DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 Amendment
2. According to the Amendment, filed 05 March 2026, the status of the claims is as follows:
Claims 1, 4, 7, 9, 10, 12, 15, and 20 are currently amended; and
Claims 2, 3, 5, 6, 8, 11, 13, 14, 16-19, and 21 are as originally filed.
3. The objection to claims 1 and 12 are withdrawn in view of the Amendment, filed 05 March 2026.
4. The rejection of claims 7, 8, 18, and 19 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or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention, is withdrawn in view of the Amendment, filed 05 March 2026.
Response to Arguments
5. Applicant’s arguments, see Remarks, pp. 5-6, filed 05 March 2026, with respect to the rejection of claims 1-21 under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more, have been fully considered, and are persuasive. The rejection of claims 1-21 has been withdrawn.
Applicant contends, see Remarks, pp. 5-6, the following:
Applicant submits that any judicial exceptions (of which Applicant does not concede there are any) are integrated into practical application.
An improvement in the functioning of a computer, or an improvement to other technology or technical field integrate judicial exceptions into practical applications (MPEP, S. 2106.04(d). I. RELEVANT CONSIDERATIONS FOR EVALUATING WHETHER ADDITIONAL ELEMENTS INTEGRATE A JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION).
Claim 2 of Example 46 of the October 2019 Update: Subject Matter Eligibility issued by the USPTO (accessible online: <https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_app1.pdf- was found eligible under Step 2A Prong Two because though the claim included judicial exceptions, the final limitation (d) (automatically sending a control signal to a feed dispenser, if an animal exhibited an aberrant behavioral pattern) added a meaningful limitation in that it can employ the information provided by the judicial exception to operate the feed dispenser and that the limitation did not merely link the judicial exception to a technical field.
As described above, applying a music effect to the music data is an additional limitation. The information from any judicial exceptions recited the claim is being employed to apply the music effect. Accordingly, the claims integrate any judicial exception into practical application.
This argument was found persuasive. Examiner agrees that the step of applying a music effect to the music data based on a recommendation generates based on the first user response, the second user response, and a target state using a predictive model, integrates the abstract idea into a practical application.
6. Applicant’s arguments, see Remarks, p. 7, filed 05 March 2026, with respect to the rejection of claims 1-21 under 35 U.S.C. 102(a)(1) as being anticipated by Osborne et al., U.S. Patent Application Publication No. 2014/0307878 A1 (“Osborne”), have been fully considered, have been fully considered, but they are not persuasive.
Applicant contends, see Remarks, p. 7, the following:
The claims have been clarified to state that a music effect is applied to the music data based on a recommendation generated based on the first user response, the second user response, and a target state. The music effects might include, for example, changes of tempo, filtering (changing the amplification level of different frequency bands in the music), changes in left/right balance, reverberation, spatialization, room models, pitch shifting, chirp effects, vocoder (human voice as notes), auto-tune, auto detune, humanize electronic music, changes to the order of samples of music (i.e. the chorus and verses may be ordered in different ways to change the order the music is played but the piece is still recognizable), and changes to a profile of tension and release over the course of a piece of music.
Osborne discloses a method and system for analyzing audio (e.g., music) tracks (Osborne, abstract). Specifically, Osborne states that it does not require modulation of tempo, nor composition of psycho-acoustically correct, synthetic music to achieve its effect (Osborne, paragraph [0021]). Furthermore, Osborne describes selecting previously categorized music from a database to achieve target states (paragraph [0183]; see also paragraph [0170). Osborne teaches selecting previously categorized music. Osborne fails to trach applying music effects to music data. Accordingly, Osborne does not disclose "apply a music effect to the music data based on a recommendation generated based on the first user response, the second user response, and a target state using a predictive model" as now claimed.
Applicant asserts substantially the same arguments for claim 12.
