DETAILED ACTION
This office action is in response to Applicant’s submission filed on 11/21/2024. This is a CON case based on the Application 17/292339 which was issued as US Patent 12175998 on December 24, 2024. Claims 22-41 are pending in the application of which Claims 22, 35, and 41 are independent and have been examined.
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
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365 is acknowledged. The prior-filed application (Provisional application No. 62/758511 Filed on 11/9/2018) is acknowledged.
Information Disclosure Statement
The information disclosure statement(s)(IDS) submitted on 11/21/2024, and 5/1/2025 have been considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claim 38, and therefore claim 39 which depend therefrom 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.
Claim 38, recites “… applying a machine learning algorithm to the multi-dimensional statistical signature to generate a ...”, which appears to be indefinite since it is not clear which multi-dimensional statistical signature it is referring to.
Applicant is advised to review all claims for any potential antecedent basis issues.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 22 - 41 are rejected on the ground of nonstatutory double patenting as being unpatentable over U.S. Patent No. 12175998. The claims of the issued patent are narrower in scope than that of the instant application. Therefore, the claims of the issued patent anticipate the claims of the instant application. Instant application and issued patent each have device, method, and CRM claim. Please see the detailed claim mapping below.
Instant Application: 18955380
Issued Patent: US12175998
22
A device for migraine identification, comprising: signal processing circuitry configured to:
1
A migraine identification device comprising:
22.aa
access an input signal indicative of speech associated with a user;
1.a
audio input circuitry configured to provide an input signal that is indicative of speech provided by a user; signal processing circuitry including a processor configured to:
22.bb
extract, from the input signal, a signature defining speech production abilities of the user to assess changes in speech patterns associated with a migraine,
1.b
receive the input signal, the input signal including one or more speech samples representing speech patterns; and process the input signal to:
1.c
generate an instantaneous multidimensional statistical signature of speech production abilities of the user, and
22.cc
generate a comparison between speech features from the signature and one or more baseline statistical signatures of speech production ability associated with the user, and
1.d
compare the multi-dimensional statistical signature against one or more baseline statistical signatures of speech production ability derived or obtained from the user to assess changes in the speech patterns associated with a migraine; and
22.dd
provide a migraine identification signal based on the comparison to predict a migraine occurrence.
1.e
based on the analysis, activate a contextual on-device invocation model that is trained to detect an enhanced set of one or more hot words, wherein one or more of the hot words of the enhanced set is not in the default set
1.f
a notification element coupled to the signal processing circuitry, the notification element configured to receive the migraine identification signal and provide at least one notification signal to the user.
23
The device of claim 22,
2
The migraine identification device of claim 1,
23.aa
wherein the signature is a multidimensional statistical signature that spans one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation.
2.a
wherein the multi-dimensional statistical signature spans one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation.
23
The device of claim 22,
2
The migraine identification device of claim 1,
23.aa
wherein the signature is a multidimensional statistical signature that spans one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation.
2.a
wherein the multi-dimensional statistical signature spans one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation.
24
The device of claim 22, wherein the signal processing circuitry is configured to
3
The migraine identification device of claim 1,
24.aa
process the input signal by measuring speech features represented in the input signal, the speech features comprising one or more of speaking rate, articulation rate, articulation entropy, vowel space area, energy decay slope, phonatory duration, and average pitch.
3.a
wherein the signal processing circuitry is configured to process the input signal by measuring speech features represented in the input signal, the speech features comprising one or more of speaking rate, articulation rate, articulation entropy, vowel space area, energy decay slope, phonatory duration, and average pitch.
25
The device of claim 22, wherein the signal processing circuitry is configured to
4
The migraine identification device of claim 3, wherein the signal processing circuitry is configured to
25.aa
compare the signature against the one or more baseline statistical signatures of speech production ability by comparison of each speech feature of the input signal to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability.
4.a
compare the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability by comparing each speech feature to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability.
26
The device of claim 22, wherein the signal processing circuitry is configured to
5
The migraine identification device of claim 1, wherein the signal processing circuitry is configured to
26.aa
process the input signal utilizing the input signal and additional data comprising one or more of sensor data, a time of day, an ambient light level, a device usage pattern of the user, or a user input.
5.a
process the input signal utilizing the input signal and additional data comprising one or more of sensor data, a time of day, an ambient light level, a device usage pattern of the user, or a user input.
27
The device of claim 26, wherein the signal processing circuitry is configured to
6
The migraine identification device of claim 5, wherein the signal processing circuitry is configured to
27.aa
process the input signal by selecting or adjusting the one or more baseline statistical signatures of speech production ability based on the additional data.
