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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/13/2026 has been entered.
Response to Amendment
The amendment filed on 06/13/2026, has been received and made of record. In response to the Final Office Action, dated on 01/14/2026. Claims 1-6, 8-13 and 17-20 are pending in the current application. Claims 14-16 have been cancelled. Claims 1-2 have been amended.
Response to Arguments
Applicant’s arguments filed on 06/13/2026 have been fully considered.
In the Arguments/Remarks:
Re: Rejection of the Claims Under 35 U.S.C. 103
Applicant argues, beginning on page 6 of applicant’s remarks, applicant argues that Hausman does not teach or suggest “initiating, by the robot, an interim interaction with the human, wherein the interim interaction is based at least in part on the response from the LLM and wherein the interaction does not contribute to the completing of the task”. Examiner respectfully disagrees. Examiner Hausman recites in paragraph 63 “The prompt example(s) 203A can be prepended or otherwise incorporated into the LLM prompt 205A to encourage prediction, in LLM output, of content that is in the output style(s). The explanation 204A can be an explanation generated based on processing a prior LLM prompt, also based on the FF NL input, in a prior pass and utilizing the LLM 150. For example, the prior LLM prompt could be “explain how you would bring me a snack from the table”, and the explanation 204A can be generated based on the highest probability decoding from the prior LLM output. For instance, the explanation 204A can be “I would find the table, then find a snack on the table, then bring it to you”.”. Applicant argues “the task that the user asks the robot to perform is to explain the steps. The robot literally completes the task that is requested of it by providing the explanation for how it would get a snack rather than actually getting the snack.”. Examiner respectfully disagrees. The paragraph 63 cited by the examiner was seen as a type of interim interaction and dialogue between the user and the robot. Later, in paragraph 68 of Hausman, the framework for actual task completion is laid out “For example, any “place” robotic skills can always have a fixed measure, such as 1.0 or 0.9, or a “terminate” robotic skill (i.e., signifying task is complete) may always have a fixed measure, such as 0.1 or 0.2. In some implementations, the world-grounding engine 134 can additionally or alternatively, for some robotic skill(s), generate world grounding measures based on a corresponding one of the value function model(s) 152 that is not machine-learning based (e.g., is not a neural network and/or is not trained). For example, a value function model can define that any “place” robotic skill should have a fixed measure, such as 1.0 or 0.9, when environmental state data 209A and/or robot state data 210A indicate that an object is being grasped by the robot 110, and another fixed measure, such as 0.0 or 0.1, otherwise. As another example, a value function for a “navigate to [object/location]” robotic skill can define the world-grounding measure is a function of the distance between the robot and the object/location, as determined based on environmental state data 209A and robot state data 210A. or a “terminate” robotic skill (i.e., signifying task is complete) may always have a fixed measure, such as 0.1 or 0.2.”.”. Examiner further submits that it would be obvious to one of ordinary skill in the art to ask the robot “How would you bring me an apple?” and upon hearing that the robot is capable to carry out the task to then order the robot to retrieve the apple. Under the broadest reasonable interpretation (BRI) of the claim this would be seen as an interim interaction which would not affect the retrieval of the apple. Examiner submits under the broadest reasonable interpretation (BRI) of the argued claim limitation that the combination of Cupersmith and Hausman teaches or suggests the argued limitation by the sections provided here and shown below in the current rejection, therefore applicant’s arguments are unpersuasive. The examiner maintains the current rejection. The same reasoning as applied to the independent claim above also apply to their corresponding dependent claims.
Examiner has augmented the claims in view of the applicant’s amendments and arguments (see rejection below).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cupersmith (US 2021/0304559 A1) in view of Hausman (US 2023/0311335 A1).