However, respectfully, this argument is not persuasive. Based on broadest reasonable interpretation, Osborne teaches the claimed subject matter of “apply a music effect to the music data based on a recommendation generated based on the first user response, the second user response, and a target state using a predictive model”. In the Remarks, p. 7, filed 05 March 2026, Applicant states that “The music effects might include, for example, … changes to the order of samples of music”. See also Specification, para. [00890], filed 11 September 2023 (“The following is a list of Music Effects that the Music Processor can apply: … the order of samples of music may be changed …”). Osborne does in fact teach the following:
apply a music effect to the music data based on a recommendation (see “… music playback/streaming (eg. playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software. Specific tracks for a listener may be selected for playback (by streaming or otherwise) according to bio-feedback from that listener; the playlist may be created locally and the music tracks requested for streaming/download etc; it is possible also for the bio-feedback and desired "state" information to be sent to a remote music server and for that server to generate the appropriate playlist and provide music tracks to the local, personal playback device.” in para. [0183]; and see “The first piece of music selected will correspond to the initial neuro-physiological state of the subject, represented by E. Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]) generated based on the first user response, the second user response, and a target state using a predictive model (see “As noted above, X-System may use a subject's biometric data (where a sensor is available) to measure neuro-physiological arousal. It then leads the subject by stages towards a target level of such arousal, state of mind and/or affect. This is achieved with a database of music, previously categorised using predictive modelling of innate neuro-physiological responses. Categorisation in real-time or near real-time is also possible. Categorisation can be visually displayed (e.g. on the display of the computing device used for music playback); this can include a display of the E values for each music track, or how the E (Excitement) value changes during a track; R, I, H, C and T parameters can also be visually displayed. A piece of music that predicts or matches the subject's current level of neuro-physiological arousal is selected and a playlist constructed on the basis of the fundamental musical effect of each constituent piece of music. Listening to the playlist directs or moves the user towards the desired level of arousal, state of mind and/or affect by unconscious neuro-physiological entrainment with the music and enables that level to be maintained. The subject's current level of neuro-physiological arousal can also be visually represented, as can the convergence to the desired target state.” in para. [0170]; and see “… This may be based on Nigel Osborne's INRM (Innate Neuro-physiological Response to Music) paradigm. [0180] a database of music categorised manually or automatically (using the automatic categorisation software) to achieve specific levels of arousal and counterarousal [0181] sensors to detect physiological indicators of arousal (such as excitement) and counterarousal (such as drowsiness), including heart rate and galvanic skin conductance [0182] diagnostic software which employs sensor data to monitor levels of arousal and counterarousal in the user” in para. [0178]-[0182]).
Osborne teaches applying a music effect, i.e. order of samples of music, as selecting an order of pieces of music in a playlist. Thus, the rejection is maintained for this reason.
Claim Objections
7. Claim 4 is objected to because of the following informalities:
in lines 1-2, “a music effect” lacks proper antecedent basis, and should be amended to “the [[a ]]music effect”.
Appropriate correction is required.
8. Claim 9 is objected to because of the following informalities:
in line 2, “the the at least one computing device” is a typographical error, and should be amended to “the .
Appropriate correction is required.
9. Claim 15 is objected to because of the following informalities:
in lines 1-2, “a music effect” lacks proper antecedent basis, and should be amended to “the [[a ]]music effect”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
10. 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.
11. Claims 1-21 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.
Claims 1 recites the limitation “apply a music effect to the music data based on a recommendation generated based on the first user response, the second user response, and a target state using a predictive model”, and Claim 12 recites the limitation “applying a music effect to the music data based on the recommendation”. However, it is not clear how the predictive model uses the two user responses and target state to determine the music effect.
Claims 2-11 and 13-21 are rejected due to their dependencies, either directly or indirectly, to the respective base claims.
Claim Rejections - 35 USC § 102
12. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
13. Claims 1-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Osborne et al., U.S. Patent Application Publication No. 2014/0307878 A1 (“Osborne”).
