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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 4/28/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In ¶0241 of the instant specification, “audio stem database 716” should read, “audio stem database 714.”
In ¶0250 of the instant specification, “At step 1726” should read, “At step 1728.”
Appropriate correction is required.
Claim Objections
Claim 18 is objected to because of the following informalities: In line 5, “adjusting the second current state vector” should read, “to adjust the second current state vector.” Appropriate correction is required.
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 1-18 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 1 recites the limitation "the speakers" in line 31. There is insufficient antecedent basis for this limitation in the claim. Amending to “the speaker” would resolve this rejection.
Claims 2-9 are likewise rejected for depending, directly or indirectly, from claim 1.
Claim 3 recites the limitation "the first soundscape" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the second soundscape" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the goal state vector" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the level of focus" in line 4. There is insufficient antecedent basis for this limitation in the claim. It is possible that Applicant intended to recite, “the current level of focus.”
Claim 8 recites the limitation, “music which is more susceptible to force an improvement to a level of focus” in line 3. It is unknown what is meant by “music which is more susceptible.” It is possible that Applicant intended to recite, “music which is more likely to force an improvement to a level of focus.”
Claim 10 recites the limitation "the speakers" in line 31. There is insufficient antecedent basis for this limitation in the claim. Amending to “the speaker” would resolve this rejection.
Claims 11-18 are likewise rejected for depending, directly or indirectly, from claim 10.
Claim 12 recites the limitation "the first soundscape" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the second soundscape" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the goal state vector" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the level of focus" in line 4. There is insufficient antecedent basis for this limitation in the claim. It is possible that Applicant intended to recite, “the current level of focus.”
Claim 17 recites the limitation, “music which is more susceptible to force an improvement to a level of focus” in line 3. It is unknown what is meant by “music which is more susceptible.” It is possible that Applicant intended to recite, “music which is more likely to force an improvement to a level of focus.”
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 1, 3, 5-7, and 9 are rejected under 35 U.S.C. 103 as unpatentable over Garten et al. (US 20150297109 A1, October 22, 2015), hereinafter Garten, in view of Vincente et al. (US 20210090590 A1, filed September 9, 2019), hereinafter Vincente, and further in view of Ehrlich et al. (A closed-loop, music-based brain-computer interface for emotion mediation, March 18, 2019, retrieved August 11, 2026 from https://pubmed.ncbi.nlm.nih.gov/30883569/), hereinafter Ehrlich, to the extent understood.
Regarding claim 1, Garten teaches a method for generating music for an electronic device coupled to a server (Garten ¶0058: "The computing devices may include one or more client or server computers in communication with one another over a near-field, local, wireless, wired, or wide-area computer network, such as the Internet, and at least one of the computers is configured to receive signals from sensors worn by a user."), the method being executable by a processor located on the server (Garten ¶0059: "While embodiments and implementations of the present invention may be discussed in particular non-limiting examples with respect to use of the cloud to implement aspects of the system platform, a local server, a single remote server, a SAAS platform, or any other computing device may be used instead of the cloud."), the processor coupled to: a speaker and a biosensor located in the electronic device, the sensor being configured to measure an electroencephalographic (EEG) data of the user (Garten ¶0163: "Johnny listens to music while wearing an EEG intelligent music system. The EEG could be embedded in the headphones, with sensors for example on the band at c3 and c4 and on the ears."); the method comprising: based on the determined second current state vector, determining whether a current state should be modified to achieve a desired goal state of the user by determining an error state vector (Garten ¶0862: "The difference between the user's target state and their current state may be represented as a vector. This vector can be used to select or recommend songs that may help the user achieve their target state."); in response to determining that the current state should be modified, determining a second set of music parameters, for achieving the desired goal state of the user (Garten ¶0887: "The controller changes the effects its applies to the music data to be output by setting the parameters of the Music Processor. The controller uses feedback from the Biological Feature Extractor and the features of the music output from the Sonic Feature Extractor to change the parameters (music effect parameters) of the Music Processor to adapt to helping the user reach their desired Target State.").
