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 .
Claim Objections
Claim 13 is objected to because of the following informalities:
i. Regarding Claim 13, the phrase “captured by a the camera” should read “captured by a camera”.
Appropriate correction is required.
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 1-28 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 Claims 1-28, the teaching of the machine learning system and all associated training/detections components listed in the dependent claims are not described in the Specification in a manner that would reasonably convey that Applicant had possession of the claimed invention. While Applicant lists throughout the Specification that the various forms of data (facial tracking, audio tracking, device usage tracking) can be used to track various aspects of a person’s mental health, there is no disclosure of how these components are used or combined in a machine learning device. While inputs are described, how the associated outputs, or even what the outputs are outside of “a mental health state of the user” and “interventions for the user” (Para. 0020), it is unclear how Applicant is getting to these end results, i.e. there is no mention of how data is combined and/or weighted to create the output/specific results of the output. Any algorithm/machine learning program claimed must be described in sufficient detail so one of ordinary skill in the art is capable of using the invention. The Applicant’s Specification and Claims merely define the invention in functional language that states the desired result, but does not describe in detail as to how the result is achieved with the given inputs (see MPEP 2161.01). For these reasons, Claims 1-28 are rejected under U.S.C. 112(a).
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 1-28 are rejected under 35 U.S.C. 101 because claims are directed toward an abstract idea without significantly more.
Step 1: Independent Claims 1 and 28 are directed to a non-transitory computer storage media and method of use. Thus, they are directed to statutory categories of invention.
Step 2a, Prong 1:
Claims 1 and 28 recite the following limitations:
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
storing the mental health state
These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind, i.e., using pen and paper. With a plurality of measurements for each physiological variable of a patient, a human could reasonably make a determination of a mental health state of a user, and keep the information stored on the paper. The obtaining of the data does not require any component, just that the data was collected by a sensor or user interface, so one could merely read the sensor read-out data to meet the claim language.
Step 2A, Prong 2:
Claims 1 and 28 recite the following additional elements:
a tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
a trained machine learning model
memory
The recitation of one or more processors, machine learning model and computer storage media with computer-usable instructions that, when executed by the one or more processors, implement a method are merely reciting both the processors and computer storage media at a high-level of generality, and the computer readable storage media merely instructs the processors to carry out the steps of the method. In other words, the computer components are being used as a tool to carry out the method (See MPEP 2106.05(f)).
Thus, the abstract idea is not integrated into a practical application. The combination of these additional elements is no more than insignificant extra solution activity, and mere instructions to apply the exception using generic computer components (the processors and computer readable storage media). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept.
Further, the broad recitation of machine learning, without specification of inputs, outputs, training information, weighting of inputs, etc., is reciting the machine learning at a broad level that is merely well understood, routine, and conventional (see MPEP 2106.05(d)), and without the complexity that prevents a person from performing the limitations on pen and paper.
For these reasons, there is no inventive concept. The claim is not patent eligible. Even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent Claims:
Claims 2-27 recite limitations that further define the type of data collected, and are merely limiting the physiological variables to a particular fields of use, without providing the complexity of training/processing necessary to amount to more than an abstract idea.
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.
Claims 1-3, 6-7, 9, 11, 16-23, and 25-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Publication 20210177347 awarded to Wild et al.
Regarding Claims 1 and 28, Wild teaches a tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations (Para. 0076) and a method comprising: obtaining data from a sensor (Para. 0108) or user-interface of a mobile computing device gathered during use of the mobile computing device by a user (Para. 0140); inferring, from the data, with a trained machine learning model, a mental-health state of the user (Para. 0087, “The stress profiler 10 can include a learning function, which recognizes patterns of stress information associated with previous periods of stress. Over time, the learning function progressively improves the accuracy and speed of stress profiling for a user”); and storing the mental health state in memory (Para. 0075).