As to Claim 1, Osborne teaches the following:
An intelligent music system (see “The present invention relates to a method and system for analysing sound (e.g. music tracks). Tracks from a database of sounds, for example music, can be analysed in order to predict automatically the effect or impact those sounds will have on a listener.” in para. [0002]) comprising:
at least one bio-signal sensor (“sensor”, not labeled) to capture bio-signal data about at least one user (see “The sensor may be in the form of a wristband, a hand-held or any other device suitable for taking the required parameter measurements. The sensor may be body-mounted, or use ear buds (e.g. combining a sensor into ear-bud headphones), remote monitoring via IR or acoustic, wireless, or more generally any form of life sensing. The data captured preferably comprises biometric parameters such as heart rate (including pulse rhythm analysis), blood pressure, adrenaline and oxytocin levels, muscular tension, brain waves and galvanic skin conductivity. Alternative equipment formats include necklaces, bracelets, sensors embedded in clothing, other jewellery, sensors implanted under skin, headsets, earphones, sensors in handheld form such as covers for `phones, MP3 players, or other mobile computing devices.” in para. [0198]);
an output (“music player”, not labeled) to output music data (see “The music player may be an adaptation of standard industry software such as the Windows Media Player which is capable of building dynamic playlists according to the Musical Selection Algorithms and of offering the user additional utility such as selection of musical style, display of associated metadata and video content.” in para. [0214]), the music data (“musical excerpts”, “each piece of music”, or “music tracks”) comprising at least a first music data item (“each music excerpt”) and a second music data item (“each music excerpt”) (see “The overall arousal index calculated for each piece of music may be expressed either as a single number that describes the overall neurophysiological effect of listening to it from start to finish, or it can be displayed graphically with arousal index on the vertical axis and time on the horizontal axis. The resulting trace would effectively describe the neurophysiological journey a listener may expect as they listen from beginning to end. This latter is likely to be of particular use in longer and more complex pieces of music such as much of the classical repertoire, whereas some other repertoire such as modern Western pop music might more conveniently be represented by a single number. In either case, the effect of a piece of music is both inherent (in that it is a product of the patterns detected in the music) and dependent on the state of the listener (in that the neurophysiological effect of music is relative rather than absolute [Altshuler `The Iso-Moodic Principle` 1948]).” in para. [0133]; and see “The concatenation of p and q allows each musical excerpt to be mapped onto a Musical Effect Matrix M, a 2*n dimensional matrix, n dimensions corresponding to the physiological parameters measured by E representing granular ranges into which E can fall, the other n dimensions corresponding to the effect the track will have on the physiological parameters (ascending, descending or maintaining any given physiological parameter or dimension of E).” in para. [0146]); and
at least one computing device (“portable computing device”, not labeled) in communication with the least one bio-signal sensor (see “These implementations may enable real-time analysis of music tracks and other sounds, all done locally within a portable computing device such as a smartphone or tablet, or remotely on a server, or some combination of distributed local and server based processing.” in para. [0036]; and see “Note that all software may also be implemented in hardware, firmware, SoC, as part of a third party audio stack and in any other convenient manner.” in para. [0184]) to continuously receive bio-signal data comprising brainwave data of the at least one user (see “The data captured preferably comprises biometric parameters such as heart rate (including pulse rhythm analysis), blood pressure, adrenaline and oxytocin levels, muscular tension, brain waves and galvanic skin conductivity.” in para. [0198]), the at least one computing device configured to:
detect a first user response associated with the first music data item, the first user response based on bio-signal data captured during playback of the first music data item (see “The concatenation of p and q allows each musical excerpt to be mapped onto a Musical Effect Matrix M, a 2*n dimensional matrix, n dimensions corresponding to the physiological parameters measured by E representing granular ranges into which E can fall, the other n dimensions corresponding to the effect the track will have on the physiological parameters (ascending, descending or maintaining any given physiological parameter or dimension of E).” in para. [0146]);
detect a second user response associated with the second music data item, the second user response based on bio-signal data captured during playback of the second music data item (see “The concatenation of p and q allows each musical excerpt to be mapped onto a Musical Effect Matrix M, a 2*n dimensional matrix, n dimensions corresponding to the physiological parameters measured by E representing granular ranges into which E can fall, the other n dimensions corresponding to the effect the track will have on the physiological parameters (ascending, descending or maintaining any given physiological parameter or dimension of E).” in para. [0146]); and