Regarding claim 1, Cupersmith teaches a robotic system comprising: a robot; a system controller; and an interface communicatively coupled to the system controller, wherein the system controller comprises at least one processor and at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor, the at least one non-transitory processor-readable storage medium storing processor-executable instructions and/or data that, when executed by the at least one processor, cause the robotic system to perform a method of operation of the robotic system, the method which includes: operating, by the robotic system, the robot in an environment, the environment comprising a human [(see at least paragraphs 38, 290,307) As in 38 “robotic service systems and methods, including various robotic devices (“robots”) configured for use in various service capacities. In example embodiments, a robot management system server is configured to manage a fleet of service robots that are deployed within a casino property, also referred to herein as an “operations venue” or just “venue” (e.g., gaming floor, hotel, lobbies, or such). The robot management system includes a robot management system server that wirelessly connects with each of the service robots and provides centralized task scheduling and assignment.” As in 290 “one or more processors 320 of robot 900 may control a propulsion system (e.g., drivetrain assembly 308) of robot 900 to navigate to a pickup location 1208 of a delivery item and receive the delivery item (step 1303), e.g., when the delivery item is not already contained in internal container storage system 906, and then navigate to delivery location 1204 (step 1304). When robot 900 arrives at delivery location 1204, robot 900 may authenticate an identity of recipient 1206, such as by acquiring a biometric and/or photograph, as described herein (step 1306).”]; initiating, by the robot performance of a task that includes at least one action by the robot; after the initiating performance of the task, detecting, by the robot, the human is waiting for the robot to complete the task [(see at least paragraph 209) “the patron may be able to view a current status of a robot 300 currently assigned to a task for that patron. For example, the patron may have submitted an order for a drink and the robot management system 400 may provide status information on the order (e.g., via the player app). The status information may include current task status (e.g., awaiting drink to be made, carrying drink to patron), or current location information of any robot 300 currently assigned to that task, thereby allowing the patron to see where their robot 300 is currently located, watch the robot movement (e.g., on a floor map), or the like. In some embodiments, the patron or an administrator may be able to view current camera data from the robot 300 or take control of the robot 300.”]; in response to detecting the human is waiting for the robot to complete the task, sending, by the interface, a query, wherein the query comprises a natural language statement that includes at least one of: a description of the robot; a description of the environment; a description of the human; and/or a description of the task [(see at least paragraphs 137-138) As in 137 “For example, the user may request the operating hours of a local dining establishment and the robot 300 may reply with an opening and closing time of the restaurant. Some user requests may activate functions on the robot 300. For example, the user may verbally request printing of a new player loyalty card and the conversation interface 544 may, in response, activate a printing function of a player loyalty administration package (not shown) and associated GUI on a display device 350 of the robot 300. In some embodiments, the conversation interface 544 supports multilingual voice and text inputs and outputs, or visual recognition of sign language or other gestures. As such, the conversation interface 544 allows the robot 300 to respond in various ways to various audio commands or requests provided by the user.”], and wherein the natural language statement further includes a request to provide something for the robot to say to the human while the human is waiting for the robot to complete the task [(see at least paragraph 137) “The conversation interface package 544, in the example embodiment, is configured to perform audio or text-based one-or two-way interactions with the user (e.g., as a voice user interface). For example, the conversation interface package 544 may be instructed by a task 562 to receive a verbal user request for information (e.g., audio input via the microphone 356) and respond to that request with an audible response (e.g., audio output via the speakers 354). The conversation interface package 544 may, for example, use speech recognition techniques or packages to parse or convert the user request audio into text, submit the text request to a backend search engine (e.g., locally or to the robot management system server 106), receive response text, and output the response text as audio output through the speakers 354. For example, the user may request the operating hours of a local dining establishment and the robot 300 may reply with an opening and closing time of the restaurant. Some user requests may activate functions on the robot 300. For example, the user may verbally request printing of a new player loyalty card and the conversation interface 544 may, in response, activate a printing function of a player loyalty administration package (not shown) and associated GUI on a display device 350 of the robot 300. In some embodiments, the conversation interface 544 supports multilingual voice and text inputs and outputs, or visual recognition of sign language or other gestures. As such, the conversation interface 544 allows the robot 300 to respond in various ways to various audio commands or requests provided by the user.”]
