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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/22/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 5, and 7 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 5, and 7 of copending Application Number 18/840,566 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because obvious variations of each other.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding Claim 1 (drawn to a device):
Current Application
Claim 1:
A learning device comprising:
processing circuitry configured to:
acquire speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and a classification label of a quick response included in the conversation data of the listener; and
create a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the classification label of the quick response as correct answer data.
‘566
Claim 1:
A learning device comprising:
processing circuitry configured to: acquires acquire speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and emotion information of the listener;
and create a learned model of estimating a quick response of the listener to a conversation of the speaker using the acquired information with a quick response included in the conversation data of the listener as correct answer data.
Regarding Claim 5 (drawn to a method):
Current Application
Claim 5:
A learning method performed by a learning device, the learning method comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and a classification label of a quick response included in the conversation data of the listener; and
creating a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the classification label of the quick response as correct answer data
‘566
Claim 5:
A learning method performed by a learning device, the learning method comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and emotion information of the listener; and
creating a learned model of estimating a quick response of the listener to a conversation of the speaker using the acquired information with a quick response included in the conversation data of the listener as correct answer data.
Regarding Claim 7 (drawn to a non-transitory CRM):
Current Application
Claim 7:
A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and a classification label of a quick response included in the conversation data of the listener; and
creating a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired by the acquisition step with the classification label of the quick response as correct answer data.
‘566
Claim 7:
A non-transitory computer-readable recording medium storing therein a learning program for causing a computer to function as the learning device according to claim 1.
Claim 2 of the current application corresponds to claims 2 of copending Application Number 18/840,566.
Claim 1, 2, 5, and 7 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 5, and 7 of copending Application No. 18/840,504 in view of Kamiyama et al. (US 2020/0312352).
This is a provisional nonstatutory double patenting rejection.
Regarding Claim 1 (drawn to a device):
Current Application
Claim 1:
A learning device comprising:
processing circuitry configured to:
acquire speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener,
create a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired of the quick response as correct answer data.
‘504
Claim 1:
A learning device comprising:
processing circuitry configured to: acquire speech data of a speaker, information on the speaker, conversation data of a listener, and information on the listener;
and create a learned model of estimating a quick response of the listener to a conversation of the speaker using the information acquired with a quick response included in the conversation data of the listener as correct answer data.
response included in the conversation data of the listener as correct answer data.
However, copending Application No. 18/840,504 fails to teach a classification label of a quick response included in the conversation data of the listener.
Kamiyama et al. teaches a classification label of a quick response included in the conversation data of the listener (Specifically, first, sets each including feature amounts (here, the mean mean(c), the variance value var(c), the speaking speed mean(r)) obtained from a speech signal whose urgency level is known in advance, and information (correct answer label) indicating the urgency level of the speech signal are prepared as learning data) (page 3, paragraph [0047]).
Therefore it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have combined the teachings of copending application 18/840,504 with the teachings of Kamiyama to improve dialog between users by pre-determining the responses necessary depending on the dialog data.
Regarding Claim 5 (drawn to a method):
Current Application
Claim 5:
A learning method performed by a learning device, the learning method comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and a classification label of a quick response included in the conversation data of the listener; and
creating a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the quick response as correct answer data
‘504
Claim 5:
A learning method performed by a learning device, the learning method comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, and information on the listener; and
creating a learned model of estimating a quick response of the listener to a conversation of the speaker using the information acquired with a quick response included in the conversation data of the listener as correct answer data.
However, copending Application No. 18/840,504 fails to teach a classification label of a quick response included in the conversation data of the listener.
Kamiyama et al. teaches a classification label of a quick response included in the conversation data of the listener (Specifically, first, sets each including feature amounts (here, the mean mean(c), the variance value var(c), the speaking speed mean(r)) obtained from a speech signal whose urgency level is known in advance, and information (correct answer label) indicating the urgency level of the speech signal are prepared as learning data) (page 3, paragraph [0047]).