apply a music effect to the music data based on a recommendation (see “… music playback/streaming (eg. playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software. Specific tracks for a listener may be selected for playback (by streaming or otherwise) according to bio-feedback from that listener; the playlist may be created locally and the music tracks requested for streaming/download etc; it is possible also for the bio-feedback and desired "state" information to be sent to a remote music server and for that server to generate the appropriate playlist and provide music tracks to the local, personal playback device.” in para. [0183]; and see “The first piece of music selected will correspond to the initial neuro-physiological state of the subject, represented by E. Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]) generated based on the first user response, the second user response, and a target state using a predictive model (see “As noted above, X-System may use a subject's biometric data (where a sensor is available) to measure neuro-physiological arousal. It then leads the subject by stages towards a target level of such arousal, state of mind and/or affect. This is achieved with a database of music, previously categorised using predictive modelling of innate neuro-physiological responses. Categorisation in real-time or near real-time is also possible. Categorisation can be visually displayed (e.g. on the display of the computing device used for music playback); this can include a display of the E values for each music track, or how the E (Excitement) value changes during a track; R, I, H, C and T parameters can also be visually displayed. A piece of music that predicts or matches the subject's current level of neuro-physiological arousal is selected and a playlist constructed on the basis of the fundamental musical effect of each constituent piece of music. Listening to the playlist directs or moves the user towards the desired level of arousal, state of mind and/or affect by unconscious neuro-physiological entrainment with the music and enables that level to be maintained. The subject's current level of neuro-physiological arousal can also be visually represented, as can the convergence to the desired target state.” in para. [0170]; and see “… This may be based on Nigel Osborne's INRM (Innate Neuro-physiological Response to Music) paradigm. [0180] a database of music categorised manually or automatically (using the automatic categorisation software) to achieve specific levels of arousal and counterarousal [0181] sensors to detect physiological indicators of arousal (such as excitement) and counterarousal (such as drowsiness), including heart rate and galvanic skin conductance [0182] diagnostic software which employs sensor data to monitor levels of arousal and counterarousal in the user” in para. [0178]-[0182]).
As to Claim 2, Osborne teaches the following:
wherein the at least one computing device configured to update an order of music data items within the music data based on the first and second user responses (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 3, Osborne teaches the following:
wherein the at least one computing device is further configured to update the predictive model based on the first user response, the second user response, and a target state (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 4, Osborne teaches the following:
wherein the predictive model selects a music effect using the first user response, the second user response, and results from the music effect on users with similar user responses (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 5, Osborne teaches the following:
wherein the target state is a target state profile (“target states of mind and body”) that changes during the playback of the music data (see “… music playback/streaming (eg. Playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software. Specific tracks for a listener may be selected for playback (by streaming or otherwise) according to bio-feedback from that listener; the playlist may be created locally and the music tracks requested for streaming/download etc; it is possible also for the bio-feedback and desired "state" information to be sent to a remote music server and for that server to generate the appropriate playlist and provide music tracks to the local, personal playback device.” in para. [0183]).
As to Claim 6, Osborne teaches the following:
wherein the target state profile includes at least one of a tension state and a release state (see “…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.” in para. [0022]); and see “… to achieve target states such as: [0172] Excitement [0173] Relaxation [0174] Concentration [0175] Alertness [0176] Potentiation of physical activity” in para. [0171]).
As to Claim 7, Osborne teaches the following:
wherein the system probes the at least one user by inserting audio stimulus into the music data and assessing a probe user response to the audio stimulus (see “Any given set of algorithms will be dependent on the stimulus being modelled and the biometric by which the effect of the stimulus is to be measured, but, even given constant parameters, there are a number of valid mathematical approaches: the specific algorithms we describe in this specification themselves are therefore not the most fundamental feature of the invention, even though most algorithms in the system are unique in conception and implementation.” in para. [0020]; and see “Linear harmonic cost is expressed in terms of cost per second. The metric therefore represents both the rate at which the fundamental is changing, and the harmonic distance of the changes. Higher numbers indicate a more stimulating effect.” in para. [0099]).
