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 .
Response to Amendment
Applicant’s amendment filed on March 30, 206 has been entered. Claims 1-19 have been amended. No claims have been cancelled. No claims have been added. Claims 1-20 are still pending in this application with claims 1, 18, and 19 being independent.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cha et al. (US Patent Application, Pub. No.: 2015/0100310 A1), and further in view of Guller (US Patent 11,935,646).
In regards to claim 1, Cha discloses an information processing apparatus (e.g., audio apparatus 500) comprising: at least one processor; and at least one memory storing instructions, which when executed by the processor, cause the information processing apparatus to: select (via e.g., selection unit 720) a learning result that corresponds to operation information or an operation sound of a magnetic resonance imaging apparatus (See pg. 3, paragraph [0035] - [0036]; pg. 6, paragraph [0103]; and pg. 7, paragraph [0120]); and an estimation unit (e.g., period estimation unit 520) configured to estimate audio for which the operation sound has been reduced in operation-sound-included audio in which the operation sound and audio of a subject are overlapped, using the learning result (See pg. 6, paragraph [0103] and pg. 6, paragraph [0105] - [0106]). Cha, however, does not specifically disclose selecting a machine learning model that corresponds to operation information of a magnetic resonance imaging apparatus, and estimating audio using the machine learning model. Guller, however, does disclose selecting a machine learning model that corresponds to operation information of a magnetic resonance imaging apparatus, and estimating audio using the machine learning model (See col. 7 lines 37-42; col. 11 lines 18-34; and col. 14 lines 3-28). Therefore, it would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate these features within the system as a way of enabling healthcare providers to develop the right treatment plan for their patients.
In regards to claim 2, Cha discloses the information processing apparatus, wherein the selection unit selects the learning result by comparing at least a portion of a waveform of the operation sound and a waveform of the operation-sound included audio (See pg. 2, paragraph [0025] - [0026]) and pg. 10, paragraph [0189] - [0190]). Guller further discloses selecting the machine learning model (See col. 11 lines 18-34; and col. 14 lines 3-28).
In regards to claim 3, Cha discloses the information processing apparatus, wherein the selection unit selects the learning result based on correlation information obtained from the waveform of the operation sound and the waveform of the operation- sound-included audio (See pg. 10, paragraph [0189] - [0190]). Guller further discloses selecting the machine learning model (See col. 11 lines 18-34; and col. 14 lines 3-28).
In regards to claim 4, Cha discloses the information processing apparatus, wherein in a case where operation information obtained from the magnetic resonance imaging apparatus has been changed, the selection unit obtains a learning result that corresponds to new operation information (See pg. 7, paragraph [0119] - [0120]). Guller further discloses obtaining the machine learning model that corresponds to a new operation information (See col 14 lines 3-28).
In regards to claim 5, Cha discloses the information processing apparatus, wherein the selection unit determines a level of an input volume of the operation sound or the operation-sound-included audio in a time series and, in a case where the input volume is smaller than a threshold volume for a certain amount of time and the input volume rises, exceeding the threshold volume, obtains a learning result that corresponds to a newly obtained operation sound (See pg. 7, paragraph [0119] and pg. 9, paragraph [0170] - [0171]). Guller further discloses obtaining the machine learning model that corresponds to a newly obtained operation sound (See col 14 lines 3-28).
In regards to claim 6, Cha discloses the information processing apparatus, wherein the selection unit obtains a level of an input volume of the operation sound or the operation-sound-included audio at predetermined time intervals and, in a case where a peak value or a standard deviation of the input volume exceeds a reference threshold, obtains a learning result that corresponds to a newly obtained operation sound (See pg. 7, paragraph [0119] - [0120]). Guller further discloses obtaining the machine learning model that corresponds to a newly obtained operation sound (See col 14 lines 3-28).
In regards to claim 7, Cha discloses the information processing apparatus, wherein the estimation unit causes an audio output unit to output synthesized audio for which a signal indicating an audio region obtained based on the estimated audio and a signal indicating a non-audio (i.e., noise) region obtained based on the operation-sound- included audio have been synthesized (See pg. 6, paragraph [0105] - [0106]).
In regards to claim 8, Cha discloses the information processing apparatus, wherein the estimation unit generates the synthesized audio using an emphasized signal for which the signal indicating the audio region has been emphasized compared to the signal indicating the non-audio region or a suppressed signal for which the signal indicating the non-audio region has been suppressed compared to the signal indicating the audio region (See pg. 6, paragraph [0106]).
In regards to claim 9, Cha discloses the information processing apparatus, wherein the estimation unit generates operation-sound-included audio for evaluation from a signal of an operation sound for which a signal of an audio region of the subject has been removed from the operation-sound-included audio and clean audio of the subject used for the learning result (See pg. 6, paragraph [0103]), generates operation- sound-reduced audio for which the signal of the operation sound has been reduced in the operation-sound-included audio for evaluation, using a plurality of pieces of conversion information for converting operation-sound-included audio for learning into the clean audio (See pg. 6, paragraph [0106]), obtains evaluation information for evaluating the plurality of pieces of conversion information, using the operation-sound- reduced audio and the clean audio, and selects, as the learning result, one piece of conversion information from the plurality of pieces of conversion information based on the evaluation information (See pg. 6, paragraph [0108]). Guller, however, does disclose selecting a machine learning model that corresponds to operation information of a magnetic resonance imaging apparatus, and estimating audio using the machine learning model (See col. 7 lines 37-42; col. 11 lines 18-34; and col. 14 lines 3-28).