6.a
process the input signal by selecting or adjusting the one or more baseline statistical signatures of speech production ability based on the additional data.
28
The device of claim 26,
7
The migraine identification device of claim 5,
28.aa
wherein the user input comprises at least one of information regarding exposure to potential migraine triggers or a self-reported premonitory migraine symptom.
7.a
wherein the user input comprises at least one of information regarding exposure to potential migraine triggers or a self-reported premonitory migraine symptom.
8
The migraine identification device of claim 1,
8.a
wherein the migraine identification device is a mobile computing device operating a migraine identification application.
29
The device of claim 22, further comprising
9
The migraine identification device of claim 8,
29.aa
a computing device that implements a migraine identification application to query the user periodically to provide a speech sample from which the input signal is derived.
9.a
wherein the migraine identification application queries the user periodically to provide a speech sample from which the input signal is derived.
30
The migraine identification device of claim 29,
10
The migraine identification device of claim 8,
30.aa
wherein the migraine identification application facilitates the user to spontaneously provide a speech sample from which the input signal is derived.
10.a
wherein the migraine identification application facilitates the user spontaneously providing a speech sample from which the input signal is derived.
31
The device of claim 29,
11
The migraine identification device of claim 8,
31.aa
wherein the migraine identification application passively detects changes in speech patterns of the user and initiates generation of an instantaneous multi-dimensional statistical signature of speech production abilities of the user.
11.a
wherein the migraine identification application passively detects changes in speech patterns of the user and initiates generation of the instantaneous multi-dimensional statistical signature of speech production abilities of the user.
32
The device of claim 22, further comprising
12
The migraine identification device of claim 1,
32.aa
a notification element including at least a display in communication with the signal processing circuitry.
12.a
wherein the notification element comprises a display.
33
The device of claim 32, wherein the signal processing circuitry is further configured to
13
The migraine identification device of claim 12, wherein the signal processing circuitry is further configured to
33.aa
cause the display to prompt the user to provide a speech sample from which the input signal is derived.
13.a
cause the display to prompt the user to provide a speech sample from which the input signal is derived.
34
The device of claim 32, wherein the signal processing circuitry is further configured to
14
The migraine identification device of claim 12,
34.aa
cause the display to render a display notification instructing the user to take action to relieve migraine symptoms.
14.a
wherein the at least one notification signal comprises a display notification instructing the user to take action to relieve migraine symptoms.
35
A method for identifying a migraine, the method comprising:
15
A method for identifying a migraine, the method comprising:
35.aa
accessing an input signal that is indicative of speech associated with a user;
15.a
receiving an input signal including a speech pattern that is indicative of speech provided by a user;
35.bb
measuring one or more speech features from the input signal indicative of migraine onset;
35.cc
extracting a signature of speech production abilities of the user from the input signal;
15.b
extracting a multi-dimensional statistical signature of speech production abilities of the user from the input signal;
35.dd
comparing the signature against one or more baseline statistical signatures of speech production ability; and
15.c
comparing the multi-dimensional statistical signature against one or more statistical signatures of speech production ability to assess a change in the speech pattern; and
35.ee
providing a migraine identification signal based on the comparison.
15.d
providing a migraine identification signal based on the multi-dimensional statistical signature comparison.
36
The method of claim 35,
16
The method of claim 15,
36.aa
wherein the one or more baseline statistical signatures of speech production ability are derived or obtained from the user.
16.a
wherein the one or more baseline statistical signatures of speech production ability are derived or obtained from the user.
37
The method of claim 35,
17
The method of claim 15,
37.aa
wherein the one or more baseline statistical signatures of speech production ability are at least partially based on normative acoustic data from a database.
17.a
wherein the one or more baseline statistical signatures of speech production ability are at least partially based on normative acoustic data from a database.
38
The method of claim 35, further comprising
18
The method of claim 15,
38.aa
applying a machine learning algorithm to the multi-dimensional statistical signature to generate a comparison between the signature and the one or more baseline statistical signatures of speech production ability.
18.a
wherein the comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises applying a machine learning algorithm to the multi-dimensional statistical signature.
39
The method of claim 38,
19
The method of claim 18,
39.aa
wherein the machine learning algorithm is trained with past comparisons for other users.
19.a
wherein the machine learning algorithm is trained with past comparisons for other users.
40
The method of claim 35, further comprising:
20
The method of claim 15, wherein:
40.aa
wherein the signature includes a multi-dimensional statistical signature of speech production abilities of the user from the input signal that comprises a measurement of speech features across one or more perceptual dimensions.
20.a
extracting the multi-dimensional statistical signature of speech production abilities of the user from the input signal comprises measuring speech features across one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation; and
20.b
comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises comparing each speech feature to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability.