Therefore it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have combined the teachings of copending application 18/840,504 with the teachings of Kamiyama to improve dialog between users by pre-determining the responses necessary depending on the dialog data.
Regarding Claim 7 (drawn to a non-transitory CRM):
Current Application
Claim 7:
A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and a classification label of a quick response included in the conversation data of the listener; and
creating a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired by the acquisition step with the classification label of the quick response as correct answer data.
‘504
Claim 7:
A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising:
acquiring speech data of a speaker, information on the speaker, conversation data of a listener, and information on the listener; and
creating a learned model of estimating a quick response of the listener to a conversation of the speaker using the information acquired with a quick response included in the conversation data of the listener as correct answer data.
However, copending Application No. 18/840,504 fails to teach a classification label of a quick response included in the conversation data of the listener.
Kamiyama et al. teaches a classification label of a quick response included in the conversation data of the listener (Specifically, first, sets each including feature amounts (here, the mean mean(c), the variance value var(c), the speaking speed mean(r)) obtained from a speech signal whose urgency level is known in advance, and information (correct answer label) indicating the urgency level of the speech signal are prepared as learning data) (page 3, paragraph [0047]).
Therefore it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have combined the teachings of copending application 18/840,504 with the teachings of Kamiyama to improve dialog between users by pre-determining the responses necessary depending on the dialog data.
Claim 2 of the current application corresponds to claims 2 of copending Application Number 18/840,504.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 5, and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 5,and 7 relate to the statutory category of method/process and machine/apparatus.
The independent claims 1, 5, and 7 recite acquir(ing) speech data of a speaker, information on the speaker, conversation data of a listener, and a classification label of a quick response included in the conversation data of the listener; and creat(ing) a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the classification label of the quick response as correct answer data.
The limitations of claims 1, 5, and 7 of “acquir(ing…”, and “creat(ing) as drafted covers mental activity. More specifically for claim 5, a human can obtain during a conversation between a speaker and a listener, information about both the speaker and the listener, what the conversation is about, and figure out the short and quick responses being given by the listener. The human can then use the quick responses that have been given previously given to create a model to predict what quick and short answers will be given by the listener.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 7 recite the additional elements of “processing circuitry” which are recited generally in the specification. For example, in paragraph 90023] of the as published specification, there is a description of using an electronic circuit such as a central processing unit (CPU) or a micro processing unit (MPU). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea..
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, the claim relates what determines the information about the speaker and listener. The claim relates to the mental observation of a person’s expression, motion, or voice. No additional elements are present.
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(s) 1, 2, 5, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnan et al. (US 2022/0093101) in view of Kamiyama et al. (US 2020/0312352).
Regarding Claim 1, Krishnan et al teaches a learning device comprising: processing circuitry (Each of these devices (110/120/125) may include one or more controllers/processors (2204/2304), which may each include a central processing unit (CPU) for processing data and computer-readable instructions) (page 60, paragraph [0517]) configured to: acquire speech data of a speaker (The device 110 may include audio capture component(s), such as a microphone or array of microphones of a device 110, captures audio 11 and creates corresponding audio data) (page ,6, paragraph [0060]) information on the speaker (The system may also process input data with user recognition component 295 to identify a user that is providing the input to the system (e.g., identify which user is speaking)) (page 18, paragraph [0155]), conversation data of a listener(user-to-user dialog) (page 0341]) (in the dialog, one user is a speaker and the other user is a listener), information on the listener (The system may update information relevant to an ongoing user-to-user conversation by updating data for the conversation as the conversation is ongoing) (page 38, paragraph [0341]), and a classification label of a quick response included in the conversation data of the listener (The system may also be configured to maintain a natural pace during a conversation and to insert conversational cues (such as “uh huh,” “mm,” or the like) to indicate to the user that the system is maintaining a connection with the user(s) for purposes in participating in the dialog) (page 2, paragraph [0036]).
Krishnan et al fails to teach create a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the classification label of the quick response as correct answer data.