Garten does not explicitly disclose an audio stems database comprising a first plurality of audio stems and a second plurality of audio stems; the speaker being configured to receive and play a generative music; based on comparing of a first current state vector having a first set of musical parameters with stem label vectors of the audio stems, retrieving a first plurality of audio stems from the audio stem database and generating, by the processor, a first portion of generative music by combining the first plurality of audio stems into a plurality of simultaneously played layers, the first portion of generative music having the first set of musical parameters; measuring, with the biosensor, the EEG data while the first portion of generative music is played by the speakers to the user; determining, by analyzing the EEG data, a second current state vector that characterizes a second current state of the user; based on the second set of music parameters, retrieving a second plurality of audio stems from the audio stem database, and combining the second plurality of audio stems to generate a second portion of generative music characterized by the second set of music parameters; and transmitting the second portion of generative music to the speaker and collecting, in real time, a second set of EEG data of the user measured while the second portion of generative music is being played to the user by the speakers.
However, Vincente teaches an audio stems database comprising a first plurality of audio stems and a second plurality of audio stems (Vincente ¶0057: "Audio stem database 104 is arranged to store plural audio stems. In some embodiments, audio stem database 104 stores audio stems in an encoded format (e.g., .wav, .mp3, .m4a, .ogg, .wma, etc). One or more audio stems that are stored on the audio stem database 104 can be retrieved and inserted into a song during a song creation process."); based on comparing of a first current state vector having a first set of musical parameters with stem label vectors of the audio stems, retrieving a first plurality of audio stems from the audio stem database (Vincente ¶0119: "Audio stem database 104 is arranged to store plural audio stems. In some embodiments, audio stem database 104 stores audio stems in an encoded format (e.g., .wav, .mp3, .m4a, .ogg, .wma, etc). One or more audio stems that are stored on the audio stem database 104 can be retrieved and inserted into a song during a song creation process.") and generating, by the processor, a first portion of generative music by combining the first plurality of audio stems into a plurality of simultaneously played layers (Vincente ¶0002: "Stems prepared in this fashion may be blended together to form a music track. The arrangement of the stems and the stems themselves can be modified using various audio manipulation tools such as mixers. Stem-mixers, for example, are used to mix audio material based on creating groups of audio tracks and processing them separately prior to combining them into a final master mix."); based on the second set of music parameters, retrieving a second plurality of audio stems from the audio stem database (Vincente ¶0119: "The method also performs determining a query acoustic feature vector of a query audio content item in the second vector space, comparing the query acoustic feature vector and the plurality of target acoustic feature vectors in the second vector space, and identifying, based on the comparison, at least one audio content item in the plurality of target audio content items that is related to the query audio content item."), and combining the second plurality of audio stems to generate a second portion of generative music characterized by the second set of music parameters (Vincente ¶0139: "A practical application of embodiments described herein include identifying audio stems for the purpose of assembling them. The assembled plurality of audio stems can result in media content that can be played via a playback device. In some embodiments, the media content is in the form of a media content item in the form of a file that can be streamed, saved, mixed with other media content items, and the like.).
