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 .
Claim Rejections - 35 USC § 102
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.
Claims 1-5, 7-11, 14 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kawahara (WO 2016136450 A1), from submitted IDS on 07/09/25.
Regarding claim 1, Kawahara teaches a data processing apparatus (“sound source control device”, page 1, paragraph 4) comprising:
A selecting part that selects first content to be provided to a user as a stimulus from a plurality of pieces of content (“a control unit”, page 1, paragraph 4; control unit 230, Fig. 2), and
an acquiring part that acquires sleep data indicating sleep quality of the user (“sound source control device…configured to guide the depth so as to improve the quality of sleep”, page 4, paragraph 4) provided with the first content selected by the selecting part (“biological rhythm acquisition unit that acquires a biological rhythm of a subject”, page 1, paragraph 4; acquisition units 202, Fig. 2), wherein
the selecting part selects second content having an attribute different from that of the first content, on the basis of a relationship between an attribute of the first content affecting contents of a stimulus that the first content provides to the user and sleep quality indicated by the sleep data (“a control unit that controls the sound source unit so as to reproduce the sound determined by the predetermined parameter in accordance with the acquired biological rhythm of the subject”, page 1, paragraph 4; control unit 230, Fig. 2). The sound source control device analyzes the biological rhythm, indicating the sleep quality of the user which is affected by the attribute (“control parameters”, page 4, paragraph 5) of the content shown to the user.
Regarding claim 2, Kawahara teaches the acquiring part further acquiring attribute data indicating an attribute of the user (“a biorhythm acquisition unit that acquires the biorhythm of a subject”, claim 1, “biological sensors…are supplied to the sound source control device”, page 2, paragraph 5; acquisition units 202, biosensors 11, 12, 13, Fig. 2), and
the selecting part selects the first content and the second content further on the basis of the attribute data (“a control unit that controls a sound source unit to play a sound defined by predetermined parameters in accordance with the acquired biorhythm of the subject”, claim 1; control unit 230, Fig. 2).
Regarding claim 3, Kawahara teaches the data processing apparatus further including a storage that stores the sleep data in association with a sleep date (“the storage unit has a database DB and stores a plurality of sets of control parameters and characteristics of sleep depths averaged for the subject”, page 3, paragraph 9; storage unit 250, Fig. 2), wherein
the selecting part selects the second content if sleep quality indicated by second sleep data, which is sleep data in a second sleep period including one or more sleep dates after the first content has been provided to the user as a stimulus, is not improved compared to sleep quality indicated by first sleep data, which is sleep data in a first sleep period including one or more sleep dates before the first content is provided to the user as a stimulus (“calculates an average value of the sleeping periods of periods shorter than the period of the biorhythm and instructs the sound source control unit to control contents to generate sound…sound source control unit causes the sound source unit to reproduce the natural sound A at a 2% tempo faster than the biological rhythm”, page 6, paragraph 3; sound source control unit 230, Fig. 2). The sound source control unit selects a content with a different attribute such as tempo, based off of the average sleep periods to improve sleep quality.
Regarding claim 4, Kawahara teaches the data processing apparatus further including an evaluating part that generates an evaluation result indicating a degree of an effect due to the first content being provided to the user, on the basis of a relationship between the sleep quality indicated by the first sleep data and the sleep quality indicated by the second sleep data (“evaluating unit writes the current sleep evaluation result on the control parameter…record a characteristic indicating how the sleep depth estimated in the current sleep with respect to the subject E has changed with the lapse of time”, page 3, paragraph 8; evaluation unit 220, Fig. 2).
Regarding claim 5, Kawahara teaches the acquiring part further acquiring surrounding environment data indicating a state of a surrounding environment of the user (“the environmental sensor detects the environment, specifically the noise level, the temperature/humidity, the atmospheric pressure, the illuminance of the ambient light, etc.”, page 2, paragraph 5; environmental sensor 15, Fig. 2), and
The evaluating part identifies a degree of influence of the surrounding environment indicated by the surrounding environment data on sleep quality by referring to data indicating a relationship between the surrounding environment and the sleep quality (“evaluation unit may evaluate the sleeping state of the subject based on the evaluation result of the sleep evaluated by the subject…the detection result of the environmental sensor may be used for reflecting the parameter”, page 1, paragraph 6; evaluation unit 220, Fig. 2), and generates the evaluation result by correcting the degree of effect identified on the basis of the relationship between the sleep quality indicated by the first sleep data and the sleep quality indicated by the second sleep data, on the basis of the identified degree of influence (“correct the evaluation result or exclude it from the object of learning if the sleeping environment is a special condition with reference to the temperature, the humidity, and the light intensity detected by the environmental sensor”, page 7, paragraph 1).
