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
In response to the Office Action mailed February 23, 2026, applicant submitted an amendment filed on May 1, 2026, in which the applicant amended and requested reconsideration.
Response to Arguments
Applicants argue that the prior art cited fails to teach the claims as amended. Applicants’ arguments are persuasive, but are moot in view of new ground of rejection. It was requested for the double patenting to be held in abeyance and the 101 rejection remains for reasons as set forth below. Furthermore, based on the amendments, the LCD is a generic processor, determining if a user is excited is mental processing and a backchannel response is merely data signaling that a user is engaged, understanding and/or paying attention.
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.
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Claim 1-8 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 5 and 7 of copending Application No. 18/840,512 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because
Regarding claims 1 and 5, Krishanan discloses a learning device and method, hereinafter referenced as a method 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 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.
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.
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.
Regarding claims 2 and 4, Krishanan discloses a method 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, and acquire any one or more of the expression, the motion, and the voice of the speaker as the information on the listener.
2. The learning device according to claim 1, 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, and acquire any one or more of the expression, the motion, and the voice of the speaker as the information on the listener.
Regarding claims 3 and 6, Krishanan discloses a learning device and method, hereinafter referenced as a estimation device comprising:
processing circuitry (p. 0081) configured to:
acquire speech data of a speaker, information on the speaker, and emotion information of a listener); and
input the acquired information as input data to a learned model of predicting a quick response of the listener from a conversation content of the speaker and estimate the quick response of the listener to a conversation of the speaker.
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.
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.
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.
Regarding claim 7, Krishanan discloses 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.
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.
Regarding claim 8, Krishanan discloses a non-transitory computer-readable recording medium storing therein an estimation program for causing a computer to function as the estimation device according to claim 3.
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.
Regarding claims 1 and 5, Krishanan discloses a learning device and method, hereinafter referenced as a method 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 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 (claims 1, 5 and 7).
Regarding claims 2 and 4, Krishanan discloses a method 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, and acquire any one or more of the expression, the motion, and the voice of the speaker as the information on the listener (claim 2).
Regarding claims 3 and 6, Krishanan discloses a learning device and method, hereinafter referenced as a estimation device comprising:
processing circuitry (p. 0081) configured to:
acquire speech data of a speaker, information on the speaker, and emotion information of a listener); and
input the acquired information as input data to a learned model of predicting a quick response of the listener from a conversation content of the speaker and estimate the quick response of the listener to a conversation of the speaker (claims 1, 5 and 7).
Regarding claim 7, Krishanan discloses 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 7).
Regarding claim 8, Krishanan discloses a non-transitory computer-readable recording medium storing therein an estimation program for causing a computer to function as the estimation device according to claim 3 (claim 7).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claims 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. The claims are directed to the abstract idea of determining a quick response, as explained in detail below.
The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “circuity and a LCD” nothing in the claim element precludes the steps from practically being performed by mental processing. For example, the language, 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 including excitement (can be done by a user determining that a user is speaking, obtaining information from the user and determining the user emotions), creating/predicting a learned model of estimating a backchannel quick response of the listener to a conversation of the speaker using the acquired information with a backchannel quick response included in the conversation data of the listener as correct answer data (can be done by determining how to respond to the user based on the obtained data) and outputting a quick response (can be done by a user outputting data). The present claim language under its broadest reasonable interpretation, covers performance of mental processing and recites generic computer components, which all falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements which are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
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) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnan et al. (PGPUB 2022/0093101), hereinafter referenced as Krishnan in view of Finley et al. (PGPUB 2019/00065464), hereinafter referenced as Finley.
Regarding claims 1 and 5, Krishanan discloses a learning device and method, hereinafter referenced as a method comprising:
processing circuitry (p. 0081) configured to:
acquire speech data of a speaker (microphone to capture audio; p. 0060), information on the speaker, conversation data of a listener (dialog of users; p. 0155), information on the listener (information relevant to an ongoing user-to-user conversation; p. 0341-0342), and emotion information of the listener (emotional state of the user(s); p. 0092, 0293, 0306-0318);
create a learned model of estimating a backchannel quick response (p. 0040, 0349) 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 (train model for interject with responses such as yes, no, uh huh, mmm and other conversational cues; p. 0036, 0306-0328); and
output the estimated backchannel quick response via a liquid crystal display (LCD) screen (p. 0349-0350). It is noted that Krishanan teaches detecting excitement (p. 0476, 0490, 0502-0503), but does not specifically teach that the emotion includes excitement of the listener.
Finely discloses a method wherein the emotion includes excitement of the listener (p. 0115), to assist with communicating information.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to determine current behavioral, psychological and speech styles characteristics of a speaker in a given situation and context by analyzing the speech utterances of the speaker.
Regarding claims 2 and 4, Krishanan discloses a method 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, and acquire any one or more of the expression, the motion, and the voice of the speaker as the information on the listener (emotional state of the user(s); p. 0092, 0293, 0306-0318).
Regarding claims 3 and 6, Krishanan discloses a learning device and method, hereinafter referenced as an estimation device comprising:
processing circuitry (p. 0081) configured to:
acquire speech data of a speaker (microphone to capture audio; p. 0060), information on the speaker (information relevant to an ongoing user-to-user conversation; p. 0341-0342), and emotion information of a listener (emotional state of the user(s); p. 0092, 0293, 0306-0318); and
input the acquired information as input data to a learned model of predicting a quick response of the listener from a conversation content of the speaker and estimate the quick response of the listener to a conversation of the speaker (train model for interject with responses such as yes, no, uh huh, mmm and other conversational cues; p. 0036, 0149-0150, 0306-0328, 0478).
Regarding claim 7, Krishanan discloses 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 (p. 0527).
Regarding claim 8, Krishanan discloses a non-transitory computer-readable recording medium storing therein an estimation program for causing a computer to function as the estimation device according to claim 3 (p. 0527).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This information has been detailed in the PTO 892 attached (Notice of References Cited).
Merrill teaches determining emotion responses of a listener.
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 JAKIEDA R JACKSON whose telephone number is (571)272-7619. The examiner can normally be reached Mon - Fri 6:30a-2:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571.272.5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAKIEDA R JACKSON/Primary Examiner, Art Unit 2657
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