Kamiyama et al teaches create a learned model (The urgency level estimation model is generated by machine learning such as, for example, a support vector machine (SVM), a random forest, and a neural network) (page 3, paragraph [0047]) of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the classification label of the quick response as correct answer data (Specifically, first, sets each including feature amounts (here, the mean mean(c), the variance value var(c), the speaking speed mean(r)) obtained from a speech signal whose urgency level is known in advance, and information (correct answer label) indicating the urgency level of the speech signal are prepared as learning data) (page 3, paragraph [0047]).
Therefore it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have combined the teachings of copending application 18/840,504 with the teachings of Kamiyama to improve dialog between users by pre-determining the responses necessary depending on the dialog data).
Regarding Claim 2, Krishnan et al teaches the learning device, wherein the processing circuitry is further configured to acquire any one or more of an expression, a motion, and voice of the speaker as the information on the speaker (the system detects (174) first audio and image data reflecting speech/image(s) of a first user expression made by the first user 5. The first user expression may include speech, a gesture (such as a movement, facial expression, etc.) or the like) (page 5, paragraph [0054]), and acquire any one or more of the expression, the motion, and the voice of the speaker as the information on the listener (The system may also detect (182) second audio and image data reflecting speech/image(s) of a second expression made by the second user 6. The second user expression may include speech, a gesture (such as a movement, facial expression, etc.) or the like) (page 5, paragraph [0055]).
Claim 5 is rejected for the same reason as claim 1.
Regarding Claim 7, Krishnan et al teaches a non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process (Each of these devices (110/120/125) may include one or more controllers/processors (2204/2304), which may each include a central processing unit (CPU) for processing data and computer-readable instructions, and a memory (2206/2306) for storing data and instructions of the respective device) (page 60, paragraph [0517]) comprising: acquiring speech data of a speaker (The device 110 may include audio capture component(s), such as a microphone or array of microphones of a device 110, captures audio 11 and creates corresponding audio data) (page ,6, paragraph [0060]) information on the speaker (The system may also process input data with user recognition component 295 to identify a user that is providing the input to the system (e.g., identify which user is speaking)) (page 18, paragraph [0155]), conversation data of a listener(user-to-user dialog) (page 0341]) (in the dialog, one user is a speaker and the other user is a listener), information on the listener (The system may update information relevant to an ongoing user-to-user conversation by updating data for the conversation as the conversation is ongoing) (page 38, paragraph [0341]), and a classification label of a quick response included in the conversation data of the listener (The system may also be configured to maintain a natural pace during a conversation and to insert conversational cues (such as “uh huh,” “mm,” or the like) to indicate to the user that the system is maintaining a connection with the user(s) for purposes in participating in the dialog) (page 2, paragraph [0036]).
Krishnan et al fails to teach creating a learned model (The urgency level estimation model is generated by machine learning such as, for example, a support vector machine (SVM), a random forest, and a neural network) (page 3, paragraph [0047])of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired by the acquisition step with the classification label of the quick response as correct answer data.
Kamiyama et al teaches creating a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired by the acquisition step with the classification label of the quick response as correct answer data (Specifically, first, sets each including feature amounts (here, the mean mean(c), the variance value var(c), the speaking speed mean(r)) obtained from a speech signal whose urgency level is known in advance, and information (correct answer label) indicating the urgency level of the speech signal are prepared as learning data) (page 3, paragraph [0047]).
Therefore it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to have combined the teachings of copending application 18/840,504 with the teachings of Kamiyama to improve dialog between users by pre-determining the responses necessary depending on the dialog data.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ishii et al. (US 2020/0372915) discloses nonverbal information generation.
Ishii et al. (US 2020/0401794) discloses nonverbal information generation.
Ishii et al. (US 2021/00370519 discloses nonverbal information generation.
Bratt et al. (US 2022/0115001) discloses understanding and generating human conversational cues.
Krishnan et al. (US 2022/0093094) discloses dialog management for multiple users.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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, Paras D Shah can be reached at (571}270-1650. 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.
/SATWANT K SINGH/Primary Examiner, Art Unit 2653