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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1 recites “voice activation device”. (A) the limitation uses the generic term “device”; (B) the device is for receiving one or more voice commands; (C) there is no further structure in the claim to perform the claimed action. The voice activation device will be interpreted as a microphone or functional equivalent as described in ¶0027 of the specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 28 and 31 are no longer rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
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 1, 6-7, 10, 21-22 and 34-36 are rejected under 35 U.S.C. 103 as being unpatentable over Goddard (US20180284718A1) in view of Ibrahimov (EP4011567A1) and Minami (US20250196341A1).
Claim 1
Goddard teaches a voice activated and hands-free method (¶0056 teaches the use of an audio input device such as a voice recognition system and a microphone. A microphone is hands free.) of controlling positioning of a work piece (¶0004 teaches the invention is related to a workpiece positioning system.) held by a positioner for welding to be done on the work piece (Figure 1 teaches a positioning system (100) for holding a workpiece to be welded (¶0018).), the method comprising: transmitting one or more voice commands from an operator via a voice activation device to a PLC (¶0021 teaches a PLC is used as part of the interface controller that interfaces with external systems. ¶0055 teaches user input devices include a microphone, which is a voice activation device.)raising, lowering, and/or rotating the positioner (¶0018 teaches the positioner has a linear axis (for raising and lowering) and a rotational axis.) by executable instructions from the PLC (¶0021 teaches the interface controller includes a PLC. ¶0028 teaches the positioning system (100), which includes the interface controller (180) receives commands to move the workpiece.) so as to raise, lower and/or rotate the work piece into a position for the welding to be done on the work piece (¶0018 teaches the positioning system holds a workpiece to be welded.), without manual adjustment of the work piece (The voice commands and welding job sequencer do not require manual adjustment of the workpiece.) and providing real time audio feedback and alerts to the operator from the PLC. (¶0029 teaches that the PLC provides feedback to the sequencer during the process. ¶0057 teaches the user output devices on the sequencer can be audio output devices.)
Goddard does not disclose the PLC having voice activated software and AI-enabled voice control; translating the one or more voice commands to a machine-readable language and/or a communication protocol; or the instructions from the PLC are selected from an action library.
However, Ibrahimov teaches a robotic controller (200) having voice activated software (240/241) translating the one or more voice commands to a machine-readable language and/or a communication protocol and select a command from an action library. (¶0029 teaches that the robot control unit (200) is used to processing voice inputs and convert them to text. The text is then parsed (242) to identify potential commands stored in a library (223) and then send said command to the controller (210) to generate a robot command corresponding to the voice command. A robot command is in machine readable language.)
One of ordinary skill would have been motivated to apply the known voice recognition and processing technique of Ibrahimov to the system of Goddard in order to process detected voice inputs to determine the content thereof. (See Ibrahimov ¶0029)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known voice recognition and processing technique of Ibrahimov to the system of Goddard because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is the PLC/controller of Goddard will have voice activated software and the ability to translate/convert the voice commands into instructions for the positioner.
Goddard in view of Ibrahimov does not explicitly disclose an AI-enabled voice control.
However, Minami teaches AI-enabled voice control and utilizing deep learning within the AI-enabled voice control to translate one or more voice commands. (Figure 1 teaches a control device (11) that includes a voice recognition unit (115) and communicates with a natural language processing system (50). Both the voice recognition unit (115) and the NLP (50) use deep learning (¶0042 and ¶0033) to recognize the text. The controller (11) then uses the response from the NPL system (50) to generate a command for the robot (15) (¶0038).)
One of ordinary skill would have been motivated to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov in order to provide a controller that enables a robot to work flexibly and properly as intended in response to user instructions in natural language.
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the combined system of Goddard and Ibrahimov will utilize AI technologies to perform the voice to machine command translations.