Regarding claim 7, Kawahara teaches the selecting part selecting the second content if the sleep quality indicated by the sleep data after the first content has been provided to the user as a stimulus is poorer than a predetermined threshold (“when the estimated sleep depth transits to a direction in which the estimated sleep depth gets deeper and the estimated sleep depth is shallower than the sleep depth of the target characteristic, the evaluation section…instructs the sound source control unit to control contents to generate sound”, page 6, paragraph 3; control unit 230, Fig. 2).
Regarding claim 8, Kawahara teaches the selecting part inputting attribute data of the first content and the sleep data measured after the first content has been provided, to a learned model created by learning using learning sleep data (“depending on the type of sound source to be listened to during sleeping…the degree of satisfaction of sleep may be different depending on the subject’s preference…evaluation result in sleep is reflected on the control parameter”, page 6, paragraph 5, “learning operation of the control parameter…learning operation is executed with a predetermined event”, page 6, paragraph 6; control unit 230, learning operation, Fig. 5B), an attribute of learning content provided to a learning user whose learning sleep data has been measured, and an improvement result of sleep quality of the learning user after the learning content has been provided, as training data, to select the second content corresponding to an attribute output by the learned model (“learning operation facilitates selection of control parameters suitable for the subject, it is easier to increase the satisfaction of sleeping”, page 7, paragraph 4).
Regarding claim 9, Kawahara teaches the selecting part inputting attribute data of the first content, attribute data of the user, and the sleep data measured after the first content has been provided, to a learned model created by learning using learning sleep data, an attribute of learning content provided to a learning user whose learning sleep data has been measured, an attribute of the learning user (“input unit is allowed for allowing the subject E after sleeping to input a subjective evaluation result of the sleep”, page 4, paragraph 1; input unit 206, Fig. 2), and an improvement result of sleep quality of the learning user after the learning content has been provided, as training data, to select the second content corresponding to an attribute output by the learned model (paragraph 5 and 6 from page 6, and paragraph 4 from page 7). The user can input their subjective evaluation of the sleep, as well as the program can allow for the user to also input attribute data such as a user ID, a name, location, date of birth, residential location, or a hobby as the user data. This data would be put into the learned model.
Regarding claim 10, Kawahara teaches the selecting part selecting the first content and the second content without receiving a content selection instruction from the user (CPU controls the sound source unit to reproduce the sound determined by the selected control parameter according to the estimated sleep depth so as to be linked with the acquired biological rhythm of the subject”, page 5, paragraph 3).
Regarding claim 11, Kawahara teaches the selecting part selecting a plurality of pieces of the second content each having a different attribute (“evaluation unit selects and reads one set used for sleep guidance from among a plurality of sets…stored in the database DB…and reads the sound source control unit….control parameters include a plurality of parameters such as sound source type, tempo control, sound volume control, and the like”, page 3, paragraph 7; evaluation unit 220, Fig. 2) corresponding to a plurality of time periods (“database DB in the storage unit to record a characteristic indicating how the sleep depth estimated in the current sleep with respect to the subject E has changed with the lapse of time”, page 3, paragraph 8; storage unit 250, Fig. 2).
Regarding claim 14, Kawahara teaches a data processing method, executed by a computer (“sound source control device is for example, a mobile terminal, a personal computer, or the like, and a CPU (Central Processing Unit) executes a preinstalled computer program”, page 3, paragraph 2), comprising the steps of:
Selecting first content to be provided to a user as a stimulus from a plurality of pieces of content (“sound source control executed by the CPU”, page 5, paragraph 3);
Acquiring sleep data indicating sleep quality of the user provided with the selected first content (the CPU acquires the biological rhythm (body movement, breathing, heart beat, etc.) of the subject”, page 5, paragraph 3); and
Selecting second content having an attribute different from that of the first content on the basis of a relationship between an attribute of the first content affecting contents of a stimulus that the first content provides to the user and sleep quality indicated by the sleep data (“CPU controls the sound source unit to reproduce the sound determined by the selected control parameter…evaluates the control parameter used based on the sleep state of the subject, and changes at least a part of the content of the control parameter according to the evaluation”, page 5, paragraph 3).
