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 Arguments
2. Regarding the rejection under 35 USC 101, Applicant argues that:
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Examiner respectfully disagrees. Applicant is advised that, according to MPEP 2106 and the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), the Office determines claim eligibility under 35 U.S.C. § 101 using the Alice framework. The analysis under Step 2A - Prong 1 evaluates whether the claim recites a judicial exception. Step 2A - Prong 2 asks does the claim recite additional elements that integrate the judicial exception into a practical application, and, if necessary, Step 2B further analyzes whether or not the claim provides an Inventive Concept. That is, the claim needs to be analyzed limitation by limitation, and/or element by element, following the MPEP/2019 PEG guidelines.
In the instant case, focusing on what the inventors have invented exactly and giving the broadest reasonable interpretation (BRI) to the claims, Examiner asserts that the pending claims 1-18 are directed to an abstract idea of inferencing musical piece using existing Al technology but without reciting any additional limitation/element that amounts to “significantly more” than the judicial exception.
As discussed in details in section 4 below, Examiner considers that the bolded portion (L1b) of representative claim 1 encompasses a judicial exception of “Mental Process” defined by the 2019 PEG. The “trained inference model” is recited at a high-level of generality which is used like a “black box AI”, whose internal workings are a mystery of math concepts to its users, to perform the abstract idea. As such, the recitation of the “trained inference model” does not negate the mental nature of the bolded portion (L1b) of claim 1 because the claim here merely uses the pre-trained machine learning model as a tool to perform the otherwise mental processes. See MPEP 2106.05(f).
Claim 2 of Example 37 of USPTO Subject Matter Eligibility Examples recites a method of rearranging icons on a graphical user interface (GUI) of a computer system. It is deemed that the claim does not recite any of the judicial exceptions enumerated in the 2019 PEG because the claim, under its broadest reasonable interpretation, does not cover performance in the mind but for the recitation of generic computer components. Further, the claim does not recite a mathematical relationship, formula, or calculation. Thus, the claim is eligible because it does not recite a judicial exception.
In the instance case, Examiner identified that the “heart” (bolded portion) of pending claim 1 reciting a mental process that amounts to an abstract idea falling within the “Mental Process” grouping of Abstract Ideas under the 2019 PEG. The pre-trained machine learning model is merely used as a tool to perform the otherwise mental processes, while a mental process can be implemented using any general-purpose computer (MPEP 2106.04(a)(2)). Further, a pre-trained machine learning model may be interpretated as software, hardware or combinations thereof, it does not necessarily need a particular machine or manufacture that is integral to the claimed musical piece inference device of the present application. For example, a car jack is a device used to lift a vehicle off the ground; the jack itself, however, does not have be an “integral part” of the vehicle.
Accordingly, the decision in Claim 2 of Example 37 is not analogous to the pending claims of the present application.
Applicant argues that (REMARKS, p.11-12):
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Examiner respectfully disagrees. Under Step 2A - Prong 2 (see MPEP 2106.04(d)), Examiner evaluates whether the claim recites any additional element that integrate the judicial exception into a practical application. Examiner identifies that representative claim 1 recites the following additional limitations/elements:
L1: an electronic controller including at least one processor, the electronic controller being configured to execute a plurality of modules including
L1a: a data acquisition module configured to acquire target data including an input token sequence arranged to indicate at least a part of a musical piece, the input token sequence including a plurality of bar-line or beat tokens arranged to indicate bar-line or beat positions of at least the part of the musical piece, the bar-line or beat positions being positions of bar lines that are vertical lines that separate measures of at least the part of the musical piece, positions of beats within the measures of at least the part of the musical piece, or both; and
L2: an output module configured to output the result of the inference.
Examiner then asks: does the additional limitation/element provide an improvement in the functioning of a computer, or an improvement to other technology or technical field? does it implement the abstract idea with a particular machine or manufacture that is integral to the claim? does it effect a transformation or reduction of a particular article to a different state or thing? does it applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception? In accordance with the guideline in MPEP 2106.04(d), Examiner has reviewed carefully each of these additional limitations/elements together with the claim as a whole, and maintains the following position:
The combination of the limitation L1 encompasses a general-purpose computer, comprising generic computer components adapted to receive input data/information and perform computing activities via basic function of the computer. According to the MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers mental processes except for the mention of generic computer components performing computing activities via basic function of the computer, then the claim is likely considered to be directed to an ineligible abstract idea, as it essentially describes a mental process that could be performed by a human without the computer components adding any significant practical application beyond the abstract concept itself.