Claim 6
Goddard in view of Ibrahimov and Minami teaches the method of claim 1, wherein the feedback comprises one or more of:(a) a ready to move signal; (b) a move command signal; (c) a fault signal comprising identification of a fault and information regarding how to rectify the fault; and (d) a confirmation signal regarding command execution. (Goddard, ¶0029 teaches that the PLC provides feedback regarding a move being properly completed. This is an example of either a ready to move, move command, or confirmation signal.)
Claim 7
Goddard in view of Ibrahimov and Minami teaches the method of claim 1, further comprising providing one or more real-time verbal alerts to the operator wherein the one or more verbal alerts comprises one or more of:(a) a machine status alert; (b) a maintenance alert; and (c) a safety alert. (Goddard, ¶0029 teaches that the PLC provides feedback regarding a move being properly completed. This is an example of a machine status alert.)
Claim 10
Goddard in view of Ibrahimov and Minami teaches the method of claim 1,wherein the AI-enabled voice controller is housed within an industrial enclosure. (Goddard, which teaches the PLC that is modified by Hines to arrive at the AI-enabled voice controller for Claim 1, teaches an enclosure for the PLC (180) in Figure 1.)
Claim 21
Goddard in view of Ibrahimov and Minami teaches the method of claim 1 wherein the voice activation device is hands free. (Goddard ¶0056 teaches the sequencer receives input from a microphone. This is a hands-free, voice activated device.)
Claim 22
Goddard in view of Ibrahimov and Minami teaches the method of claim 1 wherein the voice activation device is wearable. (Goddard ¶0056 teaches the sequencer receives input from a microphone. Hines teaches the use of a wearable microphone/headset (¶0157).)
Claim 34
Goddard teaches an industrial control process for moving a work piece mounted on an adjustable positioner (Figure 1 teaches a positioning system (100) for holding a workpiece to be welded (¶0018).), comprising: a voice controller receives human operator voice commands (¶0056 teaches the user interface includes a voice recognition system and microphone. The input device is used to input information to the sequencer, which sends commands to the positioning system (100) See ¶0028.); actuating mechanical adjustment of the positioner using executable instructions from the PLC (¶0021 teaches the controller (180) of the positioning system (100) is a PLC. ¶0028 teaches the sequencer (which receives voice input (¶0056)) commands the positioning system to move (mechanically adjust) the workpiece.); generating real time audible feedback and alerts to the human operator (¶0029 teaches that the PLC provides feedback to the sequencer during the process. ¶0057 teaches the user output devices on the sequencer can be audio output devices.); and the process being voice activated and hands-free. (¶0056 teaches the sequencer receives input from a microphone. This is a hands-free, voice activated device.)
Goddard does not explicitly disclose initiating an input phase wherein a voice controller receives and processes human operator voice commands; translating the voice commands using the voice controller to provide communication protocol for a PLC; actuating mechanical adjustment of the positioner using executable instructions from the PLC based on the communication protocol and a library of adjustment options.
However, Ibrahimov teaches initiating an input phase wherein a voice controller receives and processes human operator voice commands (¶0057-0058 teach the use of a “hotword” which initiates the input phase for voice instructions.); translating the voice commands using the voice controller to provide communication protocol for a PLC (¶0029 teaches that the robot control unit (200) is used to processing voice inputs and convert them to text. The text is then converted to a robot command, which is a communication for a PLC. Applicant describes a communication protocol in ¶0132 of the specification as “Bluetooth, Wi-Fi, RFID, and/or wired connections”. Ibrahimov ¶0024 teaches the control unit (200) communicates with the robot using wireless or wired communication methods.); actuating mechanical adjustment of the positioner using executable instructions from the PLC based on the communication protocol and a library of adjustment options. (¶0029 teaches the text is parsed (242) to identify potential commands stored in a library (223) and then send said command to the controller (210) to generate a robot command corresponding to the voice command. ¶0056 teaches that the robot moves to different positions. ¶0024 teaches the control unit (200) communicates with the robot using wireless or wired communication methods.)