Furthermore, Ehrlich teaches the speaker being configured to receive and play a generative music (Ehrlich § 2.1: "The music generation system’s input controls and the generated musical patterns are consequently emotion-related. The MIDI-pat terns are sent over a virtual MIDI path (MIDI Yoke) to software (ProTools 8 LE) hosting virtual instruments (Fig 2A(c)) that are then translated into sound (playback engine was an AVIDMBox3 Mini external soundcard)."); the first portion of generative music having the first set of musical parameters (Ehrlich § 2.1: "These control parameters were implemented such that they modulate the musical pattern according to five music structural components, namely: harmonic mode, tempo, rhythmic roughness, overall pitch, and relative loudness of subsequent notes. According to their settings, different musical patterns are generated."); measuring, with the biosensor, the EEG data while the first portion of generative music is played by the speakers to the user (Ehrlich Fig. 1 caption: "During calibration, the user is exposed to automatically generated patterns of affective music, and brain activity is measured simultaneously via EEG. EEG patterns are extracted and used to build a user-specific emotion model."); determining, by analyzing the EEG data, a second current state vector that characterizes a second current state of the user (Ehrlich § 2.2.3: "During online application, the previously built LDA-model was used as a translator of EEG signals into input parameter settings for the automatic music generation system (Fig 3, bottom). Incoming EEG data (in segments of 4 sec with 87.5% overlap) were used for online feature extraction and classification."); and transmitting the second portion of generative music to the speaker and collecting, in real time, a second set of EEG data of the user measured while the second portion of generative music is being played to the user by the speakers (Ehrlich § 2.2.3: "Each score update (one per 0.5 sec) was subsequently translated into an update of the music generation system’s input parameters, with val = s and aro = s. Closing the loop was achieved by playing back the resulting musical patterns to the subject.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method for generating music for an electronic device coupled to a server of Garten by adding the music generation from stems of Vincente and the biosensor feedback and reactive adaptation of Ehrlich to automate the system's interpretation of the user's reaction (Garten ¶0903).
Regarding claim 3, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 1 as discussed above.
Ehrlich further teaches that determining the first set of musical parameters of the first soundscape is based on a first current state vector (Ehrlich § 2.2.3: "During online application, the previously built LDA-model was used as a translator of EEG signals into input parameter settings for the automatic music generation system (Fig 3, bottom).").
Regarding claim 5, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 1 as discussed above.
Garten further teaches that determining a current level of focus based on the first current state vector (Garten ¶0908: "The Controller uses the features from the Biological Feature Extractor to determine the current User State. In one example the User State can be described by four parameters: a) Valence (positive or negative emotion), b) Arousal, c) level of attention, and d) level of synchronization.") and wherein the desired goal state of the user is a desired level of focus (Garten ¶0442: "GOAL: FOCUS—how much concentration and distraction—measure of how well we are doing. System tries different variations of background music.").
Regarding claim 6, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 1 as discussed above.
Ehrlich further teaches that the second set of musical parameters is determined by a machine learning model (Ehrlich § 2.2.3: "During online application, the previously built LDA-model was used as a translator of EEG signals into input parameter settings for the automatic music generation system (Fig 3, bottom).").
Regarding claim 7, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 5 as discussed above.
Garten further teaches determining whether the level of focus is improved (Garten ¶0436: "Brainwave detector monitors Caleb to ensure the new music has changed his focus/attention levels (PROCESS).").
Ehrlich further teaches collecting, in real time, the EEG data of the user to which the second portion of generative music is played (Ehrlich § 2.2.3: "Each score update (one per 0.5 sec) was subsequently translated into an update of the music generation system’s input parameters, with val = s and aro = s. Closing the loop was achieved by playing back the resulting musical patterns to the subject.").
Regarding claim 9, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 1 as discussed above.
Garten further teaches that the server is coupled to another sensor configured to measure environmental data (Garten ¶0086: "One way to improve emotion detection with EEG is to add more sensors to read more data not available from the brain, or to incorporate data from other sensors on other devices that a user is also wearing. Sensors in other wearable technology devices can read things like: temperature; galvanic skin response; motion; heart-rate and pulse; muscle tension through electromyography."), and the method further comprising receiving environmental data from the electronic device (Garten ¶0087: "Additional data can help make a stronger case for one emotion or another. For example: an EEG might be able to sense a negative reaction to stimuli, but without contextual information from the user—either from the user's participation in an app environment, or from additional sensor data gleaned from other devices or other sensors of the system of the present invention—it might be difficult for the system to “learn” what precipitated that negative response.") and a context-relevant interaction data indicative of the user interaction with the electronic device (Garten ¶0088: "The user can reject the system's prediction and correct it with their own experience. In this way, the accuracy of the models used to predict emotion can be improved through direct user manual over-ride, using other measures of physiology related to emotion, context of the user (e.g. get information on the current activity from the user's calendar) and their behaviour (e.g. they skip over songs by artist X and they choose to listen to songs by artist Y.)") and adjusting the second current state vector based on the received environmental data and the context-relevant interaction data (Garten ¶0087: "Additional data can help make a stronger case for one emotion or another. For example: an EEG might be able to sense a negative reaction to stimuli, but without contextual information from the user—either from the user's participation in an app environment, or from additional sensor data gleaned from other devices or other sensors of the system of the present invention—it might be difficult for the system to “learn” what precipitated that negative response.").