As to Claim 8, Osborne teaches the following:
wherein inserting the audio stimulus into the music data comprises inserting a portion of the second music data item into the first music data item (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 9, Osborne teaches the following:
the the at least one computing device (“portable computing device”, not labeled) further configured to apply a song transition from the second music data item to a third music data item (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 10, Osborne teaches the following:
wherein the at least one bio-signal sensor comprises at least one brainwave sensor (“EEG”, not labeled) for detecting brainwave signals (see “Other sensors include physical bio-sensors such as oxygenation, EDA, EDC, EDR, ECG, sugar levels, BPM, EEG etc, and multi-spectrum sensors (radio, IR, UV, heat, and broad spectrum), which detect bodily radiation auras.” in para. [0200]), and the music effect is selected to entrain the music data with the brainwave signals (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 11, Osborne teaches the following:
memory (“database”, not labeled) storing a database of music data associated with emotions (see “[0179] … This may be based on Nigel Osborne's INRM (Innate Neuro-physiological Response to Music) paradigm. [0180] a database of music categorised manually or automatically (using the automatic categorisation software) to achieve specific levels of arousal and counterarousal [0181] sensors to detect physiological indicators of arousal (such as excitement) and counterarousal (such as drowsiness), including heart rate and galvanic skin conductance [0182] diagnostic software which employs sensor data to monitor levels of arousal and counterarousal in the user [0183] music playback/streaming (eg. playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software.” in para. [0179]-[0183]);
wherein the at least one computing device is configured to generate a recommended music data item from the database based on one or more of the first user response, second user response, and the target state (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 12, Osborne teaches the following:
A method of applying effects to music data (see “The present invention relates to a method and system for analysing sound (e.g. music tracks). Tracks from a database of sounds, for example music, can be analysed in order to predict automatically the effect or impact those sounds will have on a listener.” in para. [0002]), the method comprising:
playing a first music data item (“each music excerpt”, not labeled) (see “The music player may be an adaptation of standard industry software such as the Windows Media Player which is capable of building dynamic playlists according to the Musical Selection Algorithms and of offering the user additional utility such as selection of musical style, display of associated metadata and video content.” in para. [0214]);
detecting a first user response associated with the first music data item, the first user response based on bio-signal data captured during playback of the first music data item (see “The concatenation of p and q allows each musical excerpt to be mapped onto a Musical Effect Matrix M, a 2*n dimensional matrix, n dimensions corresponding to the physiological parameters measured by E representing granular ranges into which E can fall, the other n dimensions corresponding to the effect the track will have on the physiological parameters (ascending, descending or maintaining any given physiological parameter or dimension of E).” in para. [0146]);
playing a second music data item (“each music excerpt”, not labeled) (see “The music player may be an adaptation of standard industry software such as the Windows Media Player which is capable of building dynamic playlists according to the Musical Selection Algorithms and of offering the user additional utility such as selection of musical style, display of associated metadata and video content.” in para. [0214]);
detecting a second user response associated with the second music data item, the second user response based on bio-signal data captured during playback of the second music data item (see “The concatenation of p and q allows each musical excerpt to be mapped onto a Musical Effect Matrix M, a 2*n dimensional matrix, n dimensions corresponding to the physiological parameters measured by E representing granular ranges into which E can fall, the other n dimensions corresponding to the effect the track will have on the physiological parameters (ascending, descending or maintaining any given physiological parameter or dimension of E).” in para. [0146]); and
generating a recommendation based on the first user response, the second user response, and a target state using a predictive model (see “As noted above, X-System may use a subject's biometric data (where a sensor is available) to measure neuro-physiological arousal. It then leads the subject by stages towards a target level of such arousal, state of mind and/or affect. This is achieved with a database of music, previously categorised using predictive modelling of innate neuro-physiological responses. Categorisation in real-time or near real-time is also possible. Categorisation can be visually displayed (e.g. on the display of the computing device used for music playback); this can include a display of the E values for each music track, or how the E (Excitement) value changes during a track; R, I, H, C and T parameters can also be visually displayed. A piece of music that predicts or matches the subject's current level of neuro-physiological arousal is selected and a playlist constructed on the basis of the fundamental musical effect of each constituent piece of music. Listening to the playlist directs or moves the user towards the desired level of arousal, state of mind and/or affect by unconscious neuro-physiological entrainment with the music and enables that level to be maintained. The subject's current level of neuro-physiological arousal can also be visually represented, as can the convergence to the desired target state.” in para. [0170]; and see “… This may be based on Nigel Osborne's INRM (Innate Neuro-physiological Response to Music) paradigm. [0180] a database of music categorised manually or automatically (using the automatic categorisation software) to achieve specific levels of arousal and counterarousal [0181] sensors to detect physiological indicators of arousal (such as excitement) and counterarousal (such as drowsiness), including heart rate and galvanic skin conductance [0182] diagnostic software which employs sensor data to monitor levels of arousal and counterarousal in the user” in para. [0178]-[0182]);