One of ordinary skill would have been motivated to apply the known voice recognition and processing system of Ibrahimov to the system of Goddard in order to process detected voice inputs to determine the content thereof. (See Ibrahimov ¶0029)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the voice recognition and processing system of Ibrahimov to the system of Goddard because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is the system of Goddard will have voice activated software and the ability to translate/convert the voice commands into instructions for the positioner.
Goddard in view of Ibrahimov does not explicitly disclose an AI-enabled voice control using integrated generative AI with deep learning and natural language processing.
However, Minami teaches an AI-enabled voice control using integrated generative AI with deep learning and natural language processing. (Figure 1 teaches a control device (11) that includes a voice recognition unit (115) and communicates with a natural language processing system (50). Both the voice recognition unit (115) and the NLP (50) use deep learning (¶0042 and ¶0033) and/or generative AI (¶0033) to recognize the text. The controller (11) then uses the response from the NPL system (50) to generate a command for the robot (15) (¶0038).)
One of ordinary skill would have been motivated to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov in order to provide a controller that enables a robot to work flexibly and properly as intended in response to user instructions in natural language.
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the combined system of Goddard and Ibrahimov will utilize AI technologies to perform the voice to machine command translations.
Claim 35
Goddard in view of Ibrahimov and Hines teaches the industrial control process of claim 34 wherein the communication protocol dictates the executable instructions for the positioner adjustment. (Applicant describes a communication protocol in ¶0132 of the specification as “Bluetooth, Wi-Fi, RFID, and/or wired connections”. Ibrahimov ¶0024 teaches the control unit (200) communicates with the robot using wireless or wired communication methods. This communication includes the instructions for movement.)
Claim 36
Goddard in view of Ibrahimov and Hines industrial control process of claim 35 wherein the voice commands dictate the communication protocol. (Applicant describes a communication protocol in ¶0132 of the specification as “Bluetooth, Wi-Fi, RFID, and/or wired connections”. Ibrahimov ¶0024 teaches the control unit (200) communicates with the robot using wireless or wired communication methods. The control unit generates the robot commands based on the voice input from the user (See ¶0029.)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Goddard (US20180284718A1) in view of Ibrahimov (EP4011567A1) and Minami (US20250196341A1), as applied in claim 1, further in view of Hines (US20240199392A1).
Claim 4
Goddard in view of Ibrahimov and Minami teaches the method of claim 1.
Goddard in view of Ibrahimov and Minami does not explicitly disclose wherein the one or more voice commands are customizable based on operational needs and/or preferences.
However, Hines teaches wherein the one or more voice commands are customizable based on operational needs and/or preferences. (Hines ¶0176 teaches “In most cases, the voice commands are limited to a few of the voice commands that correspond to established system commands that were used by the older handheld remote-control versions of the system. However, additional processing can be used to combine the commands to perform complex actions that are then transmitted to the vehicle 115”. This is a teaching of the system allowing for customization of additional, operation specific commands outside of the limited commands.)
One of ordinary skill would have been motivated to apply the known custom command technique from Hines to the system of Goddard in order to perform complex actions. (See Hines ¶0176)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known custom command technique from Hines to the system of Goddard because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Goddard (US20180284718A1) in view of Ibrahimov (EP4011567A1) and Minami (US20250196341A1), as applied in claim 1, further in view of Albrecht (US20220379477A1).
Claim 8
Goddard in view of Ibrahimov and Minami teaches the method of claim 1.
Goddard in view of Ibrahimov and Minami does not explicitly disclose further comprising offering ongoing technical support and/or update(s) to ensure the system remains effective and up-to-date.
However, Albrecht teaches offering ongoing technical support and/or update(s) to ensure the system remains effective and up-to-date. (Figure 1 teaches a robotic control system (112) that can have a voice recognition system (¶0023). The system receives software updates (¶0069).)