The combination of the limitation L1a encompasses merely gathering the data/information necessary for performing the identified abstract idea. See MPEP 2106.05(g)(3). As such, it represents an extra-solution activity to the judicial exception. The claimed “data acquisition module” is recited at a high level of generality (i.e., as a generic processor performing a generic computer function such as collecting data, transmitting data, analyzing data, storing data, and outputting data). The claim does not specify any particular type of sensor or device that is uniquely deployed to perform said “acquire target data”. As to the limitations to the acquired target data, under the BRI, they represent mere data characterization and descriptive of the information being acquired/processed. They are nothing more than an attempt to generally link the use of the judicial exception to the particular field of music composition. Thus, they represent only a mere token acquiescence to limiting the reach of the claim to this field, like Bilski’s identification of the participants in a process for hedging risk as commodity providers and commodity consumers, which the Supreme Court indicated did no more than describe how the abstract idea of hedging risk could be used in the commodities and energy markets. See MPEP 2106.05(h), discussing the limitation in Bilski v. Kappos, 561 U.S. 593, 595 (2010), as well as other examples of field of use limitations.
Under the BRI, the limitation L2 is considered an insignificant post-solution activities (i.e., transmitting or displaying the algorithm results), which does not amount to the recitation of significantly more than the abstract idea itself.
Furthermore, Examiner asserts that none of the additional limitations listed above, when being used to perform the identified the judicial exception, transforms the physical attributes of the “processor”, the “controller”, and/or the “plurality of modules” to digital data of a different form. Instead, considering the claim as a whole, the data is obtained and manipulated, i.e. “transformed”, into other data through mathematical calculations.
Hence, none of the additional limitations recited in the pending claims amounts for "significantly more" or reflects an “inventive concept” under the 2019 PEG.
It is held that simply setting forth advantages (i.e. benefits) of use without providing any rational/evidence to how/why the claimed elements amount to significantly more than the judicial exception could be treated as mere instructions to apply the judicial exception on a computer component (MPEP 2106.05(f)), but not qualified for an improvement (i.e. enhancement) in the functioning of a computer or an improvement to another technology or technical field. The key is to show that the claim goes beyond just performing a calculation and provides a practical application or significant improvement through the use of that calculation. See MPEP 2106.04(d)(I) and 2106.05(a).
Applicant’s arguments with respect to the subject matter eligibility are therefore not persuasive.
Applicant's arguments regarding the rejection under 35 USC 102/103 have been considered but are moot in view of the new ground(s) of rejection. Detailed response is given in sections 5-8 as set forth below in this Office action.
Claim Rejections - 35 USC § 101
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action:
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.
4. Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Claims 1-18 are directed to an abstract idea of inferencing musical piece using Al technology.
Specifically, representative claim 1 recites:
A musical piece inference device comprising:
L1: an electronic controller including at least one processor, the electronic controller being configured to execute a plurality of modules including
L1a: a data acquisition module configured to acquire target data including an input token sequence arranged to indicate at least a part of a musical piece, the input token sequence including a plurality of bar-line or beat tokens arranged to indicate bar-line or beat positions of at least the part of the musical piece, the bar-line or beat positions being positions of bar lines that are vertical lines that separate measures of at least the part of the musical piece, positions of beats within the measures of at least the part of the musical piece, or both;
L1b: an inference module configured to, by using a trained inference model, generate an output token sequence indicating a result of an inference with respect to the musical piece from the input token sequence included in the target data; and
L2: an output module configured to output the result of the inference.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below:
Step
Analysis
1. Statutory Category ?
Yes.
System/device
2A - Prong 1: Judicial Exception Recited?
Yes.
See the bolded portion listed above.
Under its broadest reasonable interpretation, limitation L1b encompasses a mental process of data manipulation that can be performed in the human mind or by a human using a pen and paper but for the recitation of generic computer components. That is, other than reciting “an inference module,” nothing in the bolded portion precludes the limitation L1b from practically being performed in the mind or with pen/paper.
In view of the USPTO’s July 17, 2024 Subject Matter Eligibility Examples (e.g., Examples 47-49), “generating/predicting using a machine learning model" is considered an "abstract idea" if the claim focuses solely on the concept of making predictions using a generic machine learning algorithm, without any specific technical improvements or applications that go beyond the basic idea of using a computer to analyze data and generate predictions; essentially, if the claim is too high-level and does not describe a concrete, inventive implementation of the machine learning process. In the instant case, the recited “generate an output token sequence” generally applies the abstract idea without placing any limits on how the trained machine learning models function. Rather, the claim only recites the outcome of the generation/prediction but does not include any details about how the “generate/predict” is accomplished. See MPEP 2106.05(f).
