is different from a second arousal level for acquisition of the second piece of data (Paragraphs [0038]-[0039], [0042]-[0043], [0076]-[0077], and [0090]-[0091] discuss the computer-based models can detect one or more emotion states and/or mental states and changes in physiology, for example, if through analysis of heartbeat dynamics or heart data the user is predicted to be in a state of anxiety and alternatively, the trained model may infer (from any combination of sensor data, user input data or clinical data) that the user is experiencing symptoms of a mental state such as depression.).
Regarding claim 4, Harper discloses wherein optimize conditions for the process of the first biological information based on an action state of the target biological object (Paragraphs [0040], [0049]-[0050], [0075], and [0112] discuss a computer system and extracted input data which is pre-processed and then input into a model and the system consequently might aim to collect clinically meaningful data from the user (for example, associated thoughts, feelings, and behaviors) in order to gather the most relevant high-quality information for use in a clinical setting.).
Harper does not explicitly disclose:
the processor is further configured to.
Chappell teaches:
the processor is further configured to (Paragraph [0007] discusses receiving, by at least one processor, neuro-physiological data from one or more sensors.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Harper to include, the processor is further configured to, as taught by Chappell, in order to obtain accurate audience response data by rating user engagement. (Chappell Paragraphs [0005]-[0006]).
Regarding claim 5, Harper discloses wherein the process of the first biological information comprises one of a normalization process or a standardization process (Paragraphs [0117] and [0126] discuss the pre-processing can be performed on the input to convert it and/or gather it into a suitable format for the trained models/probabilistic models.).
Regarding claim 6, Harper discloses wherein the plurality of machine learning models includes one of a regression model or an identification model (Paragraphs [0042]-[0043] discuss using one or more models including a Bayesian deep neural network incorporating Monte Carlo dropout or variational Bayes methods to approximate a probability distribution over one or more outputs; a hidden Markov model; a Gaussian process; a naïve Bayes classifier; a probabilistic graphical model; a linear discriminant analysis model; a latent variable model; a Gaussian mixture model; a factor analysis model; an independent component analysis model; and/or any other probabilistic machine learning method/technique that generates a probability distribution over its output.).
Regarding claim 7, Harper discloses further comprising the plurality of machine learning models (Paragraphs [0023], [0042], and [0126] discuss one or more probabilistic models and through the use of learned models and/or machine learning approaches/algorithms, complex mental state and/or emotional detection models and/or dynamic prompting models can be generated and refined.).
Regarding claim 8, Harper discloses wherein each of the plurality of machine learning models comprises a model (Paragraphs [0023], [0042], and [0126] discuss one or more probabilistic models and through the use of learned models and/or machine learning approaches/algorithms, complex mental state and/or emotional detection models and/or dynamic prompting models can be generated and refined.), and
the model is generated based on one of normalized information or standardized information of a first feature amount associated with a arousal baseline and arousal information (Paragraphs [0038]-[0039], [0048], and [0117] discuss the pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models/probabilistic models and through the use of learned models and/or machine learning approaches/algorithms, complex mental state and/or emotional detection models and/or dynamic prompting models can be generated and refined, training the model for determining the mental state of the user based on user data. For example, by using a threshold against which the confidence value can be checked, predicted mental state(s) with a confidence value below the threshold can avoid triggering a dynamic prompt to the user to ensure that only higher confidence predictions of mental states are used by the system to prompt the user.).
Regarding claim 9, Harper discloses wherein:
acquire second biological information (Examiner notes that the Specification does not state “second biological information” only “biological information” and Examiner interprets “second biological information as information acquired by a sensor. See Specification Paragraph [0005].) of the target biological object (Paragraph [0083] discusses wearable device is used to obtain biometric data, including, the physiology of the wearer, sleep data, activity data, heart data, heartbeat measurements, location data, and/or skin temperature data.);
acquire the first feature amount based on the second biological information of the target biological object, wherein the second biological information is acquired before the acquisition of the first biological information (Examiner notes that the Specification does not differentiate between “first biological information” and “second biological information”.) (Paragraphs [0083] and [0087] discuss a wearable device is used to obtain biometric data collected by the sensors, including, the physiology of the wearer, sleep data, activity data, heart data, heartbeat measurements, location data, and/or skin temperature data, which can be relevant to understanding both the mental health and general health of patients and dynamic prompts are determined by the computer based models which identify emotional events using the wearable device.);
convert one of a first a value of the first machine learning model, based on the first feature amount and a second feature amount, wherein the first machine learning model is generated based on the second feature amount (Paragraphs [0117] discuss pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models/probabilistic model(s), the pre-processed data is received by a probabilistic model that is used to output a probability distribution over emotional and/or mentals states.); and
generate the first piece of the input information with respect to the first machine learning model based on the conversion of the one of the value of the first machine learning model (Paragraphs [0117]-[0118], [0122] discuss the pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models/probabilistic models, the pre-processed data is received by a probabilistic mode, such as a Bayesian neural network, any type of probabilistic model can be used to output a probability distribution over emotional and/or mentals states, the estimated state can be a range of emotional and/or mental states such as, happiness, sadness, surprise, fear, anger, disgust, depression (using PHQ-9), anxiety (using GAD-7), social phobia (using SPIN), OCD (using OCI-R), trauma (IES-R), PTSD (PCL-5), etc.).