One of ordinary skill would have been motivated to apply the known software updates of Albrecht to the system of Goddard in view of Ibrahimov and Minami in order to keep the operational software up to date as new patches and fixes come out over time. (General knowledge in computer systems)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known software updates of Albrecht to the system of Goddard in view of Ibrahimov and Minami because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is the software system used in Goddard in view of Ibrahimov and Minami will receive periodic software updates.
Claims 23-27 and 30-32 are rejected under 35 U.S.C. 103 as being unpatentable over Goddard (US20180284718A1) in view of Minami (US20250196341A1) and Ibrahimov (EP4011567A1).
Claim 23
Goddard teaches a method of adjusting a position of a work piece (¶0004 teaches the invention is related to a workpiece positioning system.) for welding (Figure 1 teaches a positioning system (100) for holding a workpiece to be welded (¶0018).), comprising; mounting the work piece on a headstock carriage (110) and a tailstock carriage (120) of a hydraulic positioner (¶0019); to receive a voice command from an operator (¶0056 teaches the user interface includes a voice recognition system and microphone. The input device is used to input information to the sequencer, which sends commands to the positioning system (100) See ¶0028.) and send the command to a PLC (180, See ¶0021); the PLC moving at least one of the headstock and tailstock carriages such that the work piece is at a desired position for welding (¶0021 teaches the interface controller includes a PLC. ¶0028 teaches the positioning system (100), which includes the interface controller (180) receives commands to move the workpiece.); and providing audible feedback and alerts. (¶0029 teaches that the PLC provides feedback to the sequencer during the process. ¶0057 teaches the user output devices on the sequencer can be audio output devices.)
Goddard does not disclose using voice activated software operatively coupled with the positioner; the voice activated software using an AI-enabled voice control module with deep learning to perform voice-to-text and/or text-to-voice translations enabling text-to-operation functionality and/or status-to- text-to-voice functionality.
However, Minami teaches using voice activated software operatively coupled with the positioner (Figure 1 shows the system (100), including the processor (111), is connected to the robot (12).); the voice activated software using an AI-enabled voice control module with deep leaning to perform voice-to-text and/or text-to-voice translations enabling text-to-operation functionality and/or status-to- text-to-voice functionality. (Figure 1 teaches a control device (11) that includes a voice recognition unit (115) and communicates with a natural language processing (NLP) system (50). Both the voice recognition unit (115) and the NLP (50) use deep learning (¶0042 and ¶0033) and/or generative AI (¶0033) to recognize the text. The controller (11) then uses the response from the NLP system (50) to generate a command for the robot (15) (¶0038).)
One of ordinary skill would have been motivated to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov in order to provide a controller that enables a robot to work flexibly and properly as intended in response to user instructions in natural language.
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the system of Goddard will utilize AI technologies to perform the voice to machine command translations.
Goddard does not disclose the PLC selecting instructions from an action library.
However, Ibrahimov teaches a robotic controller (200) that selects a command from an action library. (¶0029 teaches that the robot control unit (200) is used to processing voice inputs and convert them to text. The text is then parsed (242) to identify potential commands stored in a library (223) and then send said command to the controller (210) to generate a robot command corresponding to the voice command. A robot command is in machine readable language.)
One of ordinary skill would have been motivated to apply the known library of commands system of Ibrahimov to the system of Goddard in order to process detected voice inputs to determine the which voice command is most suitable to process and send as instructions to the controller. (See ¶0029 of Ibrahimov)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known library of commands system of Ibrahimov to the system of Goddard because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the system of Goddard will choose from a library of known commands when searching for the best match to the vocal command, as disclosed by Ibrahimov.
Claim 24
Goddard in view of Minami and Ibrahimov teaches the method of claim 23 wherein the movement of at least one of the headstock and tailstock carriages includes height adjustment and/or rotational adjustment. (Goddard ¶0018 teaches the positioner has a linear axis (for raising and lowering) and a rotational axis.)