Cupersmith does not explicitly teach a large language model (LLM); receiving, by the interface, a response from the LLM, the response in reply to the query; and initiating, by the robot, an interim interaction with the human, wherein the interim interaction is based at least in part on the response from the LLM.
However, Hausman teaches a large language model (LLM) [(see at least paragraph 3) “For example, large language models (LLMs) have been developed that are trained on massive amounts of data and are able to be utilized to robustly process a wide range of NL inputs and generate corresponding LM output that reflects corresponding NL content that is accurate and responsive to the NL input. An LLM can include at least hundreds of millions of parameters and can often include at least billions of parameters, such as one hundred billion or more parameters. An LLM can, for example, be a sequence-to-sequence model, Transformer-based, and/or include an encoder and/or a decoder. One non-limiting example of an LLM is GOOGLE'S Pathways Language Model (PaLM). Another non-limiting example of an LLM is GOOGLE'S Language Model for Dialogue Applications (LaMDA).”]
Hausman teaches receiving, by the interface, a response from the LLM, the response in reply to the query [(see at least paragraph 8) “The generated LLM prompt can be processed, using the LLM, to generate LLM output that models a probability distribution, over candidate word compositions, that is dependent on the instruction. Continuing with the working example, the highest probability decoding of the LLM output can be, for example, “use a vacuum”. However, implementations disclosed herein do not simply blindly utilize the probability distribution of the LLM output in determining how to control a robot. Rather, implementations leverage the probability distribution of the LLM output, while also considering robotic skills that are actually performable by the robot, such as tens of, hundreds of, or thousands of pre-trained robotic skills”]; and initiating, by the robot, an interim interaction with the human, wherein the interim interaction is based at least in part on the response from the LLM and wherein the interim interaction does not contribute to the completing of the task by the robot. [(see at least paragraphs 60-70) As in 63 “The explanation 204A can be an explanation generated based on processing a prior LLM prompt, also based on the FF NL input, in a prior pass and utilizing the LLM 150. For example, the prior LLM prompt could be “explain how you would bring me a snack from the table”, and the explanation 204A can be generated based on the highest probability decoding from the prior LLM output. For instance, the explanation 204A can be “I would find the table, then find a snack on the table, then bring it to you”. The explanation 204A can be prepended to the LLM prompt 205A, replace term(s) of the FF NL input 105 in the LLM prompt 205A, or otherwise incorporated into the LLM prompt 205A.”] Examiner notes that this is being interpreted as an interim interaction which does not contribute to the completing of the task. The robot is responding to the dialogue of the user of how it would grab a snack for the user by answering it with the steps the robot would take to complete the task. The process of responding to the user of how it would complete the task does not affect the completion of the task.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Cupersmith to incorporate the teachings of Hausman of a large language model (LLM) and receiving, by the interface, a response from the LLM, the response in reply to the query and initiating, by the robot, an interim interaction with the human, wherein the interim interaction is based at least in part on the response from the LLM in order to perform certain tasks in response to various free-form natural language inputs of a user attempting to control the robot. [(Hausman 2)]