Regarding Claim 2, Wild teaches the medium of claim 1, wherein: the data includes a behavioral pattern or digital biomarker of the user (Para. 0086, “In one embodiment, the stress profiler 10 processes psychometric, physiological and behavioural information. In another embodiment, the stress profiler 10 processes all four of the types of stress information (psychometric, physiological and behavioural, and cognitive function)”); and at least some of the data is gathered responsive to the user granting permission to do so (Para. 0176, “The stress profiler 10 first requests permission from the user to collect behavioural information, and then routinely collects the information in the background without interrupting the user”).
Regarding Claim 3, Wild teaches the medium of claim 1, wherein the data include: usage of an application installed on the mobile computing device (Para. 0114, “phone usage” and “(‘app’) usage”); typing and touchscreen input variability of the user on the mobile computing device and scrolling behavior of the user on the mobile computing device (Para. 0114, “Other inputs can be measured such as, keystroke rate, cadence, typing style, pressure or force detection (keypad, trackpad, screen pressure sensor)”).
Regarding Claim 6, Wild teaches the medium of claim 1, wherein: the data indicates which applications installed on the mobile computing device are used and amounts of usage of each such application used (Para. 0114); and the machine learning model is responsive to changes in relative amounts of usage of different categories of applications (Para. 0113, “Examples of different measurements or behavioural observations which may be used to provide behavioural information include eye movement patterns, social interactions, the types of websites visited, the types of apps used, the news topics read, spending behaviour, food choices, social outings, taking holidays, and so on”), the different categories including productivity applications (Para. 0292, “software which uses the camera to detect the direction and speed of eye movements, and determines the time spent on certain ‘news articles’ and reading tasks;”) and social media applications (Para. 0293, “software which analyses Internet search history, app usage, key word dominance when within particular apps or websites such as social media”).
Regarding Claim 7, Wild teaches the medium of claim 1, wherein: the data includes speed, frequency, and direction of scrolling, by the user, of one or more user interfaces displayed by the mobile computing device (Para. 0114, “Other inputs can be measured such as, keystroke rate, cadence, typing style, pressure or force detection (keypad, trackpad, screen pressure sensor)”); and the machine learning model is configured to detect anomalous patterns in the data indicative of the mental-health state of the user (Para. 0113).
Regarding Claim 9, Wild teaches the medium of claim 1, wherein: the machine learning model is configured to infer the mental-health state of the user based on patterns of usage throughout a 24-hour cycle (Para. 0321), indicative of sleep disturbances or disruptions (Para. 0121).
Regarding Claim 11, Wild teaches the medium of claim 1, wherein: the data includes an image of a face or body of the user captured by a camera of the mobile computing device and the machine-learning model comprises a computer vision model configured to infer the mental-health state of the user based on facial expression analysis of the image (Paras. 0277-0280, “The physiological information collection tool comprises software which controls the device's camera to image the user's face and thereby detect: [0278] pulse; [0279] skin colour and circulation; [0280] facial expression”).
Regarding Claim 16, Wild teaches the medium of claim 1, wherein: the data comprise responses obtained by prompting the user to provide self-reported mood states or by inferring mood states of the user over time (Para. 0096).
Regarding Claim 17, Wild teaches the medium of claim 16, wherein: the machine learning model is configured to infer triggers of, or patterns in, changes in the mental health state based on the data (Para. 0087-0088).
Regarding Claim 18, Wild teaches the medium of claim 16, wherein: the data comprises inferred mood states (Para. 0087-0089); and the mood states are inferred based on user location (Para. 0089), activity level and social interactions (Para. 0055), or physiological measurements of the user (Para. 0088).
Regarding Claim 19, Wild teaches the medium of claim 18, wherein: the mood states are inferred based on user location (Para. 0089), activity level and social interactions (Para. 0055), or physiological measurements of the user and the physiological measurements of the user include heart rate (Para. 0029) and sleep quality (Para. 0121).
Regarding Claim 20, Wild teaches the medium of claim 16, wherein the operations comprise: recommending an intervention to the user based on inferences from the machine learning model (Para. 0102).
