Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see Amendment and Remarks, filed 06/26/26, with respect to claims 1-15 under 35 USC 101 have been fully considered and are persuasive. The rejection of claims 1-15 under 35 USC 101 has been withdrawn.
The examiner notes applicants Amendment and Remarks, filed 06/26/26, with respect to the rejection of claims 1-15 under 35 USC 112. The rejection of claims 1-15 under 35 USC 112 has been withdrawn however, the amendment introduced new clarity issues with respect to claim 2 and thus new rejections under 35 USC 112 are introduced below for claims 2-6
The examiner notes applicants Amendment and Remarks, filed 06/26/26, with respect to the rejection of claims 1-15 under 35 USC 102 in view of Osborne et al. In view of the amendments to the claims, the rejection of claims 1-4 and 6-15 under 35 USC 102 in view of Osborne et al. has been withdrawn however a new rejection of claims 1 -4,6-9,11, and 13-14 in view of Osborne et al. in view of Rowe et al has been set forth below. And claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osborne et al. in view of Rowe et al. and further in view of Craik et al.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a module for determining”, “an automatic selector”, and “ a secondary selector” in claim 1, “a collector of sound file” and “ a sound file classifier” in claim 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 2-6 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.
Regarding claim 2, “the duration, mode, tonality, quantification of the beat” , “ the acoustic nature of the sound file” , “the proportion of their use”, “the energizing, or invigorating, nature”, “the intensity and activity”, “ the perceptual characteristics” , “the dynamic range” ,” the perceived sound intensity” ,” the timbre” , “the rate of occurrence”, “ the general entropy”, “ the instrumental nature” , “the voice” , “the dance nature”, “the tempo, stability” “ the rhythm, strength of the beat”, “ the general regularity “, “ the audio file”, “ the valence” , “ the positivity” , “ the nature” , “ the nature of the density” , “ the proportion of words” , “the intensity of the sound file” , “ the average intensity” all lack antecedent basis.
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.
Claim(s) 1-4,6-9,11, and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osborne et al. (US 20180027347) hereinafter Osborne et al. in view of Rowe et al.( US 20120233164) hereinafter Rowe et al.
Osborne et al. teaches a method and system for analyzing audio (eg. music) tracks. A predictive model of the neuro-physiological functioning and response to sounds by one or more of the human lower cortical, limbic and subcortical regions in the brain is described. Sounds are analyzed so that appropriate sounds can be selected and played to a listener in order to stimulate and/or manipulate neuro-physiological arousal in that listener.
Regarding claim 1, Osborne et al. teaches a real-time reader of electroencephalographic signals, a module for determining an emotional state based on an electroencephalographic signal, a means for determining a target emotional state, an automatic selector of a sequence of at least one sound file, from a previously assembled list of sound files, based on the target emotional state, the EEG signal, and at least one parameter associated to each said sound file, and an electroacoustic transducer configured to play the selected sequence of sound files, characterized in that the device also comprises a secondary selector of a sound file, and a means for updating the sequence based on the sound file selected manually by the secondary selector.
