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 .
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it recites “computer program product”. A “computer program product” is computer program or software which does not fall into one the statutory categories.
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 1-13, 19-20 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. Independent claim 1 recites “A method for music recommendation based on a wearable device, comprising:” and “determining, by the processor, at least one piece of target recommendation music based on at least one of or relaxation parameters corresponding to multiple pieces of music to be recommended” (emphasis added). It is not clear how “music recommendation” based on “wearable device” relates to “target recommendation music” based on “relaxation parameters corresponding to multiple pieces of music”.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Morishima (US Patent No. 9,978,358), and further in view of Garten (US Patent Application Publication No. 2015/0297109).
Regarding claim 1, Morishima teaches a method for music recommendation based on a wearable device, comprising:
obtaining one or more physiological parameters of a target user collected by the wearable device (col. 3 ll. 51-63, col. 12 ll. 6-13);
processing, by a processor, the one or more physiological parameters of the target user with a relaxation state assessment to determine a current relaxation state of the target user (col. 5 ll. 13-30, col. 12 ll. 13-16); and
determining, by the processor, at least one piece of target recommendation music based on at least one of the current relaxation state of the target user
Morishima teaches estimating various sleep (relaxation) states of the user but Morishima does not explicitly teach it to be based on assessment model.
However, in the similar field, Garten teaches using an assessment model to estimates relaxation state of the user (Paragraphs 0006, 0062, 0098-0102 , 0129-0130, 0141 Brain State Classification model, 0146-0149, 0173-0174, 0185, 0193 brain state classification, 0502-0513, 0561-0564, 0568-0569, 0825, 0852, 0922-0932) and determine target recommendation music (Paragraphs 0006, 0012-0013, 0104 music based on emotional/relaxation state, 0150-0155, 0160, 0163-0167 music recommendation based on characteristics of music, 0175-0177, 0188, 0218, 0244-0250, 0414-0425, 0469-0478, 0570-572) (Paragraphs 0005-0039, 0012-0013, 0094-0640 for details on many scenarios of brain wave/relaxation state and corresponding music recommendation/selection scenarios).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Morishima to include using an assessment model to estimates relaxation state of the user and determine target recommendation music as taught by Garten in order to facilitate “classification model”….”based on prior samples of EEG generated by the user when listening to a sound” (Garten, Paragraph 0141) and “recommend a particular song associated with the same emotion presently being experienced by the user” (Garten, Abstract).
Regarding claim 2, Garten teaches the determining, by the processor, at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended comprises: ranking the multiple pieces of music to be recommended in a descending order based on the relaxation parameters corresponding to the multiple pieces of music to be recommended; determining at least one piece of target recommendation music from the multiple pieces of music to be recommended according to the ranking of the multiple pieces of music to be recommended (Paragraphs 0267-0275 ranked order in playlist recommendation and playing one of the music).
Regarding claim 3, Morishima teaches the determining, by the processor, at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended comprises: obtaining at least one previous relaxation state of the target user; and determining at least one target recommendation music based at least in part on a comparison of the current relaxation state and the at least one previous relaxation state of the target user (col. 11 ll. 39-48, col. 11 ll. 55-col. 17 ll. 29 music according to desired state transition).
Garten teaches the determining, by the processor, at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended comprises: obtaining at least one previous relaxation state of the target user; and determining at least one target recommendation music based at least in part on a comparison of the current relaxation state and the at least one previous relaxation state of the target user (Paragraphs 0104, 0469-0481, 0490-0493, 0530-0539, 0567-0574, 0856-0863).