41
A non-transient computer readable medium which, when executed by a processor, causes the processor to:
21
A non-transient computer readable medium which, when executed by a computer, causes the computer to:
41.aa
access an input signal that is indicative of speech associated with a user;
21.a
receive an input signal including a speech pattern that is indicative of speech provided by a user;
41.bb
extract clusters of acoustic measures derived from the input signal defining a multi-dimensional statistical signature; and
21.b
extract a multi-dimensional statistical signature of speech production abilities of the user from the input signal;
41.cc
produce a migraine identification signal based on a comparison between the multi-dimensional statistical signature comparison and one or more baseline statistical signatures of speech production ability,
21.c
compare the multi-dimensional statistical signature against one or more baseline statistical signatures of speech production ability to assess a change in the speech pattern; and
41.dd
the migraine identification signal predictive as to an occurrence of a migraine.
21.d
provide a migraine identification signal based on the multi-dimensional statistical signature comparison.
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.
Claims 22-27, 29 - 33, and 35-41 are rejected under 35 U.S.C. 103 as being unpatentable over Bobo et al. (US 20190307388A1)(herein "Bobo"), and in further view of Tzvieli et al. (US 20160360970A1)(herein " Tzvieli").
Regarding claims 22, 35 and 41 Bobo teaches [A device for migraine identification, comprising: signal processing circuitry configured to: - claim 22], [A method for identifying a migraine, the method comprising: - claim 35], and [A non-transient computer readable medium which, when executed by a processor, causes the processor to: - claim 41] (Bobo, Par. 0013:” … computing device and configured to analyze the signal to identify a pattern indicative of one or more predefined pathologies, wherein the predefined pathologies comprise at least one of tension headaches, migraines, depression, ….”, and Par. 0034:” … discloses a method for diagnosing one or more pathologies in a patient, the method comprising: receiving audio data …”, and Par. 0113:” … the microphones 104 are analyzed by a signal analyzer comprising at least one processor and a plurality of programmatic instructions stored in a memory, where the plurality of programmatic instructions include DSP, and machine learning, Artificial Intelligence, deep learning, neural networks (NN) and pattern recognition based algorithms, such as neural networks and artificial intelligence systems, in order to detect one or more of a set of pre-defined pathologies present in the detected vibrations of the patient.”, and Par. 0136:” The circuitry also allows the sound energy to be digitized, encoded and decoded, to have the ambient noise reduced or eliminated, and sent through speakers or headphones or transmitted for further processing.”)
[access an input signal indicative of speech associated with a user; - claim 22], [accessing an input signal that is indicative of speech associated with a user; - claim 35], [access an input signal that is indicative of speech associated with a user; - claim 41] (Bobo, Par. 0167:” … provides AI based methods of detection and analysis of human emotion/speech by detecting changes in tone, volume, speed and voice quality; and using said detected speech attributes to determine emotions like anger, joy, pain and laughter… speech data obtained from a person suffering from migraine and the person who is recording the session, demonstrating that speech data may be used for migraine diagnosis. In embodiments, speech data may be obtained from a person suffering from migraine and compared to speech data of another person who is not suffering from migraine, wherein said differences in speech data may be used for migraine diagnosis.”)
[extract clusters of acoustic measures derived from the input signal defining a multi-dimensional statistical signature; and – claim 41], [extracting a signature of speech production abilities of the user from the input signal; - claim 35] (Bobo, Par. 0113:” … pre-recorded acoustic patterns and specific frequencies unique to each kind of pathology are stored in one or more databases 114 coupled with the signal analyzer, which may be executed in a cloud solution computing platform 112.”, and Par. 0114:”Each pathology generates a unique acoustic pattern and specific frequency that enables identification of the pathology. For example, migraines generate (depicted by a spectrograph) a unique frequency pattern associated with the migraine.”, and Par. 0121:” … pathologies corresponding to a patient's acoustic data is further described …”, and Par. 0122:”… data received from each microphone may be used to generate unique patterns and features that may indicate an exclusive signature for different pathologies.”) Note: The microphone captures signals, which contains multi-dimensional characteristics like acoustic prosodic features.