Claim 25
Goddard in view of Minami and Ibrahimov teaches the method of claim 23 wherein the work piece is raised, lowered and/or rotated by the voice activated software. (Goddard teaches in ¶0018 that the head/tail stock holders move the workpiece in a linear manner (raise or lower) or rotate the workpiece. ¶0055 teaches the welding job sequencer (200/800) has user interface input devices (822) and ¶0056 teaches that user input devices can be a voice input (microphone). ¶0038 of Minami teaches the processing unit generates commands for the robot based on the output from the NLP system (50).)
Claim 26
Goddard in view of Hines and Ibrahimov teaches the method of claim 23, wherein the feedback comprises one or more of:(a) a ready to move signal; (b) a move command signal; (c) a fault signal comprising identification of a fault and information regarding how to rectify the fault; and (d) a confirmation signal regarding command execution. (Goddard, ¶0029 teaches that the PLC provides feedback regarding a move being properly completed. This is an example of either a ready to move, move command, or confirmation signal.)
Claim 27
Goddard in view of Hines and Ibrahimov teaches the method of claim 23, wherein the alerts comprising one or more of:(a) a machine status alert; (b) a maintenance alert; and (c) a safety alert. (Goddard, ¶0029 teaches that the PLC provides feedback regarding a move being properly completed. This is an example of a machine status alert.)
Claim 30
Goddard in view of Minami and Ibrahimov teaches the method of claim 23 wherein the software has safety protocols to prevent accidental movement of the work piece. (Goddard ¶0030 teaches that the welding job sequencer (200) does not allow the operator to proceed unless the workpiece is in the correct position. This prevents the workpiece from accidently being moved to the incorrect position prior to welding.)
Claim 31
Goddard in view of Minami and Ibrahimov teaches the method of claim 23 wherein the voice activated software is configured to convert voice input from an operator to a machine- readable language and/or a communication protocol. (Minami Figure 1 teaches a control device (11) that includes a voice recognition unit (115) and communicates with a natural language processing (NLP) system (50). Both the voice recognition unit (115) and the NLP (50) use deep learning (¶0042 and ¶0033) and/or generative AI (¶0033) to recognize the text. The controller (11) then uses the response from the NLP system (50) to generate a command for the robot (15) (¶0038). The command for the robot is in machine readable language.)
Claim 32
Goddard teaches a method of welding a work piece (Figure 1 teaches a positioning system (100) for holding a workpiece to be welded (¶0018).), comprising: a) mounting the work piece in an adjustable positioner(¶0018 teaches the positioner(s) have a linear and rotational axis and that the workpiece is held by the positioning system.); b) issuing a voice command by an operator (¶0056 teaches the user interface includes a voice recognition system and microphone. The input device is used to input information to the sequencer, which sends commands to the positioning system (100) See ¶0028.); e) selecting movement for the positioner by the PLC (¶0021 teaches the interface controller includes a PLC. ¶0028 teaches the positioning system (100), which includes the interface controller (180) receives commands to move the workpiece.); f) executing a selected movement of the positioner in real time to adjust the orientation of the work piece (¶0021 teaches the interface controller includes a PLC. ¶0028 teaches the positioning system (100), which includes the interface controller (180) receives commands to move the workpiece.); g) maintaining the work piece in the adjusted orientation; and then h) welding the work piece; and i) repeating steps b)-h) as needed to complete the welding on the work piece; (Figure 7 shows a flowchart of the steps of the method where positioning, holding, welding, and repositioning happen repeatedly during the process until all of the welding steps are completed.)and providing real time audible feedback and alerts to the operator. (¶0029 teaches that the PLC provides feedback to the sequencer during the process. ¶0057 teaches the user output devices on the sequencer can be audio output devices.)
Goddard does not explicitly disclose c) converting the voice command utilizing natural language processing of AI enabled software to a machine-readable language and/or a communication protocol; d) transmitting the machine-readable language or communication protocol to a PLC.
