CTNF 18/846,308 CTNF 80300 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 101 07-04-01 AIA 07-04 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. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 6 recites a matching method, which is a statutory category of invention. ( Step 1: YES ). Claim 6 recite a matching method comprising: identifying information of a user from input data for matching that is input by the user (T he terms “input data” and “information” are not at all limited by the claim language and thus would cover any conceivable form of data and information. A step of “identifying” generic information of a user from generic input data would therefore encompass mental observations or evaluations that are practically performed in the human mind by, for example, a human observing the input data and mentally identifying that data as information of a user ); and identifying sound data that matches the user by comparing the identified information with classification information that is associated with each sound data in advance ( The terms “sound data” and “classification information” are also not at all limited by the claim language and would cover any form of audio data associated in any manner with any type of class or category. A step of “identifying” matching sound data would therefore encompass mental observations or evaluations that are practically performed in the human mind by, for example, a human mentally comparing the identified information with classification information and observing which sound data was associated with the matching classification information ). As shown above, claim 6 recites a series of steps that would encompass mental activity practically performed in the human mind and therefore the claim recites an abstract idea. ( Step 2A, Prong One: YES ). Claim 6 does not expressly recite any additional elements beyond the judicial exception. Additionally, even if one were to argue that the recited “input data”, “sound data”, or “classification information” inherently required a computer or some other machine to store such information, this generic computer would merely be used as a tool to perform the generic computer function of storing the data and information, and would not integrate the recited judicial exception into a practical application ( Step 2A, Prong Two: NO ). The claim is therefore directed to a judicial exception ( Step 2A: YES ). As claim 6 does not recite any additional elements (expressly or inherently) beyond mere instructions to apply the exception using generic components, the claim as a whole does not amount to significantly more than the recited exception to provide an inventive concept ( Step 2B: NO ). Claim 7 further requires the classification information to be obtained from first information and second information. The second information is merely “based on information registered in advance” and would not alter the analysis provided above. The first information is “being obtained by inputting classification target sound data to a machine learning model generated by performing machine learning with the use of sound data and teacher data, which are training data”. In other words, a trained machine learning model is used to generate the recited first information. A “machine learning model” inherently requires a computer or other machine, and thus claim 7 includes an additional element. However, when considering whether an additional element integrates a judicial exception into a practical application, MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. In this case, the first information is generated by “inputting classification target sound data to a machine learning model” without any details as to how the machine learning model is constructed or how the machine learning model functions. Although the claim requires the machine learning model to be trained with the use of sound data and teacher data, any supervised machine learning model applied to sound data requires training using labeled sound data. The recited machine learning model, therefore, amounts to no more than a generic sound classification model trained in a generic manner using labeled sound data. This amounts to mere instructions to implement the abstract idea on a computer, because the claim fails to recite details of how a solution to a problem is accomplished and merely invokes computers or other machinery as a tool to perform the classification of sound data. For the reasons given above, claim 7 does not include additional elements that integrate the recited judicial exception into a practical application, or provide an inventive concept. Claim 8 merely requires the input data to include data indicating a preference of a user and determining a degree of similarity between the preference data and the classification information determine with respect to each sound data classified in advance. This would encompass mental activity practically performed in the human mind by a human, for example, observing the preference data and mentally comparing the preference data to the classification information determined in advance and mentally ranking the degree of similarity for each sound data. Claim 8 does not include any additional elements, and therefore does not integrate the recited judicial exception into a practical application, or provide an inventive concept. Claim 9 merely requires the input data to be voice data of the user and identifying an emotion of the user from the voice data for identifying matching sound data. This would encompass mental activity practically performed in the human mind by a human, for