[extract, from the input signal, a signature defining speech production abilities of the user to assess changes in speech patterns associated with a migraine, - claim 22], [measuring one or more speech features from the input signal indicative of migraine onset; - claim 35], [produce a migraine identification signal based on a comparison between the multi-dimensional statistical signature comparison and one or more baseline statistical signatures of speech production ability, - claim 41] (Bobo, Par. 0013:” … computing device and configured to analyze the signal to identify a pattern indicative of one or more predefined pathologies, wherein the predefined pathologies comprise at least one of tension headaches, migraines, depression, …”, and Par. 0032:” … the plurality of predefined patterns is indicative of a plurality of different migraine types.”, and Par. 0034:” … processing the audio data to obtain a spectrograph comprising a unique frequency pattern corresponding to the patient; …”., and Par. 0038:” … differentiating between at least one of a non-migraine condition, an active migraine condition, an asymptomatic migraine condition and a post therapy migraine condition in the patient. Optionally, said differentiation between said migraine conditions is performed by determining unique frequency patterns [signature] within frequency analyses and/or spectrographs corresponding to each of the migraine conditions.”, and Par. 0040:” … capturing facial expressions or speech patterns of the patient and using the captured facial expressions or speech patterns to enhance an accuracy of a diagnosis of the predefined type of pathology.”, and Par. 0046:” … analyze the signal to identify a pattern indicative of one or more predefined pathologies, …”, and Par. 0114:” Each pathology generates a unique acoustic pattern and specific frequency that enables identification of the pathology. For example, migraines generate (depicted by a spectrograph) a unique frequency pattern associated with the migraine.”, and Par. 0167:” … speech data obtained from a person suffering from migraine and the person who is recording the session, demonstrating that speech data may be used for migraine diagnosis. In embodiments, speech data may be obtained from a person suffering from migraine and compared to speech data of another person who is not suffering from migraine, wherein said differences in speech data [signature pattern] may be used for migraine diagnosis.”)
[generate a comparison between speech features from the signature and one or more baseline statistical signatures of speech production ability associated with the user, and – claim 22], [comparing the signature against one or more baseline statistical signatures of speech production ability; and – claim 35] (Bobo, Par. 0034:” … processing the audio data to obtain a spectrograph comprising a unique frequency pattern corresponding to the patient; …”., and Par. 0038:” … differentiating between at least one of a non-migraine condition, an active migraine condition, an asymptomatic migraine condition and a post therapy migraine condition in the patient. Optionally, said differentiation between said migraine conditions is performed by determining unique frequency patterns [signature] within frequency analyses and/or spectrographs corresponding to each of the migraine conditions.”, and Par. 0167:” … speech data obtained from a person suffering from migraine and the person who is recording the session, demonstrating that speech data may be used for migraine diagnosis. In embodiments, speech data may be obtained from a person suffering from migraine and compared to speech data of another person who is not suffering from migraine, wherein said differences [comparison] in speech data [signature pattern] may be used for migraine diagnosis.”) Note: spectrogram represent speech feature of a person with and without migraine.
Bobo does not teach, however Tzvieli teaches [provide a migraine identification signal based on the comparison to predict a migraine occurrence. -claim 22], [providing a migraine identification signal based on the comparison. – claim 35], [the migraine identification signal predictive as to an occurrence of a migraine. – claim 41] (Tzvieli, Par. 0097:” … the system described above includes a user interface configured to notify the user when the level of fight or flight response reaches a predetermined threshold. Optionally, the user interface utilizes at least one of an audio indication and visual indication to notify the user”.) Note: As noted earlier, Bobo identifies the migraine condition, while Tzvieli provide the notification to alert the migraine occurrence.
Tzvieli is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bobo further in view of Tzvieli to provide a migraine identification signal based on the comparison to predict a migraine occurrence. Motivation to do so would allow to manage level of pain felt by the user (Tzvieli, Par. 003).
Regarding claim 23, Bobo, as modified above, teaches the device claim of 22.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signature is a multidimensional statistical signature that spans one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation. (Tzvieli, Par. 0382:” … a measurement of affective response of a user comprises, and/or is based on, a behavioral cue of the user. A behavioral cue of the user is obtained by monitoring the user in order to detect things such as ... tone of voice ... The behavioral cues may be measured utilizing various types of sensors. Some non-limiting examples include ... a microphone ... ln one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress [e.g., stressing of certain syllables]. which may be indicative of the emotional state of the user”). Note: The microphone captures the user's speech, which contains multi-dimensional characteristics like acoustic prosodic features.
Regarding claim 24, Bobo, as modified above, teaches the device claim of 22.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signal processing circuitry is configured to process the input signal by measuring speech features represented in the input signal, the speech features comprising one or more of speaking rate, articulation rate, articulation entropy, vowel space area, energy decay slope, phonatory duration, and average pitch. (Tzvieli, Par. 0382:” … a measurement of affective response of a user comprises, and/or is based on, a behavioral cue of the user. A behavioral cue of the user is obtained by monitoring the user in order to detect things such as … tone of voice ... The behavioral cues may be measured utilizing various types of sensors. Some non-limiting examples include ... a microphone ... In one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress [e.g., stressing of certain syllables], which may be indicative of the emotional state of the user”) Note: Audio input from the microphone is processed to extract speech features like pitch and tempo. These features are then used to compute metrics such as average pitch, speaking rate, and articulation rate.