Harper does not explicitly disclose:
the processor is further configured to; and
convert one of a first normalization coefficient or a first standardization coefficient of the first machine learning model, based on the first feature amount and a second feature amount, wherein the first machine learning model is generated based on the second feature amount generate the first piece of the input information with respect to the first machine learning model based on the conversion of the one of the first normalization coefficient or the first standardization coefficient of the first machine learning model.
Chappell teaches:
the processor is further configured to (Paragraph [0007] discusses receiving, by at least one processor, neuro-physiological data from one or more sensors.).
convert one of a first normalization coefficient or a first standardization coefficient of the first machine learning model, based on the first feature amount and a second feature amount, wherein the first machine learning model is generated based on the second feature amount generate the first piece of the input information with respect to the first machine learning model based on the conversion of the one of the first normalization coefficient or the first standardization coefficient of the first machine learning model (Paragraph [0107] discusses baseline sensor data for calibration and expectation sensor data are accessed by a rule-based process for determining calibration and normalization coefficients. The calibration and normalization coefficients are output to downstream rule-based calculation processes, including the emotional power calculation process and emotional power calculation process, the valance calculation process and the arousal calculation process.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Harper to include, the processor is further configured to and convert one of a first normalization coefficient or a first standardization coefficient of the first machine learning model, based on the first feature amount and a second feature amount, wherein the first machine learning model is generated based on the second feature amount generate the first piece of the input information with respect to the first machine learning model based on the conversion of the one of the first normalization coefficient or the first standardization coefficient of the first machine learning model, as taught by Chappell, in order to obtain accurate audience response data by rating user engagement. (Chappell Paragraphs [0005]-[0006]).
Regarding claim 10, Harper discloses wherein derive a first conversion gain (Examiner is interpreting conversion gain as weightings for factors/derived data/raw data.) of the first machine learning model based on the second feature amount (Paragraphs [0018], [0048]-[0051], and [0117] discuss pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models/probabilistic model(s)and a learned algorithm for which weightings for factors/derived data/raw data can be determined for the user biometric data gathered for the user; the model may predict mental state, and by using a threshold against which the confidence value can be checked, predicted mental state(s) with a confidence value below the threshold can avoid triggering a dynamic prompt to the user to ensure that only higher confidence predictions of mental states are used by the system to prompt the user.);
map the first feature amount with the second feature amount based on the first conversion gain (Paragraphs [0017]-[0018] and [0122] discuss the step of determining the at least one mental state of the user comprises determining one or more weightings for the user biometric data and the emotional states from multiple physiological signals can be implemented using machine learning models, the supervised machine learning is concerned with a computer learning one or more rules or functions to map between example inputs and desired outputs as predetermined by an operator or programmer, usually where a data set containing the inputs is labelled.), and
convert the one of the value of the first machine learning model based on the first conversion gain (Paragraphs [0018], [0107], [0117], [0121], [0124], FIGS. 4 and 7 discuss the system, the pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models and the mental state and/or emotional state of the user can be determined using, for example, a learned algorithm for which weightings for factors/derived data/raw data can be determined for the user biometric data gathered or calculated values derived, for the user and through the use of probabilistic models, the dynamic prompts can be dependent on a combination of the inferred emotional and/or mental state, and the confidence score for the inferred/determined state.).
Harper does not explicitly disclose:
the processor is further configured to; and
convert the one of the first normalization coefficient or the first standardization coefficient of the first machine learning model based on the first conversion gain.
Chappell teaches:
the processor is further configured to (Paragraph [0007] discusses receiving, by at least one processor, neuro-physiological data from one or more sensors.); and
convert the one of the first normalization coefficient or the first standardization coefficient of the first machine learning model based on the first conversion gain (Paragraph [0107] discusses baseline sensor data for calibration and expectation sensor data are accessed by a rule-based process for determining calibration and normalization coefficients. The calibration and normalization coefficients are output to downstream rule-based calculation processes, including the emotional power calculation process and emotional power calculation process, the valance calculation process and the arousal calculation process.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Harper to include, the processor is further configured to and convert the one of the first normalization coefficient or the first standardization coefficient of the first machine learning model based on the first conversion gain, as taught by Chappell, in order to obtain accurate audience response data by rating user engagement. (Chappell Paragraphs [0005]-[0006]).