However, Minami teaches c) converting the voice command utilizing natural language processing of AI enabled software (Minami Figure 1 teaches a control device (11) that includes a voice recognition unit (115) and communicates with a natural language processing (NLP) system (50). Both the voice recognition unit (115) and the NLP (50) use deep learning (¶0042 and ¶0033) and/or generative AI (¶0033) to recognize the text.) to a machine-readable language and/or a communication protocol (¶0038 “and generate an action command for the robot 15 based on the response.”); d) transmitting the machine-readable language or communication protocol to a PLC. (¶0046 teaches the command generated by the controller (111) is sent to a robot control unit (121).)
One of ordinary skill would have been motivated to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov in order to provide a controller that enables a robot to work flexibly and properly as intended in response to user instructions in natural language.
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known AI enabled voice control of Minami to the system of Goddard in view of Ibrahimov because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the system of Goddard will utilize AI technologies to perform the voice to machine command translations and send the commands to a controller of the robot.
Goddard does not disclose the PLC selecting instructions from an action library.
Goddard does not explicitly disclose e) selecting movement for the positioner from a library of movement options of the PLC.
However Ibrahimov teaches e) selecting movement for the positioner from a library of movement options of the PLC. (¶0029 teaches that the robot control unit (200) is used to processing voice inputs and convert them to text. The text is then parsed (242) to identify potential commands stored in a library (223) and then send said command to the controller (210) to generate a robot command corresponding to the voice command. A robot command is in machine readable language.)
One of ordinary skill would have been motivated to apply the known library of commands system of Ibrahimov to the system of Goddard in order to process detected voice inputs to determine the which voice command is most suitable to process and send as instructions to the controller. (See ¶0029 of Ibrahimov)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known library of commands system of Ibrahimov to the system of Goddard because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the system of Goddard will choose from a library of known commands when searching for the best match to the vocal command, as disclosed by Ibrahimov.
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Goddard (US20180284718A1) in view of Ibrahimov (EP4011567A1) and Minami (US20250196341A1), as applied in claim 1, further in view of Hines (US20240199392A1).
Claim 29
Goddard in view of Minami and Ibrahimov teaches the method of claim 23.
Goddard in view of Minami and Ibrahimov does not explicitly disclose the voice activated software is controlled by an operator wearing a device operatively connected to the software so as to provide voice activated, hands free adjustment of the work piece.
However, Hines teaches the voice activated software is controlled by an operator wearing a device operatively connected to the software so as to provide voice activated, hands free adjustment of the work piece. (Hines teaches the use of a wearable microphone/headset (¶0157).)
One of ordinary skill would have been motivated to apply the known wearable microphone of Hines to the system of Goddard in order to use a voice controller capable of clipping to a shirt pocket or other area of clothing and allow the operator to issue voice commands to the voice controller system. (See Hines ¶0157)
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to apply the known wearable microphone of Hines to the system of Goddard because it has been held to be prima facie obvious to apply a known technique to a known method/apparatus to yield predictable results. See MPEP 2143(I)(D).
The predictable result is that the system will use a headset or other hands-free microphone device for voice input.
Response to Arguments
Applicant’s arguments, see remarks, filed 05/26/2026 (regarding the amendments to the claims) have been fully considered and are persuasive.
The previously presented rejection did not teach the newly added limitations regarding deep learning and natural language processing. However, a new rejection is presented above based on applicant’s amendments to the claims.
Applicant’s arguments, see remarks, filed 05/26/2026 (regarding the combination of Goddard and Ibrahimov) have been fully considered and are not persuasive.
It is respectfully asserted that both Goddard and Ibrahimov are related to voice control of a robot system and commands performed by the robot. Therefore, the references are analogous to one another and the problem being solved by the applicant.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found on the PTO-892 Notice of References Cited form.