Regarding claim 25, Bobo, as modified above, teaches the device claim of 22.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signal processing circuitry is configured to compare the signature against the one or more baseline statistical signatures of speech production ability by comparison of each speech feature of the input signal to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability. (Tzvieli, Par. 0382:” … a measurement of affective response of a user comprises, and/or is based on, a behavioral cue of the user. A behavioral cue of the user is obtained by monitoring the user in order to detect things such as ... tone of voice ... The behavioral cues may be measured utilizing various types of sensors. Some non-limiting examples include ... a microphone ... ln one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress (e.g., stressing of certain syllables), which may be indicative of the emotional state of the user”, and Par. 0384:”… a predictor may receive as input, e.g., as one or more feature values comprised in a sample, a baseline affective response corresponding to the user. Optionally, the baseline affective response value may be derived from measurements of affective response of the user (e.g., earlier measurements) and/or it may be a predicted value (e.g., based on measurements of other users and/or a model for baseline affective response values). Accounting for the baseline affective response value (e.g., by normalizing the measurement of affective response according to the baseline), may enable the predictor, in some embodiments, to more accurately predict the emotional response a user is feeling”, and Par. 0425:” Herein, a predetermined value, such as a predetermined confidence level or a predetermined threshold, is a fixed value and/or a value determined any time before performing a calculation that compares a certain value with the predetermined value. A value is also considered to be a predetermined value when the logic, used to determine whether a threshold that utilizes the value is reached, is known before start of performing computations to determine whether the threshold is reached.”) Note: baseline speech feature can be used to compare against input speech features to provide additional values of interest.
Regarding claim 26, Bobo, as modified above, teaches the device claim of 22.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signal processing circuitry is configured to process the input signal utilizing the input signal and additional data comprising one or more of sensor data, a time of day, an ambient light level, a device usage pattern of the user, or a user input. (Tzvieli, Par. 0124:” the user interface 404 may include one or more of the following components: (i) an image generation device, such as a video display, an augmented reality system, a virtual reality system, and/or a mixed reality system, (ii) an audio generation device, such as one or more speakers, (iii) an input device, such as a keyboard, a mouse, a gesture-based input device that may be active or passive, and/or a brain-computer interface, a sensor input or user input can be processed”).
Regarding claim 27, Bobo, as modified above, teaches the device claim of 26.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signal processing circuitry is configured to process the input signal by selecting or adjusting the one or more baseline statistical signatures of speech production ability based on the additional data. (Tzvieli, Par. 0387/8:” describes receiving additional information besides the speech input and in Par. 0384:” … a predictor may receive as input, e.g., as one or more feature values comprised in a sample), a baseline affective response corresponding to the user. Optionally, the baseline affective response value may be derived from measurements of affective response of the user (e.g., earlier measurements) and/or it may be a predicted value (e.g., based on measurements of other users and/or a model for baseline affective response values). Accounting for the baseline affective response value (e.g., by normalizing the measurement of affective response according to the baseline), may enable the predictor, in some embodiments, to more accurately predict the emotional response a user is feeling.”) Note: As taught in Par. 0384: the baseline values may be derived from measurements corresponding to the user.
Regarding claim 29, Bobo, as modified above, teaches the device claim of 22.
Bobo, as modified above, teaches migraine identification (Bobo, Par. 0027:” … using the signal analyzer, acquiring the digitized captured signal and processing the acquired digitized captured signal to identify a signature, wherein the signature is a function of a non-zero amplitude, frequency and periodicity of the signal and wherein the signature is uniquely indicative of one of a tension headache, a migraine, …”, and Par. 0041:” … processing the digital signal to identify a signature of the migraine, wherein the signature has a first signal peak having a non-zero amplitude and a frequency in a range of 20 Hz to 1000 Hz and a second signal peak …”)
Bobo, as modified above, does not teach, however Tzvieli further teaches a computing device that implements a [[migraine identification]] application to query the user periodically to provide a speech sample from which the input signal is derived. (Tzvieli, Par. 0124:” … the communication interface 405 may include one or more components for connecting to one or more of the following: … the user interface 404 may include one or more of the following components: … an audio generation device, such as one or more speakers, (iii) an input device, …”, and Par. 0124 describes receiving additional data input besides the speech input …, and Par. 0384:” … a predictor may receive as input, e.g., as one or more feature values comprised in a sample), a baseline affective response corresponding to the user.”), and predicts a label for that sample (e.g., a class associated with the sample), is referred to as a “predictor” ... samples used for various purposes (e.g., training, testing, and/or a query) are assumed to have a similar structure (e.g., similar dimensionality) and are assumed to be generated in a similar process (e.g., they all undergo the same type of preprocessing)”; the query provides speech samples over time). In Par. 0358:” … a module that receives a query that includes a sample (e.g., a vector of one or more feature values), and predicts a label for that sample (e.g., a class associated with the sample), is referred to as a “predictor” ... samples used for various purposes (e.g., training, testing, and/or a query)are assumed to have a similar structure (e.g., similar dimensionality) and are assumed to be generated in a similar process (e.g., they all undergo the same type of preprocessing)”) Note: The aforementioned module receiving user query reads on user periodically providing a speech sample from which the input signal is derived.