Claims 2, and 8 are rejected under 35 U.S.C. as unpatentable over Garten in view of Vincente and further in view of Ehrlich and Beiser et al. (US 20210357174 A1, effective filing date May 18, 2020), hereinafter Beiser, to the extent understood.
Regarding claim 2, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 1 as discussed above.
Garten (in view of Vincente and further in view of Ehrlich) does not explicitly disclose that the processor is coupled to an audio effects database comprising a first plurality of audio effects and wherein generating the first portion of generative music characterized by the first set of musical parameters further comprises: combining the first plurality of audio stems with the first plurality of audio effects into a plurality of simultaneously played layers.
However, Beiser teaches that the processor is coupled to an audio effects database comprising a first plurality of audio effects (Beiser ¶0022: "The term 'preset' as used herein refers to one or more audio processing steps which have been previously defined and stored in a database preferably with metadata describing context of when the audio processing steps may be used. The 'preset' as used herein, is more general than a set of parameters for known audio software plug-ins e.g. equalization, compression, reverberation, and may specify a generalized audio processing function and/or combination of previously defined audio processing functions.") and wherein generating the first portion of generative music characterized by the first set of musical parameters further comprises: combining the first plurality of audio stems with the first plurality of audio effects into a plurality of simultaneously played layers (Beiser ¶0026: "Referring back to FIG. 2, after recommendation 207 is selected in step 25, original audio tracks 201 may be processed (step 212) according to recommendation 207. The processed audio tracks may be mixed (step 214) into a playable audio production and the audio production may be played (step 216).").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method for generating music for an electronic device coupled to a server of Garten (as modified by Vincente and Ehrlich) by adding the effects database and effects combinations of Beiser in order to provide a playable digital file with a pleasant audio experience (Beiser ¶0020).
Regarding claim 8, Garten (in view of Vincente and further in view of Ehrlich and Beiser) teaches the method of claim 2 as discussed above.
Garten further teaches determining a third set of music parameters of a third portion of the generative music (Garten ¶0887: "The controller changes the effects its applies to the music data to be output by setting the parameters of the Music Processor. The controller uses feedback from the Biological Feature Extractor and the features of the music output from the Sonic Feature Extractor to change the parameters (music effect parameters) of the Music Processor to adapt to helping the user reach their desired Target State.") which is more susceptible to force an improvement of a level of focus (Garten ¶0436: "Brainwave detector monitors Caleb to ensure the new music has changed his focus/attention levels (PROCESS).").
Ehrlich further teaches transitioning the generative music automatedly generated into a third portion of generative music based on the third set of music parameters (Ehrlich § 2.1: "By gradually changing the input parameters, the algorithm’s generated music patterns flow seamlessly and gradually change in terms of emotional expressiveness."), and playing the third portion of generative music to the user (Ehrlich § 2.2.3: "Closing the loop was achieved by playing back the resulting musical patterns to the subject.").
Claim 4 is rejected under 35 U.S.C. 103 as unpatentable over Garten in view of Vincente, and further in view of Ehrlich and Williams et al. (A Perceptual and Affective Evaluation of an Affectively-Driven Engine for Video Game Soundtracking, December 2016, retrieved 8/11/2026 from https://www.researchgate.net/publication/312967993_A_Perceptual_and_Affective_Evaluation_of_an_Affectively-Driven_Engine_for_Video_Game_Soundtracking), hereinafter Williams, to the extent understood.