example, listening to the voice data and mentally determining an emotion, then mentally comparing that emotion to emotions of the sound data to determine a match. Claim 9 does not include any additional elements, and therefore does not integrate the recited judicial exception into a practical application, or provide an inventive concept. Claim 10 merely requires the input data to be voice data of the user and identifying an age of the user from the voice data for identifying matching sound data. This would encompass mental activity practically performed in the human mind by a human, for example, listening to the voice data and mentally determining an age, then mentally comparing that age to ages of the sound data to determine a match. Claim 10 does not include any additional elements, and therefore does not integrate the recited judicial exception into a practical application, or provide an inventive concept. Claims 1-5 are directed to a matching apparatus comprising at least one memory storing instructions and at least one processor configured to execute the instructions to perform the same methods recited in claims 6-10. Claims 11-15 are directed to a non-transitory computer readable recording medium that includes a program recorded thereon, the program including instructions that cause a computer to perform the same methods as claims 6-10. As noted in MPEP 2106.04(a)(2), a claim that requires a computer may still recite a mental process when the mental process is performed on a generic computer, such as recited in claims 1-5. Additionally, both product and process claims may recite mental processes. Claims 1-5 and 11-15 are therefore directed to an abstract idea without significantly more for the same reasons as claims 6-10. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 1-3, 5-8, 10-13 and 15 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Tang (U.S. Patent Application Pub. No. 2021/0217437) In regard to claim 1, Tang discloses a matching apparatus comprising: at least one memory storing instructions (Fig. 5, 502); and at least one processor configured to execute the instructions (Fig. 5, 501) to: identify information of a user from input data for matching that is input by the user (Fig. 1, based on input user audio, audio type information is determined, paragraphs [0020-0024]); and identify sound data that matches the user by comparing the identified information with classification information that is associated with each sound data in advance (matching audio type information is determined based on the audio type information and present matching relationship information, paragraphs [0034-0036]). In regard to claim 2, Tang discloses the classification information is obtained from first information and second information, the first information being obtained by inputting classification target sound data to a machine learning model generated by performing machine learning with use of sound data and teacher data, which are training data, and the second information being obtained by classifying the classification target sound data based on information registered in advance (matching relationship information is pre-stored based on classifying the target audio data, paragraph [0035]; the classifying performed using a trained audio classification model, paragraphs [0028-0031]). In regard to claim 3, Tang discloses the input data includes data indicating a preference of the user (see Fig. 3, user feedback indicating user preferences is received, paragraphs [0064-0068]), the one or more processors further: identifies the preference of the user from the input data, as information of the user (the input information of the user identifies user preferences, paragraph [0068]), and determines a degree of similarity between the preference of the user and the classification information with respect to each sound data classified in advance, and identifies sound data for which the predetermined degree of similarity satisfies a set condition (a matching degree in the matching relationship information is determined based on the input user preferences, paragraph [0067]; and matching audio which has a matching degree that satisfies a preset condition is selected, paragraph [0036]). In regard to claim 5, Tang discloses the input data is voice data of the user (user audio comprising a voice, paragraphs [0020-0021]), the one or more processors further: identifies, as the information of the user, an age of the user from the input data that is the voice data by performing age analysis on the user (the user’s age is identified from the voice, paragraph [0025]); and identifies the sound data that matches the user based on the identified age of the user (the age information is used as audio type information for matching, paragraphs [0025] and [0034-0035]). In regard to claim 6, Tang discloses a matching method comprising: identifying information of a user from input data for matching that is input by the user (Fig. 1, based on input user audio, audio type information is determined, paragraphs [0020-0024]); and identifying sound data that matches the user by comparing the identified information with classification information that is associated with each sound data in advance (matching audio type information is determined based on the audio type information and present matching relationship information, paragraphs [0034-0036]). In regard to claim 7, Tang discloses the classification information is obtained from first information and second information, the first information being obtained by inputting classification target sound data to a machine learning model