Note figures 1-3 , paragraphs [0013] sets forth Different audio tracks and their optimal playing order can then be selected to manipulate neuro-physiological arousal, state of mind and/or affect—for example to move towards, to reach or to maintain a desired state of arousal or counter-arousal, state of mind or affect and paragraph[ 0194] and [0211] sets forth lead the user from her/his current state of mind and body to the intended condition of arousal or counter-arousal. Which sets forth an order of the tracks to lead the user by stages from the current state to the intended state which is a gradient of increasing states. [0017]-[0018], [0017] The invention is implemented in a system called X-System. X-System includes a database of music tracks that have been analyzed according to musical parameters derived from or associated with a predictive model of human neuro-physiological functioning and response to those audio tracks. [0018] Measurement of neuro-physiological state may be done using a variety of techniques, such as electro-encephalography, [0020] X-System may use this sensor data to sub-select music from any chosen repertoire, either by individual track or entrained sequences, that when listened to, will help the user to achieve a target state of excitement, relaxation, concentration, alertness, heightened potential for physical activity etc. This is achieved by analyzing music tracks in the user's database of music (using the musical parameters derived from the predictive model of human neuro-physiological response) and then automatically constructing a playlist of music, which may also be dynamically recalculated based on real-time bio-feedback, to be played to the user in order to lead her/him towards, and help to maintain her/him at, the desired target state., [0034], [0103], [0226], [0231], [0305] Explicit overrides will permit the user to manually skip a particular track either once, or to permanently blacklist it to ensure it will never be chosen again for them. In addition to their effect, these overrides will feed the decision model. , [0336] 0335] Additional modalities include: [0336] EEG type sensors or ‘caps’ for brainwave activity [0337] Electromyograph muscular tone/trigger rate [0338] Multi-point ECG for high-resolution heart waveform [0339] Breathing depth/rate [0340] Eye-tracking/Gaze/blink analysis. [0342] Consolidation of sensors into a single package such as a wrist-watch or headphone style appliance would be ideal. [0097] A sensor may optionally be used to establish the state of arousal of the user, and music categorized by predictive modelling of the INRM paradigm can then be streamed/played back to achieve the target arousal state for that user. In an alternative implementation sensors are not provided. Instead, both initial and target states are self-selected, either directly or indirectly (such as, for example, by selecting a ‘start song’ which has an arousal value relative to the user's true current state). For example, where the user makes a poor initial selection, he/she might skip from song to song initially until one is found (i.e. by trial and error) that is both ‘liked’ and ‘fits’ with their initial state. From there, X-System, in a sensor-less implementation, may create a playlist tending towards the desired arousal state based on expected normal human response.
Osborne et al. does teach user input of music tracks and dynamically updating the playlist based on sensor data relative to the target mood and where the user may select tracks or music repertoire of music that they would like to listen to as well as start song and/or skipping songs from which the system will create a playlist.
Osborne et al. does not specifically teach where updating the sequence configured for determining an updated sequence, including said sound files of said sequence and said manually selected sound file.
Rowe et al. teaches in the same field of endeavor identifies collections of digital music and sound that effectively elicit particular emotional responses as a function of analytical features from the audio signal and information concerning the background and preferences of the subject. The invention can change emotional classifications along with variations in the audio signal over time. Interacting with a listener, the invention locates music with desired emotional characteristics from a central repository, assembles these into an effective and engaging "playlist" (sequence of songs), and plays the music files in the calculated order to the listener. [0043] Online users have multiple opportunities to provide information about themselves, their musical preferences, and their emotional responses. The star rating function on the user interface (FIG. 4) records preference levels for individual songs (shown as "Individualized Preferences" in FIG. 3). While listening to songs selected by the classifier, the user may at any time indicate that a song has, for them, a different emotional quality than that predicted by the MCST. The predicted emotion is indicated on the emotional wheel by the Source tone logo (FIG. 4). The user may indicate his own emotional response to a track by dragging the album cover art icon to the emotional wheel and onto the adjective or area corresponding to his own emotional rating.
[0044] This will update the database with the appropriate emotion for that track for that user, and retrain the user's classifier accordingly. The "favorites" and "bans" section of the interface gives users another way to control their playlists and indicate their preferences. When the user designates a track, artist, or album a favorite, the play listing algorithm will increase the likelihood of that track, album, or artist being added to playlists for that user. When the user bans a track, artist, or album, the play listing algorithm will not add that track, or others from that artist or album (if those were indicated) to playlists targeted to that user. The user specifically interacts with the algorithm to add or remove tracks and the algorithm updates the playlist.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Osborne et al. the option to allow the user to more directly interact with the playlist by adding and removing tracks and where the algorithm will then update the playlist accordingly as taught by Rowe et al. to more accurately predict the emotional responses of that user.