Regarding claim 4, Garten (in view of Vincente and further in view of Ehrlich) teaches the method of claim 1 as discussed above.
Garten (in view of Vincente and further in view of Ehrlich) does not explicitly disclose that determining the second set of musical parameters of the second soundscape is based on a vectorial difference between a goal set of musical parameters and a current set of musical parameters, determined from a vectorial difference between the goal state vector and the second current state vector.
However, Williams teaches that determining the second set of musical parameters of the second soundscape is based on a vectorial difference between a goal set of musical parameters and a current set of musical parameters (Williams § 2.1: The generated musical state sequences are subsequently further transformed according to the distance between the current features and the features which correlate to a given affective target (the transformations indicating the affective correlates are shown in Table 1)."), determined from a vectorial difference between the goal state vector and the second current state vector (Williams fig. 3: "Calculate difference between generated sequence correlate values and target correlate values").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method for generating music for an electronic device coupled to a server of Garten (as modified by Vincente and Ehrlich) by adding the musical feature interpolation of Williams to calibrate the musical feature set on a case-by-case basis (Williams § 3.1).
Claims 10, 12, 14-16, and 18 are rejected under 35 U.S.C. 103 as unpatentable over Vincente in view of Garten, and further in view of Ehrlich, to the extent understood.
Regarding claim 10, Vincente teaches a system for generating music for an electronic device, the system comprising: an audio stems database comprising a first plurality of audio stems and a second plurality of audio stems (Vincente ¶0057: "Audio stem database 104 is arranged to store plural audio stems. In some embodiments, audio stem database 104 stores audio stems in an encoded format (e.g., .wav, .mp3, .m4a, .ogg, .wma, etc). One or more audio stems that are stored on the audio stem database 104 can be retrieved and inserted into a song during a song creation process."); based on comparing of a first current state vector having a first set of musical parameters with stem label vectors of the audio stems, retrieve a first plurality of audio stems from the audio stem database (Vincente ¶0119: "The method also performs determining a query acoustic feature vector of a query audio content item in the second vector space, comparing the query acoustic feature vector and the plurality of target acoustic feature vectors in the second vector space, and identifying, based on the comparison, at least one audio content item in the plurality of target audio content items that is related to the query audio content item.") and generate a first portion of generative music by combining the first plurality of audio stems into a plurality of simultaneously played layers based on the second set of music parameters (Vincente ¶0002: "Stems prepared in this fashion may be blended together to form a music track. The arrangement of the stems and the stems themselves can be modified using various audio manipulation tools such as mixers. Stem-mixers, for example, are used to mix audio material based on creating groups of audio tracks and processing them separately prior to combining them into a final master mix."); retrieve a second plurality of audio stems from the audio stem database (Vincente ¶0119: "The method also performs determining a query acoustic feature vector of a query audio content item in the second vector space, comparing the query acoustic feature vector and the plurality of target acoustic feature vectors in the second vector space, and identifying, based on the comparison, at least one audio content item in the plurality of target audio content items that is related to the query audio content item."), and combine the second plurality of audio stems to generate a second portion of generative music characterized by the second set of music parameters (Vincente ¶0139: "A practical application of embodiments described herein include identifying audio stems for the purpose of assembling them. The assembled plurality of audio stems can result in media content that can be played via a playback device. In some embodiments, the media content is in the form of a media content item in the form of a file that can be streamed, saved, mixed with other media content items, and the like.).