Regarding claim 4, Morishima teaches the determining, by the processor, at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended comprises: in response to determining that the target user is more relaxed based on the current relaxation state and at least one previous relaxation state, determining at least one piece of target recommendation music based at least in part on a piece of music currently being played for the target user (Fig. 13 for each sub-interval check of time t2 “light sleep” continuing same hypersonic and natural sound music), or
Garten teaches the determining, by the processor, at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended comprises: or in response to determining that the target user is not more relaxed based on the current relaxation state and the at least one previous relaxation state, determining at least one piece of target recommendation music based on the relaxation parameters corresponding to the multiple pieces of music to be recommended (Paragraphs 0571-0572 music to induce sleep on wake-up).
Regarding claim 5, Garten teaches the relaxation state assessment model is generated with a first training data set, and the first training data set comprises physiological parameters of multiple reference users and relaxation states annotated based on EEG data of the multiple reference users (Paragraphs 0141, 0497-0513).
Regarding claim 6, Garten teaches before determining the at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended: obtaining at least one of music preferences, attribute information, or historical sleep data of the target user; determining a target music style corresponding to the target user based on at least one of the music preferences, attribute information, or historical sleep data of the target user (Paragraphs 0096-0103, 0112-0116, 0141, 0146-0153, 0160, 0163-0166, 0170-0173, 0184-0188); and determining multiple pieces of candidate music belonging to the target music style from a candidate music library as the multiple pieces of music to be recommended (Paragraphs 0154, 0167, 0174-0177, 0176-0177, 0193).
Regarding claim 7, Morishima teaches before determining the at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended: obtaining music features of multiple pieces of music to be recommended (Fig. 3 tempo, beat, volume etc.); and determining the relaxation parameters corresponding to the multiple pieces of music to be recommended with a relaxation parameter estimation model based on the music features of multiple pieces of music to be recommended and at least one of attribute information of the target user or historical music playback data of the target user (Figs. 3, 13, col. 5 ll.46, col. 6 ll. 49, col. 15 ll. 9-col. 16 ll. 58 determining music to be played based on existing and desired states).
Garten teaches before determining the at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended: obtaining music features of multiple pieces of music to be recommended (Paragraphs 0661-663, 0824-0825, 0867); and determining the relaxation parameters corresponding to the multiple pieces of music to be recommended with a relaxation parameter estimation model based on the music features of multiple pieces of music to be recommended and at least one of attribute information of the target user or historical music playback data of the target user (Paragraphs 0813-0910 music features and relaxation parameters associated and used to select music to achieve user desired state).
Regarding claim 9, Morishima teaches wherein the at least one piece of target recommendation music is one piece of target recommendation music; and wherein the method further comprises: after determining the at least one target recommendation music, merging the target recommendation music with a piece of music currently being played for the target user to obtain a piece of merged music, wherein the merged music is played for the target user before playing the target recommendation music (Fig. 13 for target state “LIGHT SLEEP”, playing merged “BINAURAL BEAT” and ”NATURAL SOUND” before t2 and playing only ”NATURAL SOUND” after t2; for target state “DEEP SLEEP”, playing merged “HYPERSONIC” and ”NATURAL SOUND” before t3 and playing only ” HYPERSONIC” after t3).
Regarding claim 14, Morishima teaches an electronic device (Fig. 1 item 20), comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and execution of the instructions by the at least one processor enables the at least one processor (col. 4 ll. 5-39) to:
obtain one or more physiological parameters of a target user collected by a wearable device (col. 3 ll. 51-63, col. 12 ll. 6-13);
process the one or more physiological parameters of the target user with a relaxation state assessment to determine a current relaxation state of the target user (col. 5 ll. 13-30, col. 12 ll. 13-16); and
determine at least one piece of target recommendation music based on at least one of the current relaxation state of the target user
Morishima teaches estimating various sleep (relaxation) states of the user but Morishima does not explicitly teach it to be based on assessment model.