applying a music effect to the music data based on the recommendation (see “… music playback/streaming (eg. playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software. Specific tracks for a listener may be selected for playback (by streaming or otherwise) according to bio-feedback from that listener; the playlist may be created locally and the music tracks requested for streaming/download etc; it is possible also for the bio-feedback and desired "state" information to be sent to a remote music server and for that server to generate the appropriate playlist and provide music tracks to the local, personal playback device.” in para. [0183]; and see “The first piece of music selected will correspond to the initial neuro-physiological state of the subject, represented by E. Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 13, Osborne teaches the following:
updating an order of music data items within the music data based on the first and second user responses (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 14, Osborne teaches the following:
updating the predictive model based on the first user response, the second user response, and a target state (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 15, Osborne teaches the following:
wherein the predictive model selects a music effect using the first user response, the second user response, and results from the music effect on users with similar user responses (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 16, Osborne teaches the following:
wherein the target state is a target state profile (“target states of mind and body”) that changes during the playback of the music data (see “… music playback/streaming (eg. Playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software. Specific tracks for a listener may be selected for playback (by streaming or otherwise) according to bio-feedback from that listener; the playlist may be created locally and the music tracks requested for streaming/download etc; it is possible also for the bio-feedback and desired "state" information to be sent to a remote music server and for that server to generate the appropriate playlist and provide music tracks to the local, personal playback device.” in para. [0183]).
As to Claim 17, Osborne teaches the following:
wherein the target state profile includes at least one of a tension state and a release state (see “…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.” in para. [0022]); and see “… to achieve target states such as: [0172] Excitement [0173] Relaxation [0174] Concentration [0175] Alertness [0176] Potentiation of physical activity” in para. [0171]).
As to Claim 18, Osborne teaches the following:
probing the user by inserting audio stimulus into the music data and assessing a probe user response to the audio stimulus (see “Any given set of algorithms will be dependent on the stimulus being modelled and the biometric by which the effect of the stimulus is to be measured, but, even given constant parameters, there are a number of valid mathematical approaches: the specific algorithms we describe in this specification themselves are therefore not the most fundamental feature of the invention, even though most algorithms in the system are unique in conception and implementation.” in para. [0020]; and see “Linear harmonic cost is expressed in terms of cost per second. The metric therefore represents both the rate at which the fundamental is changing, and the harmonic distance of the changes. Higher numbers indicate a more stimulating effect.” in para. [0099]).
As to Claim 19, Osborne teaches the following:
wherein inserting the audio stimulus into the music data comprises inserting a portion of the second music data item into the first music data item (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 20, Osborne teaches the following:
applying a song transition from the second music data item to a third music data item (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
As to Claim 21, Osborne teaches the following:
generating a recommended music data item from a database of music data associated with emotions (see “[0179] … This may be based on Nigel Osborne's INRM (Innate Neuro-physiological Response to Music) paradigm. [0180] a database of music categorised manually or automatically (using the automatic categorisation software) to achieve specific levels of arousal and counterarousal [0181] sensors to detect physiological indicators of arousal (such as excitement) and counterarousal (such as drowsiness), including heart rate and galvanic skin conductance [0182] diagnostic software which employs sensor data to monitor levels of arousal and counterarousal in the user [0183] music playback/streaming (eg. playlist selection) software which selects previously categorised music from a database to stream appropriate repertoire to achieve target states of mind and body by a process of step-by-step entrainment, starting from the current diagnosed "state"; progress towards these goals is monitored by the diagnostic software.” in para. [0179]-[0183]) based on one or more of the first user response, second user response, and the target state (see “Subsequent pieces are selected based on their values in M such that each would, played in order, be capable of progressively leading the subject's state towards the target state. The order in which the pieces of music are eligible to be included in a playlist is determined by a vector that represents a temporally-organised ascending, or descending as appropriate, series of musical effect values in M. The set of pieces of music in the database that meet the requirements of this series of effect values is known as `Qualified Content`.” in para. [0208]).
Conclusion
14. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 04/27/2026
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