Regarding claim 2, Osborne et al. teaches a collector of sound file identifiers; and a sound file classifier configured to associate, to at least one sound file identifier, a parameter representative of an emotional state fostered by that the sound file identified by said sound file identifier, such an parameter being chosen between: a technical parameter, such as the duration, mode, tonality, quantification of the beat or tempo of the sound file; or - an acoustic or psychoacoustic parameter representative of:
the acoustic nature of the sound file i.e. whether or not electronic instruments are used and/or the proportion of their use;
the energizing, or invigorating, nature of the sound file, i.e. a perceptive measurement of the intensity and activity - the perceptual characteristics contributing to this attribute comprise the dynamic range, the perceived sound intensity, the timbre, the rate of occurrence and the general entropy;
the instrumental nature of the sound file, i.e. whether or not the voice is used in this sound file;
the dance nature of the sound file, measured, for example, based on the tempo, stability of the rhythm, strength of the beat and the general regularity of the audio file;
the valence of the sound file, i.e. the positivity of the sound file;
the nature of the sound file recording, i.e. sound file recorded in a studio or during a direct performance;
the nature of the density of words, i.e. the proportion of words and music in a sound file;
and/or- the intensity of the sound file, i.e. the average intensity, measured in decibels, of the sound file.
Note figures 1-3 , [0010] The invention is a computer implemented system for analyzing sounds, such as audio tracks, the system automatically analyzing sounds according to musical parameters derived from or associated with a predictive model of the neuro-physiological functioning and response to sounds by one or more of the human lower cortical, limbic and subcortical regions in the brain, [0016] The musical parameters derived from or associated with the predictive model may relate to rhythmicity, and harmonicity and may also relate to turbulence—terms that will be explained in detail below. The invention may be used for the search, selection, ordering (i.e. sequencing), use, promotion, purchase and sale of music. It may further be used to select, modify, order or design non-musical sounds to have a desired neuro-physiological effect in the listener, or to permit selection, for example in designing or modifying engine exhaust notes, film soundtracks, industrial noise and other audio sources. [0017], [0020], [0222], [0227] FIG. 5 shows a desired architecture overview. FIG. 5 shows an implementation of the X-System invention where a primary music library, and analysis software resides on a user PC that is operable, remotely or locally by the listener or a third party, with the ability to transfer a selection of music to a personal music player device, which then generates a dynamic playlist based on the available music. Note also paragraphs [0009] , [0014], [0015], [0019], [0056], [0092], and [0093].
Regarding claim 3, Osborne et al. teaches wherein the classifier is a trained machine learning system. Note figures 1-3, [0230] A user initially provides the system with their personal music collection (or uses an online library of streamable or downloadable music). This is analyzed for level of excitement, using INRM categorization in combination with signal processing and machine learning techniques. The user then synchronizes this information with their music player and selects a level of excitement/arousal; someone other than the user may also select the excitement level.[0280], [0301].
Regarding claim 4, Osborne et al. teaches wherein the trained machine learning system is a supervised neural network configured to receive, as an input layer, parameter values and, as an output layer, emotional state indicators corresponding to the input layer.
Note figures 1-3, [0231] X-System may learn from individual users the range of their physiological responses in order to identify relative levels of arousal, and individually calibrate the diagnostic software. It may also learn about their personal preferences as already articulated through their choice of repertoire. X-System may also go directly from a set of musical features, using a neural network to predict the effect of these on physiological measurements, without first reducing the features to an expected excitement/arousal level.
Regarding claim 6, Osborne et al. teaches wherein the machine learning system is also pre-trained by using a set of data not specific to the user. Note Figures 1-3 and Paragraphs [0271] – [0280] discusses collecting and utilizing as part of the learning generalized group or population data to set moods for large groups of people.
Regarding claim 7, Osborne et al. teaches wherein at least one sound file is associated to an indicator of a behavior of the user regarding each said sound file, the automatic selector being configured to select a sequence of at least one sound file based on a value of this indicator for at least one sound file. Note Figures 1-3, paragraphs [0017]-[0018] [0017] The invention is implemented in a system called X-System. X-System includes a database of music tracks that have been analyzed according to musical parameters derived from or associated with a predictive model of human neuro-physiological functioning and response to those audio tracks. [0018] Measurement of neuro-physiological state may be done using a variety of techniques, such as electro-encephalography, [0020] X-System may use this sensor data to sub-select music from any chosen repertoire, either by individual track or entrained sequences, that when listened to, will help the user to achieve a target state of excitement, relaxation, concentration, alertness, heightened potential for physical activity etc.