Regarding Claim 21, Wild teaches the medium of claim 1, wherein: the data comprises multiple channels of data from multiple sensors of the mobile computing device, the sensors including an inertial measurement device having three or more axes (Para. 0114, “gyroscope”), a geolocation sensor (Para. 0114, “GPS”), a microphone (Para. 0114, “smartphone” and “voice analysis”), and a heart rate sensor (Para. 0029).
Regarding Claim 22, Wild teaches the medium of claim 21, wherein: output from the accelerometer is used to form features used by the machine learning model indicative of physical activity (Para. 0289), gestures (Para. 0107, “physical movement observations”), and behavioral patterns of the user (Para. 0289).
Regarding Claim 23, Wild teaches the medium of claim 21, wherein: the geolocation sensor is a satellite navigation sensor (Para. 0055, “GPS”); output from the geolocation sensor is used to form features used by the machine learning model indicative of mobility patterns (Para. 0031, “generating bicycle data, including pedal force, pedaling cadence, acceleration, speed, routes taken, GPS data, altimeter data, time on bicycle, pedometer data information for the individual; generating pedometer data and gait analysis information for the individual”), travel habits (Para. 0031, “generating location information indicative of a plurality of locations the individual has been”), and exposure to different environments (Para. 0089).
Regarding Claim 25, Wild teaches the medium of claim 21, wherein: output from the heart rate sensor, and variability thereof over time, is used to form features used by the machine learning model indicative of physiological arousal, stress levels (Para. 0120, “As an example of this; a heart rate meter is ordinarily used to detect heart rate, heart rate variability, return to baseline after exercise or stress event and so on”), and emotional states of the user (Para. 0120, “These ‘behavioural physiological indicators’ could well be the most reliable early indicator for chronic stress build up.”).
Regarding Claim 26, Wild teaches the medium of claim 21, the operations further comprising: determining a personalized intervention based on the inferred mental-health state of the user (Para. 0102).
Regarding Claim 27, Wild teaches the medium of claim 1, wherein: the machine learning model is configured to perform active learning (Para. 0187, “The processor (1) uses an algorithm to generate a personal stress profile which is indicative of the magnitude and form of stress experienced by the user at the time of testing. Stress can be measured and categorized in various ways. When applied consistently, the algorithm highlights relative differences over time for each individual, and differences from one individual to another. The stress profile can also be used as a basis to test the effectiveness of different types of stress treatment on each form of stress”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Wild, as shown in Claim 1 above, further in view of U.S. Patent Publication 20090018407 awarded to Jung et al, further in view of U.S. Patent Publication 20210290174 awarded to Neumann.
Regarding Claim 4, Wild teaches the medium of claim 1, wherein: the data include usage of an application installed on the mobile computing device (Para. 0114, “phone usage” and “(‘app’) usage”); typing and touchscreen input variability of the user on the mobile computing device and scrolling behavior of the user on the mobile computing device (Para. 0114, “Other inputs can be measured such as, keystroke rate, cadence, typing style, pressure or force detection (keypad, trackpad, screen pressure sensor)”). Wild does not teach the data comprising typing errors of the user on the mobile computing device and wherein: the data is multimodal, the machine learning model includes a neural network with more than three layers configured.
However, in the art of health monitoring (abstract), Jung teaches the usage of monitoring the typing errors of an individual (Para. 0197, “A motor skill test function may measure, for example, a user's ability to traverse a path on a display in straight line with a pointing device, to type a certain sequence of characters without error, or to type a certain number of characters without repetition”, Para. 0100, “Alternatively, a user may be prompted to perform an executive function as a predicate to launching an application such as a word processing program. For example, an alertness test function could be activated by a user command to open a word processing program, requiring performance of, for example, a spelling task as a preliminary step in launching the word processing program. Also, writing ability may be tested by requiring the user to write their name or write a sentence on a device, perhaps with a stylus on a touchscreen”) to monitor an individual’s mental status (Para. 0053).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Jung, i.e. by adding the typing error detection of Jung to the system of Wild, for the predictable purpose of improving the mental monitoring of Wild in the same manner as in Jung.