Regarding claim 30, Bobo, as modified above, teaches the device claim of 29.
Bobo, as modified above, teaches migraine identification (Bobo, Par. 0027:” … using the signal analyzer, acquiring the digitized captured signal and processing the acquired digitized captured signal to identify a signature, wherein the signature is a function of a non-zero amplitude, frequency and periodicity of the signal and wherein the signature is uniquely indicative of one of a tension headache, a migraine, …”, and Par. 0041:” … processing the digital signal to identify a signature of the migraine, wherein the signature has a first signal peak having a non-zero amplitude and a frequency in a range of 20 Hz to 1000 Hz and a second signal peak …”)
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the [[migraine identification]] application facilitates the user to spontaneously provide a speech sample from which the input signal is derived. (Tzvieli, Par. 0382:” … a measurement of affective response of a user comprises, and/or is based on, a behavioral cue of the user. A behavioral cue of the user is obtained by monitoring the user in order to detect things such as ... tone of voice ... The behavioral cues may be measured utilizing various types of sensors. Some non-limiting examples include ... a microphone .... ln one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress (e.g., stressing of certain syllables), which may be indicative of the emotional state of the user.”) Note: "Monitoring" involves the continuous, active listening of incoming audio. Because the system does not require an explicit trigger or wake-word to initiate input, any speech picked up by the microphone is classified as spontaneous.
Regarding claim 31, Bobo, as modified above, teaches the device claim of 29.
Bobo, as modified above, teaches migraine identification (Bobo, Par. 0027:” … using the signal analyzer, acquiring the digitized captured signal and processing the acquired digitized captured signal to identify a signature, wherein the signature is a function of a non-zero amplitude, frequency and periodicity of the signal and wherein the signature is uniquely indicative of one of a tension headache, a migraine, …”, and Par. 0041:” … processing the digital signal to identify a signature of the migraine, wherein the signature has a first signal peak having a non-zero amplitude and a frequency in a range of 20 Hz to 1000 Hz and a second signal peak …”)
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the [[migraine identification]] application passively detects changes in speech patterns of the user and initiates generation of an instantaneous multi-dimensional statistical signature of speech production abilities of the user. (Tzvieli, Par. 0382:” … a measurement of affective response of a user comprises, and/or is based on, a behavioral cue of the user. A behavioral cue of the user is obtained by monitoring the user in order to detect things such as ... tone of voice ... The behavioral cues may be measured utilizing various types of sensors. Some non-limiting examples include ... a microphone ... ln one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress [e.g., stressing of certain syllables], which may be indicative of the emotional state of the user”. Note: Monitoring continuously detects incoming speech passively—without requiring user activation—and measures natural changes in the audio in real-time.
Regarding claim 32, Bobo, as modified above, teaches the device claim of 22.
Bobo, as modified above, teaches a notification element including at least a display in communication with the signal processing circuitry. (Bobo, Par. 0110:” … the user devices 106 enable display of data captured by the headset 102 and other notifications to the user using the headset.”, and Par. 0115:” … the user devices 106 comprise a graphical user interface (GUI) for displaying at least a diagnosis of the patient's condition.”)
Regarding claim 33, Bobo, as modified above, teaches the device claim of 32.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signal processing circuitry is further configured to cause the display to prompt the user to provide a speech sample from which the input signal is derived. (Tzvieli, Par. 0104:” … The display, which is worn by the user (e.g., it is attached to a frame of the HMS), is configured to present digital content to the user.”, and Par. 0382:” … a microphone ... ln one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress (e.g., stressing of certain syllables), which may be indicative of the emotional state of the user”, and Par. 0392: “Optionally, the user describes his or her emotional response after being prompted to do so by the software agent.”) Note: The display features interactive communication capabilities; therefore, prompting the user for a speech sample is a design choice.