Further, the particulars of the data and information such as “output token sequence indicating a result of an inference with respect to the musical piece from the input token sequence included in the target data” are mere data characterization and descriptive of the information being determined/observed.
As such, the bolded limitation constitutes an abstract idea that falls within the “Mental Process” Grouping of Abstract Ideas defined by the 2019 PEG.
2A - Prong 2: Integrated into a Practical Application?
No.
The claim as a whole does not integrate the abstract idea into a practical application.
The limitation L1, including at least one processor, is recited at a high level of generality. Under the BRI, the limitation L1 encompasses a general-purpose computer. According to the MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers mental processes except for the mention of generic computer components performing computing activities via basic function of the computer, then the claim is likely considered to be directed to an ineligible abstract idea, as it essentially describes a mental process that could be performed by a human without the computer components adding any significant practical application beyond the abstract concept itself.
The generic recitation of “the electronic controller being configured …” does not amount the abstract idea to be significantly more. It is held that performing an abstract algorithm using a general-purpose computer/circuitry would not amount to significantly more than the abstract algorithm itself. See, for example, Whitserve LLC v. Dropbox, Inc.
The limitations L1a encompasses merely gathering the data/information necessary for performing the identified abstract idea. According to MPEP 2106.05(g)(3): … that were described as mere data gathering in conjunction with a law of nature or abstract idea. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13863, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). As such, it represents an extra-solution activity to the judicial exception.
The particulars of the data and information recited in L1a are considered mere data characterization which can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the relevant technological environment.
The limitation L2 is considered insignificant post-solution activities (i.e., transmitting or displaying the algorithm results), which does not amount to the recitation of significantly more than the abstract idea itself.
In general, the claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the algorithm across a wide range of applications.
2B: Claim provides an Inventive Concept?
No.
Focusing on what the inventors have invented exactly, it is considered that the “core” of instant claim 1 is directed to an abstract idea of inferencing musical piece using Al technology. The claim does not recite any additional element that amounts to “significantly more” or an “inventive concept” under the 2019 PEG (see also MPEP 2106.05).
In particular, the mere instructions to apply a judicial exception on a generic computer and/or a “Black Box” machine-learning model that is used as a tool whose internal workings are a mystery of math concepts to its users do not integrate a judicial exception into a practical application or provide an inventive concept in Step 2B of the two-part Alice framework.
The claim is therefore ineligible under 35 USC 101.
The dependent claims 2-6 inherit attributes of the independent claim 1, but do not add anything which would render the claimed invention a patent eligible application of the abstract idea. These claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because they merely add details to the algorithm which forms the abstract idea as discussed above. The limitations to the plurality of bar-line/beat tokens are all well-understood/routine/conventional which extend the identified abstract idea but do not amount for "significant more".
Claims 7-13 are rejected for the same reason as for claims 1-6 discussed above.
Claim 13 recites: “a plurality of training datasets each of which includes a combination of training data and a correct answer label … and a training processing module configured to execute machine learning of an inference model by using the plurality of training datasets, themachine learning being configured by training the inference model such that, with respect to each of the training datasets, an output token sequence generated by the inference model from the input token sequence included in the training data matches the true value indicated by the correct answer label”. Under the BRI, the combination of the limitations to the training of the inference model encompasses processes of binning/clustering/labelling a set of existing training data and training the Al model using the labelled training data. In light of the USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Example 47, claim 2), discretizing continuous training data to generate input data by processes including binning or clustering continuous data may be practically performed in the human mind using observation, evaluation, judgment, and opinion, while said training is recited at a high level of generality which may involve optimizing the AI models using a series of mathematical calculations to iteratively adjust the algorithms and/or parameter values of the AI models, therefore encompasses mathematical concepts.
Claims 14-18 are rejected for the same reason as for claims 2-6 discussed above.
Hence the claims 1-18 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
5. 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; or
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claims 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WATANABE (US 20170084259 A1).
