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 11/21/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 9, and 17 recites, “receive information for selecting a scene in which participants including a user and one or more machine learning models have a dialog with each other, and the machine learning models included in the participants”, “construct an environment in which the participants have a dialog with each other in the selected scene”, “acquire dialog content between the user and the machine learning models in the environment”, and “evaluate, based on an evaluation criterion for evaluating a dialog ability according to the dialog content, the dialog ability of the user from the acquired dialog content”.
The limitation of receiving scene information, as drafted, is a process, 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 “one or more memories storing instructions”, and “one or more processors”, nothing in the claim precludes the step from practically being performed in the mind. For example, “receive” in the context of this claim encompasses receiving information, which a human can do in the mind. Next, the limitation of constructing an environment, 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 the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “construct” in the context of this claim encompasses creating a background setting, which a human can do in the mind. Next, the limitation of acquiring dialog content, 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 the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “acquire” in the context of this claim encompasses receiving speech content, which a human can do in the mind. Lastly, the limitation of evaluating dialog content, 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 the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “evaluate” in the context of this claim encompasses analyzing text, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “one or more memories storing instructions” and “one or more processors” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using 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 is directed to an 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 the integration of the abstract idea into a practical application, the additional elements of using “one or more memories storing instructions” and “one or more processors” to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 2-8, 10-16, and 18-20 are also rejected for the same reasons provided in independent claim 1, 9, and 17 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1, 9, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Arslan et al. US 20200135041 A1 (hereinafter Arslan).
Regarding independent claims 1, 9, and 17 Arslan teaches a dialog ability enhancement assistance device comprising / a dialog ability enhancement assistance method executed by an information processing device, the method comprising / a non-transitory recording medium recording a computer program for causing a computer to execute:
one or more memories storing instructions (FIG. 7A); and
one or more processors configured to execute the instructions to (FIG. 7A):
receive information for selecting a scene in which participants including a user and one or more machine learning models have a dialog with each other, and the machine learning models included in the participants ([0011] “the testing module provides a test case scenario corresponding to the training scenario selected by the trainee user”; [0042] “various answers can be replicated by algorithms based on artificial intelligence and can be added to the training database”);
construct an environment in which the participants have a dialog with each other in the selected scene ([0042] “receiving a practice input from the trainee user to select one training scenario from the plurality of training scenarios”);
acquire dialog content between the user and the machine learning models in the environment ([0019] “initiating an interactive conversation between the trainee user and the interactive and automated training system corresponding to the one training scenario from the training database, wherein the interactive and automated training system plays a role of the customer in the interactive conversation”; [0048] “FIG. 3 shows that first the audio file is dissected into the customer dialogue and the employee dialogue, then the subparts of the customer dialogue and the employee dialogue are respectively indexed with respect to the time stamps or relative time stamps and time duration of each subpart”, examiner interprets the transcript/log shown as the dialog content); and
evaluate, based on an evaluation criterion for evaluating a dialog ability according to the dialog content, the dialog ability of the user from the acquired dialog content ([0012] “the testing module analyzes the response of the trainee user and is configured to provide a score based on a predetermined testing criteria”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-4, 10-12, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arslan in view of Koehler et al. US 8023636 B2 (hereinafter Koehler).
Regarding claims 2, 10, and 18, Arslan teaches all of the limitations of claim 1, 9, and 17 upon which claims 2, 10, and 18 depend.
Arslan fails to teach wherein the one or more processors are configured to further execute the instructions to: generate the scene from information indicating a feature of the user based on a scene generation criterion for generating the scene according to the feature of the user.
However, Koehler teaches wherein the one or more processors are configured to further execute the instructions to: generate the scene from information indicating a feature of the user based on a scene generation criterion for generating the scene according to the feature of the user ([Column 2, line 31-36] “The training program further enables training developers to create scenarios, and trainers and managers to change scenarios for the trainees to focus the training on specific areas, for example, to address a trainee's, demonstrated weaknesses, and to implement the new or modified scenario in real-time”).
Arslan in view of Koehler are considered to be analogous to the claimed invention because both are the same field of automated training systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of using real interactions with interactive and automated training system of Arslan with the technique of generating scenes from features of the user taught by Koehler in order to improve training call center agents at remote terminals networked to a database, based on verbal and/or textual interaction with simulated customers in dynamic scenarios (see Koehler [Column 1, line 14-17]).
Regarding claims 3, 11, and 19, Arslan in view of Koehler teaches all of the limitations of claim 2, 10, and 18, upon which claims 3, 11, and 19 depend.
Additionally, Koehler teaches wherein the information indicating the feature of the user indicates at least one of an age, a gender, an occupation, a preference, a personality, a record of the dialog content, and a record of an evaluation result of the dialog ability of the user ([Column 2, line 31-36] “The training program further enables training developers to create scenarios, and trainers and managers to change scenarios for the trainees to focus the training on specific areas, for example, to address a trainee's, demonstrated weaknesses, and to implement the new or modified scenario in real-time”, examiner interprets demonstrated weakness as the record of an evaluation result of the user).
Regarding claims 4, 12, and 20, Arslan in view of Koehler teaches all of the limitations of claim 2, 10, and 18, upon which claims 4, 12, and 20 depend.
Additionally, Koehler teaches wherein the acquisition means acquires information indicating a feature of the user including a preference of the user from the dialog content ([Column 10, line 62-64] “the user clicks on the designated portion of the display, such as specific text or an icon, representing the desired scenario, which activates the simulator module 230”).