However, in the similar field, Garten teaches using an assessment model to estimates relaxation state of the user (Paragraphs 0006, 0062, 0098-0102 , 0129-0130, 0141 Brain State Classification model, 0146-0149, 0173-0174, 0185, 0193 brain state classification, 0502-0513, 0561-0564, 0568-0569, 0825, 0852, 0922-0932) and determine target recommendation music (Paragraphs 0006, 0012-0013, 0104 music based on emotional/relaxation state, 0150-0155, 0160, 0163-0167 music recommendation based on characteristics of music, 0175-0177, 0188, 0218, 0244-0250, 0414-0425, 0469-0478, 0570-572) (Paragraphs 0005-0039, 0012-0013, 0094-0640 for details on many scenarios of brain wave/relaxation state and corresponding music recommendation/selection scenarios).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Morishima to include using an assessment model to estimates relaxation state of the user and determine target recommendation music as taught by Garten in order to facilitate “classification model”….”based on prior samples of EEG generated by the user when listening to a sound” (Garten, Paragraph 0141) and “recommend a particular song associated with the same emotion presently being experienced by the user” (Garten, Abstract).
Regarding claim 15, Morishima teaches wherein the electronic device is the wearable device, a mobile terminal wirelessly communicating with the wearable device (col. 4 ll. 10-14), or a remote server wirelessly communicating with the mobile terminal.
Garten teaches wherein the electronic device is the wearable device, a mobile terminal wirelessly communicating with the wearable device (Paragraphs 0058, 0060, 0852 headset/ headband in communication with mobile phone), or a remote server wirelessly communicating with the mobile terminal (Paragraphs 0061-0062 sensor and state data from mobile application to cloud).
Regarding claim 16, Garten teaches wherein the electronic device communicates wirelessly with the wearable device in a short range (Paragraphs 0058, 0060, 0852 headset/ headband in communication with mobile phone, near-field or wireless).
Regarding claim 17, Garten teaches wherein before determining the at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended: obtain at least one of music preferences, attribute information, or historical sleep data of the target user; determine a target music style corresponding to the target user based on at least one of the music preferences, attribute information, or historical sleep data of the target user (Paragraphs 0096-0103, 0112-0116, 0141, 0146-0153, 0160, 0163-0166, 0170-0173, 0184-0188); and determine multiple pieces of candidate music belonging to the target music style from a candidate music library as the multiple pieces of music to be recommended (Paragraphs 0154, 0167, 0174-0177, 0176-0177, 0193).
Regarding claim 18, Morishima teaches before determining the at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended: obtain music features of multiple pieces of music to be recommended (Fig. 3 tempo, beat, volume etc.); and determine the relaxation parameters corresponding to the multiple pieces of music to be recommended with a relaxation parameter estimation model based on the music features of multiple pieces of music to be recommended and at least one of attribute information or historical music playback data of the target user (Figs. 3, 13, col. 5 ll.46, col. 6 ll. 49, col. 15 ll. 9-col. 16 ll. 58 determining music to be played based on existing and desired states).
Garten teaches before determining the at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended: obtain music features of multiple pieces of music to be recommended (Paragraphs 0661-663, 0824-0825, 0867); and determine the relaxation parameters corresponding to the multiple pieces of music to be recommended with a relaxation parameter estimation model based on the music features of multiple pieces of music to be recommended and at least one of attribute information of the target user or historical music playback data of the target user (Paragraphs 0813-0910 music features and relaxation parameters associated and used to select music to achieve user desired state).
Regarding claim 19, Morishima teaches a non-transitory computer-readable storage medium storing computer instructions causing a computer to execute the method according to claim 1 (col. 4 ll. 9-39).
Garten teaches a non-transitory computer-readable storage medium storing computer instructions causing a computer to execute the method according to claim 1 (Paragraphs 0933-0936).
Regarding claim 20, Morishima teaches a computer program product comprising a computer program which, when executed by a computer, cause the computer to perform the method according to claim 1 (col. 4 ll. 9-39).
Garten teaches a computer program product comprising a computer program which, when executed by a computer, cause the computer to perform the method according to claim 1 (Paragraphs 0933-0936).