Regarding claims 1 and 7, WATANABE discloses a musical piece inference device (Figs. 1 and 2), a method and computer program products for practicing the device (para. 0005), comprising: an electronic controller (Fig. 1) including at least one processor (1), the electronic controller being configured to execute a plurality of modules including: a data acquisition module (20 and/or 24 of Fig. 2) configured to acquire target data (para. 0022) including an input token sequence arranged to indicate at least a part of a musical piece (Figs. 3A and 3B; para. 0041-0042), the input token sequence including a plurality of bar-line (Fig. 3A: “1” and “2” indicate the first and second measures) arranged to indicate bar-line positions of at least the part of the musical piece, the bar-line positions being positions of bar lines that are vertical lines that separate measures of at least the part of the musical piece (para. 0042); an inference module (26 of Fig. 2) configured to, by using a trained (note, the term “trained” is given a broad interpretation, e.g., “instructed”; see para. 0001, 0010) inference model (21), generate an output token sequence indicating a result of an inference (e.g., the accompaniment notes shown in FIG. 4B that are adjusted in accent position in accordance with the result of the accent position extraction shown in FIG. 3B) with respect to the musical piece from the input token sequence included in the target data (para. 0044, 0048; see also discussion of S14 and S15 of Fig. 5); and an output module (28 of Fig. 2) configured to output the result of the inference (para. 0038).
Regarding claims 2 and 8, WATANABE discloses: wherein each of the plurality of bar-line tokens is arranged at each of the positions of the bar lines in the input token sequence (Fig. 3A).
Regarding claims 3 and 9, WATANABE discloses: wherein the input token sequence is configured so as to correspond to a sequence of notes of at least the part of the musical piece (Figs. 3A, 4A), and the inference module is configured to generate the output token sequence such that the output token sequence indicates a sequence of notes of at least a part of an arranged musical piece, as the result of the inference with respect to the musical piece (Figs. 4B, 4C).
Regarding claims 4 and 10, WATANABE discloses: wherein the input token sequence is configured so as to correspond to a sequence of notes of at least the part of the musical piece (Figs. 3A, 4A), and the output token sequence is generated so as to indicate a result of estimating local attributes of at least the part of the musical piece, as the result of the inference with respect to the musical piece (Figs. 4B, 4C).
Regarding claims 5 and 11, WATANABE discloses: wherein the input token sequence is configured so as to correspond to a sequence of notes of at least the part of the musical piece (Figs. 3A, 4A), and the inference module is configured to generate the output token sequence such that the output token sequence indicates a musical score of at least the part of the musical piece, as the result of the inference with respect to the musical piece (Figs. 4B, 4C).
Regarding claims 6 and 12, WATANABE discloses: wherein the input token sequence is configured so as to correspond to a sequence of elements of at least the part of the musical piece (Figs. 3A, 4A), and the inference module is configured to generate the output token sequence such that the output token sequence indicates a sequence of notes of at least a part of an arranged musical piece, asthe result of the inferencewith respect to themusical piece (Figs. 4B, 4C).
Claim Rejections - 35 USC § 103
7. 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 of this title, 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.
8. Claims 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over AKAMA (US 20230135118 A1, US-PGPUB version of WO 2021220797 A1 published 2021-11-04, hereinafter AKAMA) in view of WATANABE.
Regarding claims 13, AKAMA discloses a model generation device (Fig. 4) comprising: an electronic controller (1 of Fig. 4) including at least one processor (para. 0096), the electronic controller being configured to execute a plurality of modules including a training data acquisition module configured to acquire a plurality of training datasets (para. 0044, 0123), the training data including an input token sequence arranged to indicate at least a part of a musical piece for training (see discussion of Step S4 of Fig. 15), the input token sequence including a plurality of time-line tokens arranged to indicate time-line positions of at least the part of the musical piece, the time-line positions being positions of time lines of at least the part of the musical piece (para. 0047, 0111-0112; see also Fig. 11 and related discussion of the input token sequence input to the encoder 21a), wherein the training data is further configured to indicate a true value of an output token sequence corresponding to a result of an inference with respect to the musical piece (para. 0008: “using training data so as to output output data corresponding to the output track when input data corresponding to the first track is input”; para. 0071: “Learning of the encoder 21a may be performed … The parameters of the encoder 21a and the decoder 21b are adjusted by comparing the input token sequence in the encoder 21a with the output token sequence generated by the decoder 21b. By repeating the adjustment, the learned model 21 in which the parameters of the encoder 21a and the decoder 21b are optimized is generated”); and a training processing module (1 of Fig. 4) configured to execute machine learning of an inference model (21) by using the plurality of training datasets, the machine learning being configured by training the inference model such that, with respect to each of the training datasets, an output token sequence generated by the inference model from the input token sequence included in the training data matches the true value (para. 0069, 0071-0073, 0111-0112, 0130).
AKAMA is silent on: said time-line tokens are bar-line tokens arranged to indicate bar-line positions of at least the part of the musical piece, the bar-line positions being positions of bar lines that are vertical lines that separate measures of at least the part of the musical piece.