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Arslan in view of Steedman Henderson US 20200152184 A1 (hereinafter Steedman Henderson).
Regarding claims 5 and 13, Arslan teaches all of the limitations of claim 1 and 9, upon which claims 5 and 13 depend.
Arslan fails to teach wherein the one or more processors are configured to further execute the instructions to: train the machine learning model by learning words and actions of a real or fictitious character
However, Steedman Henderson teaches wherein the one or more processors are configured to further execute the instructions to: train the machine learning model by learning words and actions of a real or fictitious character ([0080] “there is provided a method of training a dialogue system, using training data comprising user speech or text signals …”)
Arslan in view of Steedman Henderson are considered to be analogous to the claimed invention because both are the same field of automated training systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of using real interactions with interactive and automated training system of Arslan with the technique of training a model with words and actions taught by Steedman Henderson in order to improve a system for generating data for training a dialogue system (see Steedman Henderson [0002]).
Regarding claims 6 and 14, Arslan in view of Steedman Henderson teaches all of the limitations of claim 5 and 13, upon which claims 6 and 14 depend.
Additionally, Steedman Henderson teaches wherein the one or more processors are configured to further execute the instructions to: include, as a function of the machine learning model, a function of determining whether relevance between the dialog content and the scene satisfies a predetermined criterion and guiding a dialog with the user in such a way that the dialog content satisfies the predetermined criterion when the relevance does not satisfy the predetermined criterion ([0276] “Importance scores are predefined associated with each outcome. The further stage may comprise a rule which compares the importance scores of the selected outcomes and generates a final dialogue act comprising the outcome(s) with the highest score”).
Claims 7-8 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Arslan in view of Donovan et al. US 10600335 B1 (hereinafter Donovan).
Regarding claims 7 and 15, Arslan teaches all of the limitations of claim 1 and 9, upon which claims 7 and 15 depend.
Arslan fails to teach wherein the one or more processors are configured to further execute the instructions to: recommend at least one of the scene and the machine learning models to the user from a feature of the user based on a recommendation criterion for recommending at least one of the scene and the machine learning models to the user according to a feature of the user.
However, Donovan teaches wherein the one or more processors are configured to further execute the instructions to: recommend at least one of the scene and the machine learning models to the user from a feature of the user based on a recommendation criterion for recommending at least one of the scene and the machine learning models to the user according to a feature of the user ([Column 16, line 59-63] “Based on the score, the evaluation algorithm 309 further provides a signal that may be used to automatically adapt (change) the executing training scenario 330, or to suggest to observer/evaluator 22 to manually adapt the executing training scenario 330”).
Arslan in view of Donovan are considered to be analogous to the claimed invention because both are the same field of automated training systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of using real interactions with interactive and automated training system of Arslan with the technique of recommending scene information taught by Donovan in order to improve a computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group (see Donovan [Abstract]).
Regarding claims 8 and 16, Arslan in view of Donovan teaches all of the limitations of claims 7 and 15, upon which claims 8 and 16 depend.
Additionally, Donovan teaches wherein the one or more processors are configured to further execute the instructions to: manage information indicating the feature of the user in time series ([Column 12, line 1-8] “The training scenario/exercise module 300a then uses training exercise monitoring/control component 304 to monitor and automatically control the training exercise 330 and, in an aspect report, in real time, the trainee's or trainee group's progress. In addition, the training scenario/exercise module 300a provides a manual control feature that allows instructor/observer 22 to manually control all or part of the training exercise 330”); and
recommend at least one of the scene and the machine learning models to the user from a situation in which the feature of the user changes with a lapse of time based on the recommendation criterion for recommending at least one of the scene and the machine learning models to the user according to a situation in which the feature of the user changes with the lapse of time ([Column 29, line 43-59] “the scenario developer 24 generates a scenario script 333 for the base training scenario 331. The scenario script 333 may be a plain English-language statement of events (injections) and their timings, associated displays, indications, and signals that would be available to IT personnel experiencing the attack, expected actions and a time range for initiating and completing the expected actions, associated displays, indications, and signals resulting from the actions, and any network state changes that would occur as a result of the injections and the corresponding actions. Finally, the scenario script 333 may include one or more adaption decision points where a corresponding, executing training exercise 330 may automatically and dynamically adapt to a different path or sequence of events. The scenario developer 24 may provide the adaptation criteria at each adaptation decision point.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sullivan et al. (US 10818193 B1) teaches a communications training system is provided having a user interface, a computer-based simulator and a performance measurement database. The user interface is configured to receive a speech communication input from the user based on a training content and the computer-based simulator is configured to transform the speech communication to a text data whereby the text data can be aligned to performance measurement database values to determine a performance measure of the speech communication. The format of the text data and the performance measurement database values enable the speech communication to be aligned with predefined performance measurement database values representing expected speech communications for that training content.
Correia Gracio et al. (US 20180165979 A1) teaches an electronic device for collecting evidence during training sessions. The electronic device comprises a communications module for acquiring real-time training session data during a training session; a real-time data acquisition module for generating time-stamped training session data; an input unit for acquiring instructor rating data input by an instructor during the training session; a real-time instructor rating module for generating time-stamped instructor rating data; a storing module for storing real-time data of the training session in a data repository, including the time-stamped training session data and the time-stamped instructor rating data; a debriefing module for retrieving real-time data of a training session from the data repository, replay the real-time data on a display, and update the instructor rating data in the data repository with updated instructor rating data received from an instructor during the replay.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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