Regarding claim 11, Harper discloses wherein:
convert one of a second value of the second machine learning model based on the first conversion gain and a second conversion gain, wherein (Paragraphs [0018], [0107], [0117], [0121], [0124], FIGS. 4 and 7 discuss the system, the pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models and the mental state and/or emotional state of the user can be determined using, for example, a learned algorithm for which weightings for factors/derived data/raw data can be determined for the user biometric data gathered or calculated values derived, for the user and through the use of probabilistic models, the dynamic prompts can be dependent on a combination of the inferred emotional and/or mental state, and the confidence score for the inferred/determined state.)
the second conversion gain describes a correlation between the second feature amount and a third feature amount (Paragraphs [0017]-[0019] and [0122] discuss the step of determining the at least one mental state of the user comprises determining one or more weightings for the user biometric data and the emotional states from multiple physiological signals can be implemented using machine learning models, the supervised machine learning is concerned with a computer learning one or more rules or functions to map between example inputs and desired outputs as predetermined by an operator or programmer, usually where a data set containing the inputs is labelled; although biometric data includes measurements of various physical properties in relation to the user, data gathered for the user may also include supplementary data which can be used to correlate with the biometric data to more accurately determine the user's mental state and/or emotional state. For example, the biometric data gathered might include heart rate data from a wearable device while the supplementary data might include the current typing speed of the user on another device such as the user's laptop or smartphone.), and
the second machine learning model is generated based on the third feature amount (Paragraph [0023] discusses through the use of learned models and/or machine learning approaches/algorithms, complex mental state and/or emotional detection models and/or dynamic prompting models can be generated and refined using response to the dynamic prompts.); and
generate the second piece of the input information with respect to the second machine learning model based on the conversion of the one of the second value of the second machine learning model (Paragraphs [0018], [0051]-[0055], [0107], [0117], [0121], [0124], FIGS. 4 and 7 discuss the system, the pre-processing can be performed on the input data to convert it and/or gather it into a suitable format for the trained models/probabilistic models, and the mental state and/or emotional state of the user can be determined using, for example, a learned algorithm for which weightings for factors/derived data/raw data can be determined for the user biometric data gathered or calculated values derived, for the user and through the use of probabilistic models, the dynamic prompts can be dependent on a combination of the inferred emotional and/or mental state, and the confidence score for the inferred/determined state. For example, the model may predict symptoms of depression/low mood in a user, however, the confidence score for the output determined by the system is below a threshold for the system to act, and therefore does not send any prompts to the user. However, in some instances, even though the confidence in this emotional and/or mental state prediction is below a threshold, the system may continue to collect clinically meaningful data from the user (e.g. associated thoughts, feelings, and behaviors), which can be requested through the dynamic prompts.; for example, when confidence values for the inferred/determined mental state(s) and/or emotions are low, then a lower weighting or importance can be assigned to that inference and/or any user response data and vice versa where confidence values are high then a higher weighting or importance can be assigned to the same, depending on the model.).
Harper does not explicitly disclose:
the processor is further configured to; and
convert one of a second normalization coefficient or a second standardization coefficient of the second machine learning model, and the input information generation unit generates the input information with respect to the second machine learning model using the normalization coefficient or the standardization coefficient of the second machine learning model obtained by the conversion unit.
Chappell teaches:
the processor is further configured to (Paragraph [0007] discusses receiving, by at least one processor, neuro-physiological data from one or more sensors.); and
convert a normalization coefficient or a standardization coefficient of the second machine learning model, and the input information generation unit generates the input information with respect to the second machine learning model using the normalization coefficient or the standardization coefficient of the second machine learning model obtained by the conversion unit (Paragraph [0107] discusses baseline sensor data for calibration and expectation sensor data are accessed by a rule-based process for determining calibration and normalization coefficients. The calibration and normalization coefficients are output to downstream rule-based calculation processes, including the emotional power calculation process and emotional power calculation process, the valance calculation process and the arousal calculation process.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Harper to include, convert a normalization coefficient or a standardization coefficient of the second machine learning model, and the input information generation unit generates the input information with respect to the second machine learning model using the normalization coefficient or the standardization coefficient of the second machine learning model obtained by the conversion unit, as taught by Chappell, in order to obtain accurate audience response data by rating user engagement. (Chappell Paragraphs [0005]-[0006]).
