Notice of Pre-AIA or AIA Status
1. 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
2. The information disclosure statement (IDS) submitted on October 17, 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
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Independent claim 1 recites “A learning device comprising: an acquisition unit that acquires a content of a command input by a user to a predetermined information processing system; and a learning unit that in a case where it is determined that the command includes an unrecognizable target, learns recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system”.
The limitations “an acquisition unit that acquires a content of a command input by a user to a predetermined information processing system; and a learning unit that in a case where it is determined that the command includes an unrecognizable target, learns recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system” as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 1 recites “A learning device comprising:…” this limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 1 do not amount to 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. Claim 1 does not recite any additional limitations. The claim as drafted, is not patent eligible.
The Independent claim 16 recites “A learning method comprising: by a computer, acquiring a content of a command input by a user to a predetermined information processing system; and learning, in a case where it is determined that the acquired command includes an unrecognizable target, recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system”.
The limitations “by a computer, acquiring a content of a command input by a user to a predetermined information processing system; and learning, in a case where it is determined that the acquired command includes an unrecognizable target, recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system” as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 16 recites “A learning method comprising…” this limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 16 do not amount to 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. Claim 16 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim Rejections - 35 USC § 102
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claims 1-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miller (U.S. Publication No. 20200310749).
Regarding claim 1, Miller discloses a learning device comprising:
an acquisition unit that acquires a content of a command input by a user to a predetermined information processing system ([0016] - during a user interaction with a computing device, a user may provide a command by speaking (i.e., a voice command), by gesturing, touch, or other input means to the computing device. The command may be received by a user interface or sensor, such as a microphone, camera, touch-screen, keyboard, or other input means or user-interface. The computing device may comprise a smart device, such as a smart speaker, mobile device with an app or interactive service, a digital assistant program operating on a computing device, or other examples of computing devices described herein);
and a learning unit that in a case where it is determined that the command includes an unrecognizable target, learns recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system ([0019] - the embodiments described herein also improve the user experience of interacting with these computing devices, or the digital assistant applications operating thereon. Instead of ignoring an unknown command or merely responding to the user with an indication that the command is unknown, the embodiments described herein monitor user activity performed subsequent to receiving the unknown command in order to learn operations performed by the user and associate those operations with the command).
Regarding claim 2, Miller discloses the learning device, wherein the learning unit learns the recognition information as a different expression of the target ([0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 3, Miller discloses the learning device according to claim 3, wherein the learning unit, after it is determined that the command includes an unrecognizable target, learns the recognition information on the basis of information specified by selection operation by the user ([0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 4, Miller discloses the learning device, wherein the learning unit, in a case where it is determined that the command includes an unrecognizable target, estimates a content to be learned as the recognition information on the basis of a use history of the information processing system by the user when the unrecognizable target has been detected in the past ([0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 5, Miller discloses the learning device, wherein the learning unit, in a case where the user instructs to learn the specified information as a different expression of the target, learns the specified information as a different expression of the target ([0016] - The user command may comprise a request to perform a particular task, such as, by way of example and without limitation, turning on the lights, playing or recording a television show, launching an application on a computing device, navigating to a website, scheduling a meeting, booking a meeting or venue, configuring a setting on a computing device or appliance, or performing an operation or series of operations utilizing one or more computing devices (e.g., launching an app or navigating to a particular website and initiating an operation, such as making a restaurant reservation, travel booking, purchase, playing a video, retrieving and presenting or reading information such as a news feed, or other operation(s)). The task performed in response to a user command may comprise a set of one or more operations that are carried out via the computing device or another computing device. [0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 6, Miller discloses the learning device, wherein the learning unit learns the recognition information as information for use to read the target as another target ([0020] - From this monitored user activity data, the particular operations associated with performing the task corresponding to the user command, can be learned. In this way, the computing system (or digital assistant application) learns the operations(s) to be performed in response to the user command, in order to facilitate carrying out the task).