Regarding claim 36, Bobo, as modified above, teaches the method claim of 35.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the one or more baseline statistical signatures of speech production ability are derived or obtained from the user. (Tzvieli, Par. 0124:” describes receiving additional data input besides the speech input described in Par. 0384:” … a predictor may receive as input, e.g., as one or more feature values comprised in a sample), a baseline affective response corresponding to the user. Optionally, the baseline affective response value may be derived from measurements of affective response of the user (e.g., earlier measurements) and/or it may be a predicted value (e.g., based on measurements of other users and/or a model for baseline affective response values). Accounting for the baseline affective response value (e.g., by normalizing the measurement of affective response according to the baseline), may enable the predictor, in some embodiments, to more accurately predict the emotional response a user is feeling.”) Note As taught in Par. 0384: the baseline values may be derived from measurements corresponding to the user.
Regarding claim 37, Bobo, as modified above, teaches the method claim of 35.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the one or more baseline statistical signatures of speech production ability are at least partially based on normative acoustic data from a database. (Tzvieli, Par. 0384:” … a predictor may receive as input, (e.g., as one or more feature values comprised in a sample), a baseline affective response corresponding to the user. Optionally, the baseline affective response value may be derived from measurements of affective response of the user (e.g., earlier measurements) and/or it may be a predicted value (e.g., based on measurements of other users and/or a model for baseline affective response values). Accounting for the baseline affective response value (e.g., by normalizing the measurement of affective response according to the baseline), may enable the predictor, in some embodiments, to more accurately predict the emotional response a user is feeling.”, and Par. 0362:” … For example, a label maybe include a discrete categorial value (e.g., a category describing an emotional response or one or more AUs), a numerical value (e.g., a real number describing the extent a certain emotion was expressed), and/or a multidimensional value (e.g., a point in multidimensional space, a database record, and/or another sample).”) Note: as-filed specification in Par. 0093 recites:” the one or more baseline statistical signatures of speech production ability are at least partially based on normative acoustic data from a database. For example, the baseline statistical signature(s) may be produced by a machine learning algorithm trained with past data for other users.”
Regarding claim 38, Bobo, as modified above, teaches the method claim of 35.
Bobo, as modified above, further teaches applying a machine learning algorithm to the multi-dimensional statistical signature to generate a comparison between the signature and the one or more baseline statistical signatures of speech production ability. (Bobo, Par. 0125:” In various embodiments the comparison of the patient's spectrograph with other spectrographs to obtain if the patient suffers from any of a plurality of pre-defined pathologies is achieved in the signal analyzer by using artificial intelligence (AI), machine learning or pattern recognition based algorithms. In an embodiment, distinctive acoustic patterns and frequencies generated from a pathology, if present in a patient's spectrograph, are identified by using AI, machine learning and pattern recognition-based algorithms. In an exemplary embodiment, the spectrograph of a patient suffering from migraine is analyzed with respect to a spectrograph of a person not suffering from migraines.”)
Regarding claim 39, Bobo, as modified above, teaches the method claim of 38.
Bobo, as modified above, further teaches wherein the machine learning algorithm is trained with past comparisons for other users. (Bobo, Par. 0116:” FIG. 2 is a block diagram illustrating a process of diagnosing pathologies by using a signal analyzer 202 configured to process initial data 204 comprising patterns defining a pathology for identifying said patterns by comparison with one or more predefined recorded patterns. The build model 202 is developed using a training model 206 by employing training data 208, as well as by providing the results of the build model 202 as a feedback 210 to the training model 206. The feedback 210 enables the training model 206 to learn to recognize diagnostic patterns and develop into a use model 212. The use model 212 identifies diagnostic patterns, in any new input data 214, by comparison, and provides results 216 conveying if a pathology such as ‘migraine’ is present in the data 214 or not.”, and Par. 0164:” … patients suffering from migraines, by using the signal analyzer employing AI and deep learning based algorithms.”) Note: training a model based on training data reads on past comparisons.
Regarding claim 40, Bobo, as modified above, teaches the method claim of 35.
Bobo, as modified above, does not teach, however Tzvieli further teaches wherein the signature includes a multi-dimensional statistical signature of speech production abilities of the user from the input signal that comprises a measurement of speech features across one or more perceptual dimensions. (Tzvieli, Par. 0382:” … a measurement of affective response of a user comprises, and/or is based on, a behavioral cue of the user. A behavioral cue of the user is obtained by monitoring the user in order to detect things such as ... tone of voice ... The behavioral cues may be measured utilizing various types of sensors. Some non-limiting examples include ... a microphone ...ln one example, a behavioral cue may involve prosodic features of a user's speech such as pitch, volume, tempo, tone, and/or stress (e.g., stressing of certain syllables), which may be indicative of the emotional state of the user”). Note: the microphone can receive speech input of the user, which can be used to determine multi-dimensional speech signatures such as prosodic features including pitch, volume, and tempo (perceptual dimensions).