Regarding claim 15, Kawahara teaches a non-transitory storage medium storing a program (“internal memory…stores the digital signals”, page 3, paragraph 5, “non-transitory recording medium”, page 8, paragraph 1) for causing a computer to execute the steps of:
Selecting first content to be provided to a user as a stimulus from a plurality of pieces of content (“selects and reads one set used for sleep guidance among a plurality of sets (sets) of control parameters stored in the database DB of the storage unit”, page 3, paragraph 7; control unit 230, storage unit 250, Fig. 2);
Acquiring sleep data indicating sleep quality of the user provided with the selected first content (“the database DB in the storage unit to record a characteristic indicating how the sleep depth estimated in the current sleep”, page 3, paragraph 8; storage unit 250, Fig. 2) and
Selecting second content having an attribute different from that of the first content on the basis of a relationship between an attribute of the first content affecting contents of a stimulus that the first content provides to the user and sleep quality indicated by the sleep data (“sound source control unit controls the sound source unit according to the contents of control by…the transferred control parameters”, page 4, paragraph 2; control unit 230, Fig. 2).
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.
Claim 6, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kawahara (WO 2016136450 A1) in view of Kozlov (US 20110230790 A1)
Regarding claim 6, Kawahara teaches all the limitations of claim 4, but does not teach the acquiring part obtaining activity data indicating activity contents of the user, and the evaluating part that identifies a degree of influence of the activity contents.
However, Kozlov teaches the acquiring part further acquiring activity data indicating activity contents of the user (“one type of general user information is usually obtained…examples of objective general factors impacting sleep are…lifestyle and schedule (fitness, sports, work, type of work, nutrition, etc.”, paragraph [0113]), and
The evaluating part identifies a degree of influence of the activity contents indicated by the activity data on sleep quality by referring to data indicating a relationship between the activity contents and the sleep quality, and generates the evaluation result by correcting the degree of effect identified on the basis of the relationship between the sleep quality indicated by the first sleep data and the sleep quality indicated by the second sleep data, on the basis of the identified degree of influence (“various algorithms can be used to take objective factor input data and determine particular dependencies and parameters most useful for producing higher accuracy sleep phase prediction algorithms”, paragraph [0137]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data processing apparatus of Kawahara with the acquiring part and evaluating part of Kozlov, in order to ensure the data processing apparatus is selecting the most efficient stimulus contents according to the attributes of the user, such as their daily activities which affects sleep quality. This would overall help the sleep quality of the user.
Regarding claim 12, Kawahara teaches all the limitations of claim 1, but does not teach the acquiring part further indicating the influence of environment on the user’s body and mind, and the selecting part referring to data indicating an influence of the surrounding environment of the user on the user’s body and mind.
However, Kozlov teaches the acquiring part further acquiring surrounding environment data indicating a state of a surrounding environment of the user (“one type of general use information is usually obtained…examples of objective general factors impacting sleep are…sleep environment (temperature, humidity, bed quality, presence of other people in bed, room or house”, paragraph [0113]), and
The selecting part refers to a data indicating an influence of the surrounding environment of the user on the user’s body and mind, and data indicating contents of a stimulus suitable for a physical and mental state of the user, to select the second content to be provided to the user in the surrounding environment indicated by the surrounding environment data (“changes in sleep environment caused by changing beds (e.g. use of orthopedic mattress) or changes in the user’s living environment such as an installation of an air conditioner…the system takes into account this change in the “average” characteristics of the user for a certain period of time”, paragraph [0173]). Ultimately the environment and its effects on sleep will affect the user’s body and mind, and this would be recorded when determining the next stimulus content.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data processing apparatus of Kawahara with the acquiring part and selecting part of Kozlov to further select stimulus content according to the influence of environment on the user’s physical and mental state, as it all affects the quality of sleep.
Regarding claim 13, Kawahara teaches all the limitations of claim 1, but does not teach the selecting part referring to data indicating an influence of the activity contents on the user’s body and mind.
However, Kozlov teaches the acquiring part further acquiring activity data indicating activity contents of the user, and
The selecting part refers to data indicating an influence of the activity contents of the user on the user’s body and mind (“user physical activity, mental workload, stress, alcohol, or stimulants, medication”, paragraph 0057]) and data indicating content of a stimulus suitable for a physical and mental state of the user, to select the second content to be provided to the user who did the activity indicated by the activity data (“various algorithms can be used to take objective factor input data and determine particular dependencies and parameters most useful for producing higher accuracy sleep phase prediction algorithms”, paragraph [0137]). Based off the influence of activity contents of the user on the user’s body and mind, the algorithm can select a second stimulus content that helps provide better sleep quality.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data processing apparatus of Kawahara with the acquiring part and selecting part of Kozlov in order to further select stimulus content according to the influence of activity contents of the user on the user’s physical and mental state, as it all affects the quality of sleep.
Conclusion
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/L.L.T./Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791