Further, in the art of mental health monitoring (Para. 0072), Neumann teaches wherein: the data is multimodal, the machine learning model includes a neural network with more than three layers configured (Para. 0075, “With continued reference to FIG. 1, models may be generated using alternative or additional artificial intelligence methods, including without limitation by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes”) to classify whether the data is indicative of a mental health disorder (Para. 0072, “For instance, and without limitation, at least a machine-learning process may include a mental health suitability classification process and/or scoring algorithm that scores and/or classifies options according to quality and/or availability of mental health supports and/or protocols”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Jung, i.e. using the multi-layer machine learning/training system in the system of Wild, for the predictable purpose of improving the mental monitoring of Wild in the same manner as in Neumann.
Regarding Claim 5, Wild modified by Jung and Neumann makes obvious the medium of Claim 4. Wild further teaches wherein: the machine learning model comprises an anomaly detection model configured to detect deviation from baseline data of the user or a population of users (Para. 0038, “An embodiment comprises the step of generating a stress resilience score indicative of a response to acute stress for the individual. Preferably, the stress resilience score is indicative of one or more of the time taken for the individual to respond to an acute stress event, if the individual exhibits any response to an acute stress event, and if so, the level of response exhibited by the individual to an acute stress event and the time taken for the individual's stress information to return to baseline levels following a period of acute stress”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wild, as shown in Claim 1 above, further in view of U.S. Patent Publication 20150313529 awarded to Nevo et al.
Regarding Claim 8, Wild teaches the medium of claim 1. Wild does not teach wherein: the machine learning model is configured to determine a score based on an amount of switching between tasks by the user on the mobile computing device and determine whether the score satisfies a threshold associated with a mental health condition. Wild does teach monitoring types of applications used as well as the duration and time of day used (Para. 0114).
However, in the art of mental health monitoring, Nevo teaches wherein: the machine learning model is configured to determine a score based on an amount of switching between tasks by the user on the mobile computing device and determine whether the score satisfies a threshold (Para. 0202, “The server distinguishes between normative and obsessive usage in the smartphone, based on the frequency of switching between applications, the frequency of opening and closing applications, the frequency of turning on and off the screen, and the volume of data usage”) associated with a mental health condition (Para. 0101).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Nevo, i.e. by using the task-switch monitoring of Nevo in the system of Wild, as Wild already teaches the need to monitor app usage to track a mental state of a user.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Wild, as shown in Claim 1 above, further in view of U.S. Patent Publication 20210290174 awarded to Neumann.
Regarding Claim 11, Wild teaches the medium of claim 1, wherein: the data includes text input by the user (Para. 0114, “Application (‘app’) usage. including specific applications used, duration of usage, time of day apps used, in-app analytics (use characteristics within any app), keyword searches, word and phrase usage (usually applied within word processing, email, messaging and social media applications but not limited to these)”). Wild does not teach wherein the machine learning model includes a natural language processing model configured to infer the mental-health state of the user based on the text input by the user.
However, Neumann teaches wherein the machine learning model includes a natural language processing model configured to infer the mental-health state of the user based on the text input by the user (Para. 0064).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Neumann, i.e. by using natural language processing to process the data of Wild as in Neumann, for the predictable purpose of simply substituting one for of language processing for another.
Claims 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wild in view of Neumann, as shown in Claim 11 above, further in view of U.S. Patent Publication 20210133509 awarded to Wall et al.
Regarding Claims 12 and 13, Wild in view of Neumann makes obvious the medium of claim 11, wherein the medium tracks facial expressions (Paras. 0277-0280, “The physiological information collection tool comprises software which controls the device's camera to image the user's face and thereby detect: [0278] pulse; [0279] skin colour and circulation; [0280] facial expression”). Wild does not teach wherein: the computer vision model is configured to provide real-time inference of the mental-health state of the user within five seconds of capturing the image with the camera and the computer vision model is configured to detect facial landmarks associated with different emotions, the image is a frame of video captured by a the camera; and the computer vision model is configured to infer the mental health state of the user based on movement detected based on differences between frames of the video.