Claims 28 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Bobo, and, and in further view of Brian J. Hernacki (US20180000425A1)(herein " Hernacki ").
Regarding claim 28, Bobo, as modified above, teaches the device claim of 26.
Bobo, as modified above, does not teach, however Hernacki teaches wherein the user input comprises at least one of information regarding exposure to potential migraine triggers or a self-reported premonitory migraine symptom. (Hernacki, ABS:” Techniques and device configurations used in the detection of migraine triggers or similar human pain conditions are disclosed. In an example, a migraine trigger detection device collects data on ambient stimuli for a human subject, through various light, sound, or odor sensors. A computing device connected to the migraine trigger detection device receives and processes the data to correlate the detected stimuli with migraine symptoms of the human subject. Such correlation may be based on real-time data, data from a prior phase of the migraine, or data from other migraine episodes (e.g., to identify common triggers of migraines over time). ... In further examples, trigger reports, pain condition, time inputs, or migraine phase may be obtained through a graphical user interface of the computing device.”, and Fig. 1:” headache identification device (headset 112 and/or wrist worn wearable sensor unit 12) is a migraine identification device.”, and Par. 0001:” … in some examples, data detection and condition processing for complex and variable medical conditions related to the occurrence of a migraine headache or like sensory-based condition in a human subject.”, and Par. 0021:” The migraine trigger detection device 110 of FIG. 1 is shown in the form factor of a wearable glasses headset 112, including a first sensor 114 for sound detection, a second sensor 116 for odor compound detection, and a third sensor 118 for light detection. The migraine trigger detection device 110, however, may take a variety of forms, and include additional or fewer sensors, or may be integrated into a different type of wearable or personal device form factor such as a watch, handheld device, into clothing or accessories, or the like.”, and Par. 0022:” The physiologic sensor device 120 of FIG. 1 is shown in the form factor of a wrist worn wearable sensor unit 122, but the physiologic sensor device 120 may take on other form factors [e.g. a chest strap heart rate monitor, a food sensor placed internally within a human user, etc.]. “) Note: All the above sensors are considered user input that could support exposure to potential migraine triggers.
Hernacki is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bobo, as modified above, further in view of Hernacki to wherein the user input comprises at least one of information regarding exposure to potential migraine triggers or a self-reported premonitory migraine symptom. Motivation to do so would prevent the migraine from fully spiraling and worsening.
Regarding claim 34, Bobo, as modified above, teaches the device claim of 32.
Bobo, as modified above, does not teach, however Hernacki teaches cause the display to render a display notification instructing the user to take action to relieve migraine symptoms. (Hernacki, Fig. 1, and Par. 0016:” … data may be used to alert a human subject to the incidence of such migraine triggers (even if they are not being perceived by the human subject), to allow the human subject to take action to avoid or reduce exposure to such triggers.”, and Par. 0028:” In response to the data received, and the processing of stimuli data (such as processing with the cloud-based remote processing system 140, the computing device 130, or the migraine trigger detection device 110), the graphical user interface (provided on the display 134) may identify and output triggers that have a high correlation to migraine events. A variety of suggestions, instructions, recommendations, warnings, and indications may be output with the graphical user interface on the display 134. For example, user outputs may provide information to a human subject about active triggers, therapy or pain management, and typical migraine symptoms and phases. Additionally, such identified triggers may result in other electronic actions, such as automated activation of a medical or therapy device, as discussed herein.”)
Hernacki is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bobo, as modified above, further in view of Hernacki to cause the display to render a display notification instructing the user to take action to relieve migraine symptoms. Motivation to do so would prevent the migraine from fully spiraling and worsening.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Meshram et al. (US20190221228A1) teaches in Par. 0019:” … The one or more voice parameters may include at least one of pitch range, word spacing (in terms of time gap between words), frequency, speed at which each word is uttered, phonation threshold pressure and time taken to utter the voice-based user input and any other parameter which serves the purpose. Based on the extracted one or more voice parameters, the system identifies a disease type associated with the user. The system then compares each of the one or more voice parameters with a first set of predetermined corresponding one or more voice parameters to verify correctness of each word in the voice-based user input. The first set of predetermined corresponding one or more voice parameters corresponds to the disease type identified by the system. ...”
Examiner's Note: Examiner has cited particular columns and line numbers and/or paragraph numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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DARIOUSH AGAHI, P.E.
Primary Examiner
/DARIOUSH AGAHI/Primary Examiner, Art Unit 2656
Prosecution Insights