Regarding claim 8, Osborne et al. teaches wherein the indicator of the user's behavior is a parameter representative of a number of plays and/or of a number of playback interruptions in favor of another sound track. Note figures 1-3 and paragraph [0305] The playback component handles 2 tasks. Controlling the music playback, and operating a real-time arousal analysis/entrainment model, based on sensor input. The component may be responsible for actually playing the music, or may be a control layer on top of an existing media player such as iTunes/Windows Media Player, etc. The arousal analysis model will be based on the X-system INRM model, using the pre-computed values from the Music Analysis component as a starting point. The user will select a desired outcome, and the sensors will be used to gauge progress towards that outcome of each track. Explicit overrides will permit the user to manually skip a particular track either once, or to permanently blacklist it to ensure it will never be chosen again for them. In addition to their effect, these overrides will feed the decision model.
Regarding claim 9, Osborne et al. teaches wherein the automatic selector comprises a sound file filter based on at least one indicator of a behavior of the user regarding at least one sound file, the selector being configured to select a sound files sequence from a list of sound files filtered by the filter. Note figures 1-3, and paragraphs [0175] Categorization may be preceded by aggregation, documenting provenance, genre and other data for music tracks. This may be according to an industry standard such as that provided by Gracenote®, it may be the result of individual user editorial, crowd-sourcing methods such as collaborative filtering, or may be the result of future aggregation standards based on, for example, digital signature analysis. The purpose of aggregation is to allow the user to choose a preferred musical style, though it is not strictly necessary for the proper functioning of X-System. [0271] The main directions of product improvement and expansion are as follows: [0272] Identification of emotional responses to music stimulated by memories or response to lyrics or other aspects of a song or piece of music rather than biology—developed by filtering out the expected physiological responses.
Regarding claim 11, Osborne et al. teaches wherein a parameter used by the automatic selector to select a sound files sequence is, in addition, a technical parameter chosen from the duration, mode, tonality, quantification of the beat and the tempo of the sound file. Note figures 1-3 and paragraphs [0006] The third method is to analyze metrics computed as a function of the music itself (usually tempo, but may also include a measure of average energy), and relate such metrics to the desired state of arousal of the subject. There are several such systems. Most rely on either ‘entrainment’ (in the Huygens sense, namely the tendency to synchronize to an external beat or rhythm) or on the association of increased tempo (and in one known case, energy) with increased effort or arousal (and the converse for reduced tempo and energy). [0129] X-System detects a basic, “default” rhythmic pulse in terms of beats per minute. There are often difficulties in establishing meter, but X System approximates the arousal effect of metrical structures by averaging the accumulation of power of rhythmic events over time. The power of a rhythmic event is defined as the ratio of the energy before the beat to the energy after it. In one very simple implementation, the beats per minute value (B) is combined with the mean of the beat strength (S) to produce a value for rhythmicity. [0134].
Regarding claims 13 and 14, Osborne et al. teaches wherein the reader is non-invasive and is an electroencephalogram type of headset. Note figures 1-3, and paragraphs [0336] 0335] Additional modalities include: [0336] EEG type sensors or ‘caps’ for brainwave activity [0337] Electromyograph muscular tone/trigger rate [0338] Multi-point ECG for high-resolution heart waveform [0339] Breathing depth/rate [0340] Eye-tracking/Gaze/blink analysis. [0342] Consolidation of sensors into a single package such as a wrist-watch or headphone style appliance would be ideal.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osborne et al. (US 20180027347) hereinafter Osborne et al. in view of Rowe et al.( US 20120233164) hereinafter Rowe et al. and further in view of Craik et al.( US 20200383598) hereinafter Craik et al.