Document
Date
Description of Relevant Subject Matter
US12508715B1
2024-03-13
“For example, the processors of the robot may receive the information, process the information, and generate output commands to control the actuators of the robots to perform a certain movement.”
“An automated controller may use artificial intelligence (AI), which may include methods involving reasoning, knowledge, planning, learning, natural language processing (communication), perception and/or the ability to move and manipulate objects.”
“AI may include algorithms for deep learning such as, for example, deep belief networks, deep Boltzmann machines, deep convolutional neural networks, deep recurrent neural networks and/or hierarchical temporal memory.“
“A robot may be used in industrial applications, including, for example: welding, assembly, car body assembly, painting, material transfer automation, and/or machining.”
US20250196341A1
2023-12-19
[0042] The voice recognition unit 115 performs voice recognition processing on the voice signals from the voice input unit 114 and outputs text information represented by the voice to the processing unit 111. For example, the voice recognition unit 115 can use a voice recognition technology that converts voice signals into text information with a deep learning technology.
[0043] The voice synthesis unit 117 synthesizes a voice based on the action command to the robot 15 generated by the processing unit 111 and outputs the synthesized voice to the voice output unit 116. The voice synthesis unit 117 can use a general voice synthesis technique.
[0033] The natural language processing system 50 uses a large language model. The large language model is a deep learning model of a language model that models natural language that is a language spoken by humans based on the probability of its occurrence. The large language model is generated by pre-training based on a huge amount of data. The natural language processing system that uses the large language model may be Generative Pre-trained Transformer (GPT)-3, GPT-3.5, or GPT-4. When the natural language processing system 50 receives a request, the natural language processing system 50 statistically estimate the generation probability of next word using a large-scale language model from the sentence included in the received request, and transmits the estimation result to the request source.
[0020] In view of the above issues, it is an objective of the present disclosure to provide a controller that enables a robot to work flexibly and properly as intended in response to user instructions in natural language.
US20200035244A1
2019-10-03
[0173] The AI processor 21 can learn a neural network using programs stored in the memory 25. In particular, the AI processor 21 can learn a neural network for recognizing data related to vehicles. Here, the neural network for recognizing data related to vehicles may be designed to simulate the brain structure of human on a computer and may include a plurality of network nodes having weights and simulating the neurons of human neural network. The plurality of network nodes can transmit and receive data in accordance with each connection relationship to simulate the synaptic activity of neurons in which neurons transmit and receive signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In the deep learning model, a plurality of network nodes is positioned in different layers and can transmit and receive data in accordance with a convolution connection relationship. The neural network, for example, includes various deep learning techniques such as deep neural networks (DNN), convolutional deep neural networks(CNN), recurrent neural networks (RNN), a restricted boltzmann machine (RBM), deep belief networks (DBN), and a deep Q-network, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.
[0194] According to an embodiment, the input module may include at least one microphone capable of receiving a user's utterance as a speech signal.
US20250355419A1
2024-07-16
[0043] FIG. 1A illustrates a block diagram of a robotic controller 100 for controlling a robot 140 according to a sequence of actions 103 predicted using multimodal inputs 101, according to some example embodiments. The robotic controller 100 utilizes a large language model 110 and may be embodied as and also referred to as an LLM based controller 100.
[0049] The action sequence decoder 120 is trained with machine learning to transform the sequence of robotic instructions 117 into a sequence of actions 103 using a library of robotic skills
[0047] Additionally or alternatively, some embodiments employ a query-transformer (Q-Former) 113 that translates the multimodal encodings from the encoder 111 into “text-like” representations that can be ingested by a backend LLM decoder 115 thereby conditioning the LLM decoder 115 to produce its output in the form of the robotic instructions 117.
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 Michael W Hotchkiss whose telephone number is (571)272-3854. The examiner can normally be reached Monday-Friday from 0800-1600.
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, Sunil K Singh can be reached at 571-272-3460. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL W HOTCHKISS/Primary Examiner, Art Unit 3726
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