Regarding claim 2, In view of the above combination of references, Cupersmith further teaches wherein the initiating, by the robot, performance of a task includes initiating at least one of the action, a generation of an action plan, a motion, a generation of a motion plan, a simulation, a calculation, a loading of a capability, a reception and/or a response to instructions, and an identification of an object in the environment. [(see at least paragraph 120) “the robot 300 may include a robotic arm (not shown) that can be configured to interact with objects in the nearby environment. The robotic arm can include several arm segments, joints, and motors that are configured to enable the robot arm to articulate in various degrees of freedom. The robotic arm includes at least one end effector (or “manipulator”) configured to be used to perform various tasks, such as pressing buttons, grabbing and releasing objects, housing a sensor (e.g., RFID reader, camera), or the like. In some embodiments, the robot 300 may be configured to use the robot arm during maintenance operations, for example, to depress mechanical buttons 236 or touch touchscreen components of a gaming device 104, insert or collect tickets to test bill validators 234, ticket printers 222, or ticket readers 224. In some embodiments, the robot 300 may be configured to use the robot arm to load or unload delivery items from onboard storage (e.g., food and beverage, coat check). In some embodiments, the robot 300 may be configured to use the robotic arm to collect and deposit trash items into trash receptacles, empty ash trays, or collect dirty dishes”]
Regarding claim 3, In view of the above combination of references, Cupersmith further teaches wherein the detecting, by the robot, the human is waiting for the robot to complete the task includes detecting autonomously, by the robot, the human is waiting for the robot to complete the task. [(see at least paragraph 209) “For example, the patron may have submitted an order for a drink and the robot management system 400 may provide status information on the order (e.g., via the player app). The status information may include current task status (e.g., awaiting drink to be made, carrying drink to patron), or current location information of any robot 300 currently assigned to that task, thereby allowing the patron to see where their robot 300 is currently located, watch the robot movement (e.g., on a floor map), or the like”]
Regarding claim 4, In view of the above combination of references, Cupersmith further teaches wherein the detecting, by the robot, the human is waiting for the robot to complete the task includes detecting a timer has expired. [(see at least paragraph 280) “In some embodiments, RMS server 106 or robot 900 may automatically initiate a delivery request (e.g., periodically, according to a particular schedule, at the start of an operational shift, according to a predefined schedule (e.g., as related to a loyalty tier of a player), based upon one or more timers, and the like).”]
Regarding claim 5, In view of the above combination of references, Cupersmith further teaches wherein the detecting, by the robot, the human is waiting for the robot to complete the task includes detecting a duration of the task exceeds an expected duration. [(see at least paragraph 270) “In some embodiments, the operator assistance experience may be automatically initiated when a patron interaction session with a robot 300 has exceeded a predetermined amount of time. For example, some patrons may have difficulties interacting with the robot 300, and thus may become frustrated and take up an extensive amount of interaction time”]
Regarding claim 6, In view of the above combination of references, Cupersmith further teaches wherein the detecting a duration of the task exceeds an expected duration includes determining an expected duration based at least in part on historical data. [(see at least paragraph 270) “In another example, the RMS 400 may automatically trigger a cordiality visit of a robot 300 to a highly regarded patron upon detection of that patron at the venue 600, and that cordiality visit may include an operator assistance experience. In some embodiments, the operator assistance experience may be automatically initiated when a patron interaction session with a robot 300 has exceeded a predetermined amount of time. For example, some patrons may have difficulties interacting with the robot 300, and thus may become frustrated and take up an extensive amount of interaction time. As such, automatic initiation of the operator assist experience may allow the operator 420 a chance to guide the patron through their interaction with the robot 300 or otherwise be able to assist in the patron's underlying issues or requests.”]