WATANABE discloses a device comprising an electronic controller (Fig. 1) including at least one processor (1), the electronic controller being configured to execute a plurality of modules including: a data acquisition module (20 and/or 24 of Fig. 2) configured to acquire a target dataset including an input token sequence arranged to indicate at least a part of a musical piece, the input token sequence including a plurality of bar-line tokens arranged to indicate bar-line positions of at least the part of the musical piece, the bar-line positions being positions of bar lines that are vertical lines that separate measures of at least the part of the musical piece; and an inference model (26 of Fig. 2) configured to generate, with respect to the acquire the target dataset, an output token sequence indicating a result of an inference (e.g., the accompaniment notes shown in FIG. 4B that are adjusted in accent position in accordance with the result of the accent position extraction shown in FIG. 3B) from the input token sequence included in the acquired target dataset (para. 0044, 0048; see also discussion of S14 and S15 of Fig. 5).
Since AKAMA teaches the general condition of the input token sequence (para. 0052-0068), in view of WATANABE, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to apply AKAMA’s model generation device and method to WATANABE’s input token sequence, as an intended use of the AKAMA model generation technique, to create an output track by using an input track including a plurality of first information elements provided over a certain period or a certain section and a machine learning model (AKAMA, Abstract). The motivation is to provide an automatic arrangement apparatus and method capable of enhancing quality of an automatic arrangement through which a music track such as accompaniment track can be created to match the accent positions (rhythmic elements) in a music piece represented by the original performance information (WATANABE, para. 0006). Further, it is well-known that using AI model to create music allows to quickly generate backing tracks, explore complex arrangements, and easily experiment with new genres, making music production more accessible and efficient.
The combination of AKAMA and WATANABE is silent on: each of the plurality of training datasets includes a combination of training data and a correct answer label, the correct answer label being configured to indicate a true value of an output token sequence.
Examiner takes official notice that machine learning models, such as supervised learning models, which require labelling the training data to provide the algorithm with input data paired with the corresponding, correct output labels (ground truth), allowing the model to learn the relationship between them, are well-known in the art. It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to apply a well-known supervised learning process to train AKAMA’s learned model (21 of Fig. 4) to arrive the claimed invention wherein each of the plurality of training datasets includes a combination of training data and a correct answer label, the correct answer label being configured to indicate a true value of an output token sequence. It is deemed such a modification is merely a design variation to AKAMA’s machine learning model, which the skilled person would conceive and apply without needing inventive skill but depending on practical considerations and according to the dictates of the circumstances since it has been held that the mere application of a known technique to a specific instance by those skilled in the art would have been obvious.
Regarding claims 14-18, AKAMA does not but the teaching of WATANABE includes: wherein each of the plurality of bar-line tokens is arranged at each of the positions of the bar lines in the input token sequence (Fig. 3A); wherein the input token sequence is configured so as to correspond to a sequence of notes of at least the part of the musical piece (Figs. 3A, 4A), and the inference module is configured to generate the output token sequence such that the output token sequence indicates a sequence of notes of at least a part of an arranged musical piece, as the result of the inference with respect to the musical piece (Figs. 4B, 4C); wherein the input token sequence is configured so as to correspond to a sequence of notes of at least the part of the musical piece (Figs. 3A, 4A), and the output token sequence is generated so as to indicate a result of estimating local attributes of at least the part of the musical piece, as the result of the inference with respect to the musical piece (Figs. 4B, 4C); wherein the input token sequence is configured so as to correspond to a sequence of notes of at least the part of the musical piece (Figs. 3A, 4A), and the inference module is configured to generate the output token sequence such that the output token sequence indicates a musical score of at least the part of the musical piece, as the result of the inference with respect to the musical piece (Figs. 4B, 4C); and wherein the input token sequence is configured so as to correspond to a sequence of elements of at least the part of the musical piece (Figs. 3A, 4A), and the inference module is configured to generate the output token sequence such that the output token sequence indicates a sequence of notes of at least a part of an arranged musical piece, asthe result of the inferencewith respect to themusical piece (Figs. 4B, 4C). As such, the combination of AKAMA and WATANABE renders the claimed invention obvious.
Conclusion
9. 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 extension fee 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 date of this final action.
Contact Information
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANCHUN QIN whose telephone number is (571)272-5981. The examiner can normally be reached 9AM-5:30PM EST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dedei Hammond can be reached at (571)270-7938. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIANCHUN QIN/Primary Examiner, Art Unit 2837
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