Regarding claim 7, Miller discloses the learning device, wherein the learning unit, in a case where it is determined that the command includes an unrecognizable target, estimates the another target to be associated with the target on the basis of a use history of the information processing system by the user when the unrecognizable target has been detected in the past ([0016] - The user command may comprise a request to perform a particular task, such as, by way of example and without limitation, turning on the lights, playing or recording a television show, launching an application on a computing device, navigating to a website, scheduling a meeting, booking a meeting or venue, configuring a setting on a computing device or appliance, or performing an operation or series of operations utilizing one or more computing devices (e.g., launching an app or navigating to a particular website and initiating an operation, such as making a restaurant reservation, travel booking, purchase, playing a video, retrieving and presenting or reading information such as a news feed, or other operation(s)). The task performed in response to a user command may comprise a set of one or more operations that are carried out via the computing device or another computing device. [0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 8, Miller discloses the learning device, wherein the learning unit, in a case where it is determined that the command includes an unrecognizable target, estimates the another target to be associated with the target on the basis of a use history of the information processing system by the user or by other user different from the user ([0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 9, Miller discloses the learning device, wherein the learning unit, in a case where the user instructs to associate the estimated another target with the target, associates the estimated another target with the target ([0125] - mart speaker 385 (or the digital assistant application operating thereon, may learn to respond to the command 381 by receiving updated instructions. For instance, smart speaker 385 may access a server of response profiles (or may receive additional response profiles via a device update procedure) thereby enabling the smart speaker 385 to respond to the user command 381. The response profile may have been generated from monitoring the user activity of other users making the same or similar command. In this way, smart speaker 385 (or a similar user device having a digital assistant application operating thereon) may be enabled to correctly respond to a user command on the first instance of a user providing the command, because the device has effectively learned from other users. Although the example embodiment of FIG. 3B is implemented on a smart speaker, it is contemplated that this embodiment, and any of the smart speaker embodiments described herein, may be implemented using a digital assistant running on a user device (such as a user's mobile computing device, or other user device 102a, as described in FIG. 1)).
Regarding claim 10, Miller discloses the learning deice, wherein the acquisition unit acquires a content of a voice command input by the user, and the learning unit, in a case where it is determined that the voice command includes an unrecognizable target, learns a call corresponding to the target on the basis of operation of the information processing system by the user or a use history of the information processing system ([0031] - The user data may be received by collecting user data with one or more sensors or components on user device(s) associated with a user. Examples of user data, also described in connection to user-data collection component 210 of FIG. 2, may include information about the user device(s) and response activity information associated with the user devices (e.g., user-interactions with the device(s), app usage, online activity, searches, calls, usage duration, and other user-interaction data). The received user data may be monitored, analyzed (e.g., feature extraction) and information about the response activity event(s) may be stored (e.g., in a user profile, such as user profile 240 of FIG. 2) to facilitate a pattern analysis. [0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 11, Miller discloses the learning device, wherein the learning unit, as a call corresponding to the target, learns a character string indicating the call or voice data corresponding to the call ([0077] - response activity patterns engine 260 may comprise semantic information analyzer 262, features similarity identifier 264, and response activity pattern determiner 266. Semantic information analyzer 262 is generally responsible for determining semantic information associated with the response activity-related features identified by user activity monitor 280. For example, while a response-activity feature may indicate a specific device the user interacted with, semantic analysis may determine how the device is interacted with or characteristics of the interaction. Semantic information analyzer 262 may determine additional response activity-related features semantically related to the response activity event, which may be used for identifying response activity patterns).