Vincente does not explicitly disclose a speaker and a biosensor located in the electronic device, the sensor being configured to measure an electroencephalographic (EEG) data of the user, and the speaker being configured to receive and play a generative music; and a server comprising a processor located on the server, the processor configured to: the first portion of generative music having the first set of musical parameters; receive, from the biosensor, the EEG data while the first portion of generative music is played by the speakers to the user; determine, by analyzing the EEG data, a second current state vector that characterizes the second current state of the user; based on the determined second current state vector, determine whether a current state should be modified to achieve a desired goal state of the user by determining an error state vector; in response to determining that the current state should be modified, determine a second set of music parameters, for achieving the desired goal state of the user; and transmit the second portion of generative music to the speaker and collect, in real time, a second set of EEG data of the user measured while the second portion of generative music is being played to the user by the speakers.
However, Garten teaches a speaker and a biosensor located in the electronic device, the sensor being configured to measure an electroencephalographic (EEG) data of the user (Garten ¶0163: "Johnny listens to music while wearing an EEG intelligent music system. The EEG could be embedded in the headphones, with sensors for example on the band at c3 and c4 and on the ears."); and a server (Garten ¶0059: "While embodiments and implementations of the present invention may be discussed in particular non-limiting examples with respect to use of the cloud to implement aspects of the system platform, a local server, a single remote server, a SAAS platform, or any other computing device may be used instead of the cloud.") comprising a processor located on the server (Garten ¶0058: "The computing devices may include one or more client or server computers in communication with one another over a near-field, local, wireless, wired, or wide-area computer network, such as the Internet, and at least one of the computers is configured to receive signals from sensors worn by a user."), the processor configured to: based on the determined second current state vector, determine whether a current state should be modified to achieve a desired goal state of the user by determining an error state vector (Garten ¶0862: "The difference between the user's target state and their current state may be represented as a vector. This vector can be used to select or recommend songs that may help the user achieve their target state."); and in response to determining that the current state should be modified, determine a second set of music parameters, for achieving the desired goal state of the user (Garten ¶0887: "The controller changes the effects its applies to the music data to be output by setting the parameters of the Music Processor. The controller uses feedback from the Biological Feature Extractor and the features of the music output from the Sonic Feature Extractor to change the parameters (music effect parameters) of the Music Processor to adapt to helping the user reach their desired Target State.").
Furthermore, Ehrlich teaches the speaker being configured to receive and play a generative music (Ehrlich § 2.1: "The music generation system’s input controls and the generated musical patterns are consequently emotion-related. The MIDI-pat terns are sent over a virtual MIDI path (MIDI Yoke) to software (ProTools 8 LE) hosting virtual instruments (Fig 2A(c)) that are then translated into sound (playback engine was an AVIDMBox3 Mini external soundcard)."); the first portion of generative music having the first set of musical parameters (Ehrlich § 2.1: "These control parameters were implemented such that they modulate the musical pattern according to five music structural components, namely: harmonic mode, tempo, rhythmic roughness, overall pitch, and relative loudness of subsequent notes. According to their settings, different musical patterns are generated."); receive, from the biosensor, the EEG data while the first portion of generative music is played by the speakers to the user (Ehrlich Fig. 1 caption: "During calibration, the user is exposed to automatically enerated patterns of affective music, and brain activity is measured simultaneously via EEG. EEG patterns are extracted and used to build a user-specific emotion model."); determine, by analyzing the EEG data, a second current state vector that characterizes the second current state of the user ((Ehrlich § 2.2.3: "During online application, the previously built LDA-model was used as a translator of EEG signals into input parameter settings for the automatic music generation system (Fig 3, bottom). Incoming EEG data (in segments of 4 sec with 87.5% overlap) were used for online feature extraction and classification."); and transmit the second portion of generative music to the speaker and collect, in real time, a second set of EEG data of the user measured while the second portion of generative music is being played to the user by the speakers (Ehrlich § 2.2.3: "Each score update (one per 0.5 sec) was subsequently translated into an update of the music generation system’s input parameters, with val = s and aro = s. Closing the loop was achieved by playing back the resulting musical patterns to the subject.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for generating music for an electronic device of Vincente by adding the EEG feedback, analysis, and active adaptation of Garten and Ehrlich to automate the system's interpretation of the user's reaction (Garten ¶0903).