However, in the art of mental health monitoring (Para. 0011), Wall teaches (Para. 0321, “In some cases, the device also comprises an inward-facing camera (e.g., a “selfie” camera) and tracks and classifies the emotions of the digital therapy recipient. The tracking and classification of the emotions of the social partner and the emotions of the digital therapy recipient can be performed in real time simultaneously or in close temporal proximity (e.g., within 1, 2, 3, 4, or 5 seconds of each other, or some other appropriate time frame)”); and the computer vision model is configured to detect facial landmarks associated with different emotions, the image is a frame of video captured by a the camera and the computer vision model is configured to infer the mental health state of the user based on movement detected based on differences between frames of the video (Para. 0321, “This allows the social interaction between the patient and the target individual to be captured, for example, as the combined facial expression and/or emotion of both persons. In some cases, the detected expressions and/or emotions of the parties to a social interaction are time-stamped or otherwise ordered so as to determine a sequence of expressions, emotions, or other interactions that make up one or more social interactions. These social interactions can be evaluated for the patient's ability to engage in social reciprocity”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Wall, i.e. by using the facial tracking of Wall in the facial tracking of Wild, for the predictable purpose of improving the facial tracking of Wild as shown in Wall.
Regarding Claim 14 and 15, Wild in view of Neumann makes obvious the medium of claim 11, wherein the medium tracks facial expressions (Paras. 0277-0280, “The physiological information collection tool comprises software which controls the device's camera to image the user's face and thereby detect: [0278] pulse; [0279] skin colour and circulation; [0280] facial expression”). Wild does not teach wherein: the computer vision model is a deep learning model trained by obtaining a training set with more than 500 images of faces and learning to perform automated feature extraction for features corresponding to facial expression changes with emotion changes based on the training set, wherein the operations comprise: assessing changes in emotional state of the user over time based on intensity and duration of emotional states inferred from a plurality of images captured over time, the plurality of images including the image.
However, Wall teaches wherein: the computer vision model is a deep learning model trained by obtaining a training set with more than 500 images of faces and learning to perform automated feature extraction for features corresponding to facial expression changes with emotion changes based on the training set, and assessing changes in emotional state of the user over time based on intensity and duration of emotional states inferred from a plurality of images captured over time, the plurality of images including the image (Para. 0463, “A machine learning module 2348 assesses the relative sensitivity of each input to the diagnosis to determine which types of measurement provide the most information regarding a patient's diagnosis. These results can be used by test administration module 2342 to provide tests which most efficiently inform diagnoses and by subject assessment module 2346 to apply weights to diagnosis data in order to improve diagnostic accuracy and consistency. Diagnostic data relating to each treated patient are stored, for example in a database, to form a library of diagnostic data for pattern matching and machine learning. A large number of subject profiles can be simultaneously stored in such a database, for example 10,000 or more”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Wall, i.e. by using the facial tracking training of Wall in the facial tracking of Wild, for the predictable purpose of improving the facial tracking of Wild as shown in Wall.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Wild, as shown in Claim 1 above, further in view of U.S. Patent Publication 20090018407 awarded to Jung et al.
Regarding Claim 24, Wild teaches the medium of claim 21, wherein: output from the microphone is used to form features used by the machine learning model indicative of speech patterns (Para. 0114), social interactions (Para. 0138). Wild does not teach wherein the feature is for ambient sounds.
However, Jung teaches the usage of monitoring ambient sounds (Para. 0074, “For example, the at least one device 102 and/or user monitoring device 182 may detect user hearing data from an interaction between a user 190 and a music-playing application by measuring sound volume settings or changes thereto. Alternatively, for example, the at least one device 102 and/or user monitoring device 182 may detect user hearing data from an interaction between the user 190 and a mobile telephone by determining a volume setting on the telephone or changes to the volume setting”) to provide data for a mental health machine learning model (step 420, Fig. 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wild by Jung, i.e by monitoring the ambient noise as taught above in Jung in the system of Wild, for the predictable purpose of improving the monitoring of Wild as in Jung.
Conclusion
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/JLM/
Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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