Osborne et al. as modified by Rowe et al. teaches the claimed invention as set forth above including classifying music with respect to level s of arousal, valance and counter-arousal , [0013] In one implementation, tracks from a database of music are analyzed in order to predict automatically the neuro-physiological effect or impact those sounds will have on a listener. Different audio tracks and their optimal playing order can then be selected to manipulate neuro-physiological arousal, state of mind and/or affect—for example to move towards, to reach or to maintain a desired state of arousal or counter-arousal, state of mind or affect (the term ‘affect’ is used in the psychological sense of an emotion, mood or state). [0126] Both vertical and linear harmonicity are powerful indices of valence (Fritz 2009), or whether a sound is “positive” or “negative”, “pleasing” or “not so pleasing”. Linear harmonicity may track the evolution of valence indices over time—the principle is simply the more harmonic, the more positive valence, the less harmonic, the more negative valence. [0152] ‘Turbulence’ is therefore a measure of rate of change and extent of change in musical experience. These factors seem to activate core emotional systems of the brain, such as the amygdala and periaqueductal grey, which are in turn linked to autonomic and endocrine systems. At high levels of musical energy turbulence may enhance arousal; at low levels it may add to the counter-arousal effect.
However, Osborne et al. as modified by Rowe et al. does not specifically teach Valence, Arousal, and Dominance as three characteristics.
Craik et al. teaches in the same field of endeavor Apparatuses and methods for non-invasively detecting and classifying transcranial electrical signals are disclosed herein. In an embodiment, system for detecting and interpreting transcranial electrical signals includes: a headset including a plurality of electrodes arranged for detection of the user's transcranial electrical signals; a display configured to display information to the user while the user wears the headset; and a control unit programmed to: (i) receive data relating to the transcranial electrical signals detected by the electrodes of the headset; (ii) create a data matrix with the received data; (iii) convert the data matrix into one or more user values; (iv) define a user output state based on the one or more user values; and (iv) cause alteration of an aspect of the display based on the user output state. [0006] The present disclosure proposes apparatuses and methods for non-invasively detecting and classifying transcranial electrical signals. It is advantageous, for example, for therapeutic and entertainment purposes, to be able to use EEG data to determine a person's cognitive states in ways besides simply viewing that person's expression and body language. This is specifically applicable to the determination of a person's emotional state, as a subjective analysis of a person's emotional state based on visual evidence may not be reliable. It is also advantageous to be able to use EEG data to control images, videos, audio. [0178] FIG. 20A illustrates an example method 500a illustrating how a current user may calibrate headset 12 for use in determining one or more emotional state of a user. It should be understood that some of the steps described herein may be reordered or omitted, while other steps may be added, without departing from the spirit and scope of method 500a of FIG. 20A. It should further be understood that one or more of the steps of method 500a may be controlled by the control unit of neural analysis system 10 based on instructions stored on a memory and executed by a processor. [0179] In this example embodiment, the one or more user values are emotional values, and the one or more user output state is an emotional state. The emotional values can include, for example, one or more valence value, one or more arousal value, and one or more dominance value. For example, a first user value can be a valence value, a second user value can be an arousal value, and a third user value can be a dominance value. The emotional state can include, for example, an emotion felt by the user (e.g., joy, anger, etc.).
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device and method of Osborne et al. as modified by Rowe et al. classifying the sound not just by Valence, Arousal and Counter-Arousal as recognized by Osbourne et al. but by including the three known characteristics of emotional state Valence, Arousal, and Dominance as taught by Craik et al. to move towards, and reach or to maintain a desired state of emotion.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bill (US 20040237759) teaches a mood-based playlisting system may select content to maintain a mood consistency between selections of content. In one example, electronic content may be made available to users by determining a mood indicator indicating a present mood state of a user, determining a mood spectrum describing mood indicators that are consistent with the mood indicator for the track, and selecting a next track having a mood indicator that lies within the mood spectrum. In another example, content may be selected by determining a coordinate mood location indicating the present mood state of a user, determining a compatible mood volume indicating potential mood indicators for a next track that is compatible with the present mood, identifying one or more tracks having mood indicators that lie within the compatible mood volume, and enabling the user to access one or more of the identified tracks.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN L CASLER whose telephone number is (571)272-4956. The examiner can normally be reached M-Th 6:30 to 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN L CASLER/Primary Examiner, Art Unit 3791
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