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Morishima and Garten as applied to claim 1 above, and further in view of Zalon (US Patent No. 10,409,546).
Regarding claim 10, Garten teaches wherein the at least one piece of target recommendation music is multiple pieces of target recommendation music (Paragraphs 0237-0238, 0273-0275, 0353-0359, 0661-0664, 0856-0859 playlist); but Morishima and Garten do not teach after determining the at least one target recommendation music, merging the multiple pieces of target recommendation music pairwise to obtain at least one piece of merged music; and determining a playback order of the multiple pieces of target recommendation music and the at least one piece of merged music based on two pieces of target recommendation music corresponding to each merged music; wherein the multiple pieces of target recommendation music and the at least one piece of merged music are played for the target user in sequence based on the playback order.
However, in the similar field of music, Zalon teaches after determining the at least one target recommendation music, merging the multiple pieces (Fig. 26 OUT SONG and IN SONG) of target recommendation music pairwise to obtain at least one piece of merged music (Fig. 26 item 4210); and determining a playback order of the multiple pieces of target recommendation music and the at least one piece of merged music based on two pieces of target recommendation music corresponding to each merged music (Fig. 26 item 4210); wherein the multiple pieces of target recommendation music and the at least one piece of merged music are played for the target user in sequence based on the playback order (OUT SONG followed by IN SONG) (col. 9 ll. 12-col. 10 ll. 17, col. 15 ll. 9-38, col. 17 ll. 8-48, col. 25 ll. 18-col. 26 ll. 8, col. 40 ll. 19-38 specifying music sequence, its blending and playing individual pieces and blended piece in desired sequence).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention modify Morishima and Garten to include merging the multiple pieces of target recommendation music pairwise to obtain at least one piece of merged music; and determining a playback order of the multiple pieces of target recommendation music and the at least one piece of merged music based on two pieces of target recommendation music corresponding to each merged music; wherein the multiple pieces of target recommendation music and the at least one piece of merged music are played for the target user in sequence based on the playback order as taught by Zalon in order to “determine a joining or blending of the “in” element and the “out” element so that any joined or blending audio is preferred or deemed audibly acceptable or desirable to a listener” (Zalon, col. 15 ll. 35-38).
Regarding claim 11, Zalon teaches wherein merging the multiple pieces of target recommendation music pairwise to obtain at least one piece of merged music comprises: determining, based on the relaxation parameters of the multiple pieces of target recommendation music, at least one pair of target recommendation music from the multiple pieces of target recommendation music, where each pair of target recommendation music comprises two pieces of adjacent target recommendation music; and merging the two pieces of adjacent target recommendation music to obtain a piece of merged music; wherein the merged music is played for the target user between corresponding two pieces of adjacent target recommendation music (Figs. 26, 27B playing blended portion between OUT SONG and IN SONG).
Regarding claim 12, Zalon teaches wherein merging the multiple pieces of target recommendation music pairwise to obtain at least one piece of merged music comprises: extracting, starting from the end, a first music segment of a first preset duration from the earlier of two pieces of target recommendation music; extracting, starting from the beginning, a second music segment of a second preset duration from the latter of the two pieces of target recommendation music; and merging the first music segment and the second music segment to obtain the merged music (col. 26 ll. 50-col. 27 ll. 9 stitching OUT and IN elements by overlapping them for gapless transition between them).
Allowable Subject Matter
Claims 8, 13 are objected as they are rejected under 35 USC 112 above.
The above objection(s) is (are) based on the claim(s) as presently set forth in its (their) totality. It should not be interpreted as indicating that amended claim(s) broadly reciting certain limitations would be allowable. A more detailed reason(s) for allowance may be set forth in a subsequent Notice of Allowance if and when all claims in the application are put into a condition for allowance.
Conclusion
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HEMANT PATEL
Primary Examiner
Art Unit 2694
/HEMANT S PATEL/ Primary Examiner, Art Unit 2694