generated by performing machine learning with use of sound data and teacher data, which are training data, and the second information being obtained by classifying the classification target sound data based on information registered in advance (matching relationship information is pre-stored based on classifying the target audio data, paragraph [0035]; the classifying performed using a trained audio classification model, paragraphs [0028-0031]). In regard to claim 8, Tang discloses the input data includes data indicating a preference of the user (see Fig. 3, user feedback indicating user preferences is received, paragraphs [0064-0068]), in the identifying information of the user, the preference of the user is identified from the input data, as information of the user (the input information of the user identifies user preferences, paragraph [0068]), and in the identifying sound data that matches the user, a degree of similarity between the preference of the user and the classification information is determined with respect to each sound data classified in advance, and identifies sound data for which the predetermined degree of similarity satisfies a set condition is identified (a matching degree in the matching relationship information is determined based on the input user preferences, paragraph [0067]; and matching audio which has a matching degree that satisfies a preset condition is selected, paragraph [0036]). In regard to claim 10, Tang discloses the input data is voice data of the user (user audio comprising a voice, paragraphs [0020-0021]), in the identifying information of the user, an age of the user is identified as the information of the user from the input data that is the voice data by performing age analysis on the user (the user’s age is identified from the voice, paragraph [0025]); and in the identifying sound data that matches the user, the sound data that matches the user is identified based on the identified age of the user (the age information is used as audio type information for matching, paragraphs [0025] and [0034-0035]). In regard to claim 11, Tang discloses a non-transitory computer readable recording medium that includes a program recorded thereon (paragraph [0082]), the program including instructions that cause a computer to: identify information of a user from input data for matching that is input by the user (Fig. 1, based on input user audio, audio type information is determined, paragraphs [0020-0024]); and identify sound data that matches the user by comparing the identified information with classification information that is associated with each sound data in advance (matching audio type information is determined based on the audio type information and present matching relationship information, paragraphs [0034-0036]). In regard to claim 12, Tang discloses the classification information is obtained from first information and second information, the first information being obtained by inputting classification target sound data to a machine learning model generated by performing machine learning with use of sound data and teacher data, which are training data, and the second information being obtained by classifying the classification target sound data based on information registered in advance (matching relationship information is pre-stored based on classifying the target audio data, paragraph [0035]; the classifying performed using a trained audio classification model, paragraphs [0028-0031]). In regard to claim 13, Tang discloses the input data includes data indicating a preference of the user (see Fig. 3, user feedback indicating user preferences is received, paragraphs [0064-0068]), in identifying information of the user, the preference of the user is identified from the input data, as information of the user (the input information of the user identifies user preferences, paragraph [0068]), and in identifying sound data that matches the user, a degree of similarity between the preference of the user and the classification information is determined with respect to each sound data classified in advance, and identifies sound data for which the predetermined degree of similarity satisfies a set condition is identified (a matching degree in the matching relationship information is determined based on the input user preferences, paragraph [0067]; and matching audio which has a matching degree that satisfies a preset condition is selected, paragraph [0036]). In regard to claim 15, Tang discloses the input data is voice data of the user (user audio comprising a voice, paragraphs [0020-0021]), in identifying information of the user, an age of the user is identified as the information of the user from the input data that is the voice data by performing age analysis on the user (the user’s age is identified from the voice, paragraph [0025]); and in identifying sound data that matches the user, the sound data that matches the user is identified based on the identified age of the user (the age information is used as audio type information for matching, paragraphs [0025] and [0034-0035]) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 4, 9, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang, in view of Deshpande et al. (U.S. Patent Application Pub. No. 2020/0286506, hereinafter “Deshpande”) . In regard to claim 4, Tang discloses the input data is voice data of the user (user audio comprising a voice, paragraphs [0020-0021]), the one or more processors further: identifies, as the information of the user, properties of the user from the input data that is the voice data by performing property analysis on the user (various