In regards to claim 10, Cha discloses the information processing apparatus, further comprising a learning processing unit configured to perform learning processing for obtaining the learning result, using operation-sound-included audio for learning generated by overlapping an operation sound for learning and clean audio of a subject, which have been obtained in advance, wherein the learning processing unit generates a new operation sound by summing products of operation sounds for learning and coefficients, adds the generated new operation sound to the operation sound for learning, and performs the learning processing (See pg. 6, paragraph [0114] and pg. 8, paragraph [0157] - [0158]). Guller further discloses obtaining the machine learning model that corresponds to a newly obtained operation sound (See col 14 lines 3-28).
In regards to claim 11, Cha discloses the information processing apparatus, wherein in a case where operation information of an operation sound that has not been learned is the same as learned operation information and an imaging condition setting is different from that of an operation sound that has been classified based on the operation information, the learning processing unit adds the operation sound that has .not been learned into a class that is based on the operation information and performs the learning processing (See pg. 10, paragraph [0196]). Guller further discloses obtaining the machine learning model that corresponds to a newly obtained operation sound (See col 14 lines 3-28).
In regards to claim 12, Cha discloses the information processing apparatus, wherein the learning processing unit obtains correlation information of a waveform of a learned operation sound and a waveform of an operation sound that has not been learned, adds the operation sound that has not been learned into a class that includes the learned operation sound whose correlation information is the highest, and performs the learning processing (See pg. 10, paragraph [0189] - [0190]).
In regards to claim 13, Cha discloses the information processing apparatus, wherein in a case where the operation information of the operation sound that has not been learned is different from the learned operation information or in a case where the correlation information is lower than a predetermined reference correlation value, the learning processing unit adds, as an operation sound that is based on new operation information, the operation sound that has not been learned and performs the learning processing (See pg. 8, paragraph [0157] - [0158]).
In regards to claim 14, Cha discloses the information processing apparatus, wherein the learning processing unit compares characteristics of a plurality of operation sounds that are classified based on the operation information, separates, from the classified plurality of operation sounds, operation sounds for which at least some of the characteristics match, and performs the learning processing in separated units (See pg. 8, paragraph [0157] - [0158]).
In regards to claim 15, Cha discloses the information processing apparatus, wherein the learning processing unit compares characteristics of a plurality of operation .sounds that are classified into a respective one of a plurality of pieces of operation information and, in a case where operation sounds for which at least some of the characteristics match, performs the learning processing in units into which the plurality of operation sounds have been grouped (See pg. 8, paragraph [0157] - [0158]).
In regards to claim 16, Cha discloses the information processing apparatus, wherein the estimation unit includes a first computation model for performing the estimation and a second computation model configured so as to be lower in computational load than the first computation model, in a case where a processing load of a processor does not exceed a load threshold, selects the first computation model and performs the estimation, and in a case where the processing load of the processor exceeds the load threshold, selects the second computation model and performs the estimation (See pg. 9, paragraph [0170] [0171]).
In regards to claim 17, Cha discloses the information processing apparatus, wherein the selection unit performs the selection, using a first processor, and the estimation unit performs the estimation, using a second processor different from the first processor (See pg.
In regards to claim 18, Cha discloses a magnetic resonance imaging apparatus (See pg. 11, paragraph [0215]) comprising: a sound collecting unit (e.g., first audio input device 2020) configured to collect operation-sound-included audio in which an operation sound and audio of a subject are overlapped (See pg. 11, paragraph 0216]); a selection unit (e.g., selection unit 720) configured to select a learning result that corresponds to operation information or the operation sound (See pg. 6, paragraph [0117] and pg. 7, paragraph [0120]); an estimation unit (e.g., period estimation unit 520) configured to estimate audio for which the operation sound has been reduced in the operation-sound- included audio, using the learning result (See pg. 6, paragraph [0103] and pg. 6, paragraph [0105] - [0106]). Guller, however, does disclose selecting a machine learning model that corresponds to operation information of a magnetic resonance imaging apparatus, and estimating audio using the machine learning model (See col. 7 lines 37-42; col. 11 lines 18-34; and col. 14 lines 3-28). Therefore, it would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate these features within the system as a way of enabling healthcare providers to develop the right treatment plan for their patients.
In regards to claim 19, Cha discloses an information processing method comprising: selecting a learning result that corresponds to operation information or an operation sound of a magnetic resonance imaging apparatus (See pg. 3, paragraph [0035] - [0036]; pg. 6, paragraph [0117]; and pg. 7, paragraph [0120]); and estimating audio for which the operation sound has been reduced in operation-sound-included audio in which the operation sound and audio of a subject are overlapped, using the learning result (See pg. 6, paragraph [0103] and pg. 6, paragraph [0105] - [0106]). Guller, however, does disclose selecting a machine learning model that corresponds to operation information of a magnetic resonance imaging apparatus, and estimating audio using the machine learning model (See col. 7 lines 37-42; col. 11 lines 18-34; and col. 14 lines 3-28). Therefore, it would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate these features within the system as a way of enabling healthcare providers to develop the right treatment plan for their patients.
In regards to claim 20, Cha discloses a non-transitory computer-readable storage medium storing a program for causing a computer to execute the method according to claim 19 (See pg. 12, paragraph [0226]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THJUAN KNOWLIN ADDY whose telephone number is (571)272-7486. The examiner can normally be reached 8:30AM - 5:00PM Mon-Fri.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached at (571) 272-7488. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THJUAN K ADDY/Primary Examiner, Art Unit 2693
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