Regarding claim 12, Miller discloses the learning device, wherein the learning unit, in a case where it is determined that the voice command includes an unrecognizable target, learns information specified by an input means other than utterance by the user as the recognition information ([0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 13, Miller discloses the learning device, wherein the learning unit, in the same user interface layer as the voice command, in a case where information corresponding to the target is specified by an input means other than utterance by the user, and the same execution content as the voice command is executed for the specified target, learns a call corresponding to the target ([0031] - The user data may be received by collecting user data with one or more sensors or components on user device(s) associated with a user. Examples of user data, also described in connection to user-data collection component 210 of FIG. 2, may include information about the user device(s) and response activity information associated with the user devices (e.g., user-interactions with the device(s), app usage, online activity, searches, calls, usage duration, and other user-interaction data). The received user data may be monitored, analyzed (e.g., feature extraction) and information about the response activity event(s) may be stored (e.g., in a user profile, such as user profile 240 of FIG. 2) to facilitate a pattern analysis. [0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine [0077] - response activity patterns engine 260 may comprise semantic information analyzer 262, features similarity identifier 264, and response activity pattern determiner 266. Semantic information analyzer 262 is generally responsible for determining semantic information associated with the response activity-related features identified by user activity monitor 280. For example, while a response-activity feature may indicate a specific device the user interacted with, semantic analysis may determine how the device is interacted with or characteristics of the interaction. Semantic information analyzer 262 may determine additional response activity-related features semantically related to the response activity event, which may be used for identifying response activity patterns).
Regarding claim 14, Miller discloses the learning device, further comprising: a presentation unit that in a case where recognition information is learned by the learning unit and then the recognition information is recognized, presents the target corresponding to the recognition information ([0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 15, Miller discloses the learning device, wherein the acquisition unit acquires a content of the command based on at least one of a gesture, a line of sight, and a brain wave signal of the user, and the learning unit, in a case where it is determined that the command includes an unrecognizable target, learns at least one of a gesture, a line of sight, and a brain wave signal of the user corresponding to the target on the basis of operation of the information processing system by the user or a use history of the information processing system ([0031] - The user data may be received by collecting user data with one or more sensors or components on user device(s) associated with a user. Examples of user data, also described in connection to user-data collection component 210 of FIG. 2, may include information about the user device(s) and response activity information associated with the user devices (e.g., user-interactions with the device(s), app usage, online activity, searches, calls, usage duration, and other user-interaction data). The received user data may be monitored, analyzed (e.g., feature extraction) and information about the response activity event(s) may be stored (e.g., in a user profile, such as user profile 240 of FIG. 2) to facilitate a pattern analysis. [0033] - From the activity logs or user activity data, historical response activity event(s) may be determined and provided to response activity patterns engine. Based on an analysis of historical response activity event(s), and in some cases current sensor data regarding response activity event(s), a set of one or more likely response activity patterns may be determined. In particular, the response activity patterns engine may conduct an analysis of the historical response activity event(s) information to identify response activity patterns, which may be determined by detecting repetitions of similar user actions or routine).
Regarding claim 16, Miller discloses a learning method comprising:
by a computer, acquiring a content of a command input by a user to a predetermined information processing system ([0016] - during a user interaction with a computing device, a user may provide a command by speaking (i.e., a voice command), by gesturing, touch, or other input means to the computing device. The command may be received by a user interface or sensor, such as a microphone, camera, touch-screen, keyboard, or other input means or user-interface. The computing device may comprise a smart device, such as a smart speaker, mobile device with an app or interactive service, a digital assistant program operating on a computing device, or other examples of computing devices described herein);
and learning, in a case where it is determined that the acquired command includes an unrecognizable target, recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system ([0019] - the embodiments described herein also improve the user experience of interacting with these computing devices, or the digital assistant applications operating thereon. Instead of ignoring an unknown command or merely responding to the user with an indication that the command is unknown, the embodiments described herein monitor user activity performed subsequent to receiving the unknown command in order to learn operations performed by the user and associate those operations with the command).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Colafrancesco (U.S. Patent No. 12548564) teaches a system and method for controlling a plurality of devices. VanBlon (U.S. Publication No. 20150161984) teaches adaptively learning vocabulary for completing speech recognition commands.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN DANIEL KIM whose telephone number is (571) 272-1405. The examiner can normally be reached on Monday - Friday 9:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached on (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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/ETHAN DANIEL KIM/
Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658
Prosecution Insights