Regarding claim 12, Harper discloses wherein:
acquire at least one of second biological information or action information of the target biological object (Paragraphs [0083] and [0087] discuss a wearable device is used to obtain biometric data collected by the sensors, including, the physiology of the wearer, sleep data, activity data, heart data, heartbeat measurements, location data, and/or skin temperature data, which can be relevant to understanding both the mental health and general health of patients and dynamic prompts are determined by the computer based models which identify emotional events using the wearable device.); and
select the first machine learning model from the plurality of machine learning models based on the acquired at least one of the second biological information or the action information of the target biological object (Paragraphs [0018]-[0019], [0023], and [0042]-[0044] discuss the system and using one or more models, the mental state and/or emotional state of the user can be determined using, for example, a learned algorithm for which weightings for factors/derived data/raw data can be determined for the user biometric data gathered for the user, and through the use of learned models and/or machine learning approaches/algorithms, responses to the dynamic prompts can be used to further train models in order to create a system tailored to, or more accurate for, each user or groups of users/all users.).
Harper does not explicitly disclose:
the processor is further configured to.
Chappell teaches:
the processor is further configured to (Paragraph [0007] discusses receiving, by at least one processor, neuro-physiological data from one or more sensors.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Harper to include, the processor is further configured to, as taught by Chappell, in order to obtain accurate audience response data by rating user engagement. (Chappell Paragraphs [0005]-[0006]).
Response to Arguments
Applicant’s arguments filed 12/11/2025 have been fully considered.
Interpretation under 35 U.S.C. 112(f):
With respect to the 35 U.S.C. 112(f) rejection, Applicant’s amendment overcomes the prior interpretation and do not invoke interpretation under 35 U.S.C. 112(f).
Claim Rejections under 35 U.S.C. 112:
With respect to the 35 U.S.C. 112 claim rejections, Applicant’s amendment overcomes the previous rejection.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant states, “the features of amended independent claim 1 do not describe an abstract concept, or a concept similar to those found by the Courts to be Abstract, such as a mental process.” (Remarks, page 9). Examiner respectfully disagrees. Here, the information processing system reads a physiological response as a biological signal with a sensor device, and estimates a human emotion using machine learning, which is a method of organizing human activity not a mental process. Applicant states, “claim 1 requires hardware components like a processor and a sensor to execute the claimed features of "acquire biological information of a target biological object, ""generate a plurality of pieces of input information with respect to a plurality of machine learning models based on the process of the biological information," "input each piece of the plurality of pieces of the input information to a respective machine learning model of the plurality of machine learning models,"" obtain an estimation result from each machine learning model of the plurality of machine learning models" (emphases added). A human mind is not equipped to at least acquire biological information, input each piece of the plurality of pieces of the input information to a respective machine learning, and obtain an estimation result from each machine learning model. The claimed subject matter addresses a problem lying in efficiently using machine learning models to determine emotional arousal of a user accurately by generating different pieces of input data for different machine learning models.“ (Remarks, page 10). Examiner respectfully disagrees. The amendments fall short of resulting in an improvement or claiming the specific improvement to the way the computer operates, and are only an improvement to the abstract idea.
Prong Two of Step 2A
While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, claim 1 as written fails to result in a practical application. Applicant argues, “the alleged abstract idea is integrated into a practical implementation. Amended independent claim 1 recites the features of "acquire biological information of a target biological object," "generate a plurality of pieces of input information with respect to a plurality of machine learning models based on the process of the biological information," "input each piece of the plurality of pieces of the input information to a respective machine learning model of the plurality of machine learning models," "obtain an estimation result from each machine learning model of the plurality of machine learning models" (emphases added).” (Remarks, page 10). Examiner respectfully disagrees. “Accurately determine an emotion of the target biological object” is not a technical problem. Here, the application is organizing human activity or mathematical concepts, directed to the abstract idea of accurately determine emotional state of a user by enabling the claimed system to account for variability in user arousal baselines. Applicant states, “amended independent claim 1 describe an unconventional activity implemented using conventional components, which addresses a fundamental problem of accurately determine emotional state of a user by enabling the claimed system to account for variability in user arousal baselines and improving robustness and accuracy compared to a single-model approach. Further, the claimed information processing apparatus ensures that each model receives input tailored to its training baseline. This avoids mismatch between training assumptions and real-world user data which is a practical application of machine learning to biological sensor data” (Remarks, page 12). Examiner respectfully disagrees. The machine learning model is part of the abstract idea. The additional elements in claim 1 include the information processing system, a sensor, and a processor, however, they do not result in a practical application as they are recited at an apply it level, as stated above. Here, the improvement is to the abstract idea. The claims do not improve any technology. All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea.
Rejections under 35 U.S.C. 102:
With respect to claim 1 and the 35 U.S.C. 102 rejection, Applicant’s amendment overcomes the previous 35 U.S.C. 102 rejection.
Rejections under 35 U.S.C. 103:
With respect to claim 1 and the 35 U.S.C. 103 rejection, Applicant’s amendment overcomes the previous 35 U.S.C. 103 rejection. Applicant’s arguments with respect to the amended claim 1 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim amendments. Examiner’s rejection related to the amended claims has been amended.
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.
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686