properties of the voice are determined using classification models, such as gender, age, timbre, etc. as audio type information, paragraphs [0025] and [0038]); and identifies the sound data that matches the user based on the identified property of the user (the audio type information is used to identify matching audio, paragraph [0034]). Tang additionally discloses the voices express different emotions (paragraph [0003]). However, Tang does not expressly disclose identifying an emotion from the input data. Deshpande discloses a method wherein input data is voice data of the user (Fig. 2, a speech signal is collected, paragraph [0024]), the one or more processors further: identifies, as the information of the user, an emotion of the user from the input data that is the voice data by performing emotion analysis on the user (features are compared to an emotion recognition model to determine an emotion present in the speech signal, paragraph [0024]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify an emotion of the user by performing emotion analysis on the user, because it would allow the user to determine a matching voice that expressed emotions desired by the user. In regard to claim 9, Tang discloses the input data is voice data of the user (user audio comprising a voice, paragraphs [0020-0021]), in the identifying information of the user, properties of the user are identified as the information of the user from the input data that is the voice data by performing property analysis on the user (various properties of the voice are determined using classification models, such as gender, age, timbre, etc. as audio type information, paragraphs [0025] and [0038]); and in the identifying sound data that matches the user, the sound data that matches the user is identified based on the identified property of the user (the audio type information is used to identify matching audio, paragraph [0034]). Tang additionally discloses the voices express different emotions (paragraph [0003]). However, Tang does not expressly disclose identifying an emotion from the input data. Deshpande discloses a method wherein input data is voice data of the user (Fig. 2, a speech signal is collected, paragraph [0024]), in the identifying information of the user, an emotion of the user is identified as the information of the user from the input data that is the voice data by performing property analysis on the user (features are compared to an emotion recognition model to determine an emotion present in the speech signal, paragraph [0024]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify an emotion of the user by performing emotion analysis on the user, because it would allow the user to determine a matching voice that expressed emotions desired by the user. In regard to claim 14, Tang discloses the input data is voice data of the user (user audio comprising a voice, paragraphs [0020-0021]), in identifying information of the user, properties of the user are identified as the information of the user from the input data that is the voice data by performing property analysis on the user (various properties of the voice are determined using classification models, such as gender, age, timbre, etc. as audio type information, paragraphs [0025] and [0038]); and in identifying sound data that matches the user, the sound data that matches the user is identified based on the identified property of the user (the audio type information is used to identify matching audio, paragraph [0034]). Tang additionally discloses the voices express different emotions (paragraph [0003]). However, Tang does not expressly disclose identifying an emotion from the input data. Deshpande discloses a method wherein input data is voice data of the user (Fig. 2, a speech signal is collected, paragraph [0024]), the one or more processors further: in identifying information of the user, an emotion of the user is identified as the information of the user from the input data that is the voice data by performing property analysis on the user (features are compared to an emotion recognition model to determine an emotion present in the speech signal, paragraph [0024]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify an emotion of the user by performing emotion analysis on the user, because it would allow the user to determine a matching voice that expressed emotions desired by the user . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Manas et al., Dinnage et al., Wright et al., Latta et al., Marelus et al., and Ovide disclose additional matchmaking systems that utilize voice. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN LOUIS ALBERTALLI whose telephone number is (571)272-7616. The examiner can normally be reached M-F 8AM-3PM, 4PM-5PM. 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, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. BLA 3/30/26 /BRIAN L ALBERTALLI/ Primary Examiner, Art Unit 2656 Application/Control Number: 18/846,308 Page 2 Art Unit: 2656 Application/Control Number: 18/846,308 Page 3 Art Unit: 2656 Application/Control Number: 18/846,308 Page 4 Art Unit: 2656 Application/Control Number: 18/846,308 Page 5 Art Unit: 2656 Application/Control Number: 18/846,308 Page 6 Art Unit: 2656 Application/Control Number: 18/846,308 Page 7 Art Unit: 2656 Application/Control Number: 18/846,308 Page 8 Art Unit: 2656 Application/Control Number: 18/846,308 Page 9 Art Unit: 2656 Application/Control Number: 18/846,308 Page 10 Art Unit: 2656 Application/Control Number: 18/846,308 Page 11 Art Unit: 2656 Application/Control Number: 18/846,308 Page 12 Art Unit: 2656
Prosecution Insights