Regarding claim 12, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 10 as discussed above.
Ehrlich further teaches that the processor is configured to determine the first set of musical parameters of the first soundscape based on the first current state vector (Ehrlich § 2.2.3: "During online application, the previously built LDA-model was used as a translator of EEG signals into input parameter settings for the automatic music generation system (Fig 3, bottom).").
Regarding claim 14, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 10 as discussed above.
Garten further teaches determining a current level of focus based on the first current state vector (Garten ¶0908: "The Controller uses the features from the Biological Feature Extractor to determine the current User State. In one example the User State can be described by four parameters: a) Valence (positive or negative emotion), b) Arousal, c) level of attention, and d) level of synchronization."), wherein the desired goal state is a desired level of focus (Garten ¶0442: "GOAL: FOCUS—how much concentration and distraction—measure of how well we are doing. System tries different variations of background music.").
Regarding claim 15, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 10 as discussed above.
Ehrlich further teaches that the processor is configured to determine the second set of musical parameters by a machine learning model (Ehrlich § 2.2.3: "During online application, the previously built LDA-model was used as a translator of EEG signals into input parameter settings for the automatic music generation system (Fig 3, bottom).").
Regarding claim 16, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 14 as discussed above.
Garten further teaches that the server is configured to: determine whether the current level of focus is improved (Garten ¶0436: "Brainwave detector monitors Caleb to ensure the new music has changed his focus/attention levels (PROCESS).").
Ehrlich further teaches: collect, in real time, the EEG data of the user to which the second portion of generative music is played (Ehrlich § 2.2.3: "Each score update (one per 0.5 sec) was subsequently translated into an update of the music generation system’s input parameters, with val = s and aro = s. Closing the loop was achieved by playing back the resulting musical patterns to the subject.").
Regarding claim 18, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 10 as discussed above.
Garten further teaches another sensor coupled to the server and configured to measure environmental data (Garten ¶0086: "One way to improve emotion detection with EEG is to add more sensors to read more data not available from the brain, or to incorporate data from other sensors on other devices that a user is also wearing. Sensors in other wearable technology devices can read things like: temperature; galvanic skin response; motion; heart-rate and pulse; muscle tension through electromyography."), and the processor is further configured to receive environmental data from the electronic device (Garten ¶0087: "Additional data can help make a stronger case for one emotion or another. For example: an EEG might be able to sense a negative reaction to stimuli, but without contextual information from the user—either from the user's participation in an app environment, or from additional sensor data gleaned from other devices or other sensors of the system of the present invention—it might be difficult for the system to “learn” what precipitated that negative response.") and a context- relevant interaction data indicative of the user interaction with the electronic device (Garten ¶0088: "The user can reject the system's prediction and correct it with their own experience. In this way, the accuracy of the models used to predict emotion can be improved through direct user manual over-ride, using other measures of physiology related to emotion, context of the user (e.g. get information on the current activity from the user's calendar) and their behaviour (e.g. they skip over songs by artist X and they choose to listen to songs by artist Y.)") and adjusting the second current state vector based on the received environmental data and the context-relevant interaction data (Garten ¶0087: "Additional data can help make a stronger case for one emotion or another. For example: an EEG might be able to sense a negative reaction to stimuli, but without contextual information from the user—either from the user's participation in an app environment, or from additional sensor data gleaned from other devices or other sensors of the system of the present invention—it might be difficult for the system to “learn” what precipitated that negative response.").
Claims 11 and 17 are rejected under 35 U.S.C. as unpatentable over Vincente in view of Garten, and further in view of Ehrlich and Beiser, to the extent understood.
Regarding claim 11, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 10 as discussed above.
Vincente (in view of Garten and further in view of Ehrlich) does not explicitly disclose that the processor is coupled to an audio effects database comprising a first plurality of audio effects, and that the processor is configured to generate the first portion of generative music characterized by the first set of musical parameters by combining the first plurality of audio stems with the first plurality of audio effects into a plurality of simultaneously played layers.
However, Beiser teaches that the processor is coupled to an audio effects database comprising a first plurality of audio effects (Beiser ¶0022: "The term 'preset' as used herein refers to one or more audio processing steps which have been previously defined and stored in a database preferably with metadata describing context of when the audio processing steps may be used. The 'preset' as used herein, is more general than a set of parameters for known audio software plug-ins e.g. equalization, compression, reverberation, and may specify a generalized audio processing function and/or combination of previously defined audio processing functions."), and that the processor is configured to generate the first portion of generative music characterized by the first set of musical parameters by combining the first plurality of audio stems with the first plurality of audio effects into a plurality of simultaneously played layers (Beiser ¶0026: "Referring back to FIG. 2, after recommendation 207 is selected in step 25, original audio tracks 201 may be processed (step 212) according to recommendation 207. The processed audio tracks may be mixed (step 214) into a playable audio production and the audio production may be played (step 216).").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for generating music for an electronic device of Vincente (as modified by Garten and Ehrlich) by adding the effects database and effects combinations of Beiser in order to provide a playable digital file with a pleasant audio experience (Beiser ¶0020).
Regarding claim 17, Vincente (in view of Garten and further in view of Ehrlich and Beiser) teaches a system comprising the features of claim 11 as discussed above.
Garten further teaches that the processor is further configured to determine a third set of music parameters of a third portion of the generative music (Garten ¶0887: "The controller changes the effects its applies to the music data to be output by setting the parameters of the Music Processor. The controller uses feedback from the Biological Feature Extractor and the features of the music output from the Sonic Feature Extractor to change the parameters (music effect parameters) of the Music Processor to adapt to helping the user reach their desired Target State.") which is more susceptible to force an improvement of a level of focus (Garten ¶0436: "Brainwave detector monitors Caleb to ensure the new music has changed his focus/attention levels (PROCESS).").
Ehrlich further teaches: transition the generative music automatedly generated into a third portion of generative music based on the third set of music parameters (Ehrlich § 2.1: "By gradually changing the input parameters, the algorithm’s generated music patterns flow seamlessly and gradually change in terms of emotional expressiveness."), and the system is further configured to play the third portion of generative music to the user generated based on the third set of music parameters (Ehrlich § 2.2.3: "Closing the loop was achieved by playing back the resulting musical patterns to the subject.").
Claim 13 is rejected under 35 U.S.C. 103 as unpatentable over Vincente in view of Garten, and further in view of Ehrlich and Williams, to the extent understood.
Regarding claim 13, Vincente (in view of Garten and further in view of Ehrlich) teaches a system comprising the features of claim 10 as discussed above.
Vincente (in view of Garten and further in view of Ehrlich) does not explicitly disclose that determining the second set of musical parameters of the second soundscape is based on a vectorial difference between a goal set of musical parameters and a current set of musical parameters, determined from another vectorial difference between the goal state vector and the second current state vector.
However, Williams teaches that determining the second set of musical parameters of the second soundscape is based on a vectorial difference between a goal set of musical parameters and a current set of musical parameters (Williams § 2.1: The generated musical state sequences are subsequently further transformed according to the distance between the current features and the features which correlate to a given affective target (the transformations indicating the affective correlates are shown in Table 1)."), determined from another vectorial difference between the goal state vector and the second current state vector (Garten ¶0442: "GOAL: FOCUS—how much concentration and distraction—measure of how well we are doing. System tries different variations of background music.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for generating music for an electronic device of Vincente (as modified by Garten and Ehrlich) by adding the musical feature interpolation of Williams to calibrate the musical feature set on a case-by-case basis (Williams § 3.1).
Conclusion
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/PHILIP G SCOLES/
Examiner, Art Unit 2837
/DEDEI K HAMMOND/Supervisory Patent Examiner, Art Unit 2837
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