Regarding claim 8, In view of the above combination of references, Cupersmith further teaches the robot comprising a visual sensor, wherein detecting the human is waiting for the robot to complete the task includes detecting the human is waiting for the robot to complete the task based at least in part on data from the visual sensor. [(see at least paragraphs 99, 209) As in 99 “The head unit 302 also includes a camera device 360 configured to capture digital images or video near the robot 300, which may be used for navigation functions, human detection, patron recognition, or other various use cases described herein. The body module 310, in this example, includes a bill/ticket/card reader 362 and a ticket/card printer 364, which may be similar to the bill validator 234, ticket reader 224, and ticket printer 222 shown in FIG. 2A.” As in 209 “The status information may include current task status (e.g., awaiting drink to be made, carrying drink to patron)”]
Regarding claim 9, In view of the above combination of references, Cupersmith further teaches wherein detecting the human is waiting for the robot to complete the task based at least in part on data from the visual sensor includes: scanning the environment, by the visual sensors, to generate sensor data; and analyzing, by the sensor data processor, the sensor data to detect the human is waiting for the robot to complete the task based at least in part on the sensor data. [(see at least paragraphs 99, 209) As in 99 “The head unit 302 also includes a camera device 360 configured to capture digital images or video near the robot 300, which may be used for navigation functions, human detection, patron recognition, or other various use cases described herein. The body module 310, in this example, includes a bill/ticket/card reader 362 and a ticket/card printer 364, which may be similar to the bill validator 234, ticket reader 224, and ticket printer 222 shown in FIG. 2A.” As in 209 “The status information may include current task status (e.g., awaiting drink to be made, carrying drink to patron)”]
Regarding claim 10, In view of the above combination of references, Cupersmith further teaches the robot comprising a sound sensor, wherein detecting the human is waiting for the robot to complete the task includes detecting the human is waiting for the robot to complete the task based at least in part on data from the sound sensor. [(see at least paragraph 239) “Upon activation of the robot 300 (e.g., by receiving a remote task, via physical interaction with the device, by keyword voice activation from a nearby player), the robot 300 may display the face waking up and looking alert, or otherwise changing away from the sleeping face. The robot 300 may display a moving face or animation while the robot 300 is moving to a destination (e.g., bobbing back and forth, sweating, or the like). The robot 300 may display facial expressions and lip movements while interacting with a player”]
Regarding claim 11, Modified Cupersmith has all of the elements of claim 1 as discussed above.
Cupersmith does not explicitly teach wherein the sending, by the interface, a query to the LLM includes sending a query to a chatbot.
However, Hausman teaches wherein the sending, by the interface, a query to the LLM includes sending a query to a chatbot. [(see at least paragraphs 27-31) As in 27 “Accordingly, implementations leverage an LLM to provide task-grounding to determine useful actions for a high-level goal and leverage affordance functions (e.g., learned function(s)) to provide worldgrounding to determine what action(s) are actually possible to execute in achieving the highlevel goal. Some of those implementations utilize reinforcement learning (RL) as a way to learn language-conditioned value functions that provide affordances of what is possible in the real world.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of modified Cupersmith to incorporate the teachings of Hausman of wherein the sending, by the interface, a query to the LLM includes sending a query to a chatbot in order to learn language-conditioned value functions that provide affordances of what is possible in the real world. [(Hausman 27)]
Regarding claim 12, Modified Cupersmith has all of the elements of claim 1 as discussed above.
Cupersmith does not explicitly teach wherein the sending, by the interface, a query to the LLM includes sending a query to an LLM in the robotic system.
However, Hausman teaches wherein the sending, by the interface, a query to the LLM includes sending a query to an LLM in the robotic system. [(see at least paragraphs 33-38) As in 38 “the corresponding policy is executed by the agent and the LLM query is amended to include .sub.π (the language description of the selected skill), and the process is run again until a termination token (e.g., “done”) is chosen. This process is described in Algorithm 1, provided below. These two mirrored processes together lead to a probabilistic interpretation, where the LLM provides probabilities of a skill being useful for the high-level instruction and the affordances provide probabilities of successfully executing each skill. Combining these two probabilities together provides a probability that this skill furthers the execution of the highlevel instruction commanded by the user.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of modified Cupersmith to incorporate the teachings of Hausman of wherein the sending, by the interface, a query to the LLM includes sending a query to an LLM in the robotic system in order to inform LMs that the high-level instruction should be broken down into sequences of available low-level skills. [(Hausman 33)]
Regarding claim 13, Modified Cupersmith has all of the elements of claim 12 as discussed above.
Cupersmith does not explicitly teach wherein the sending a query to an LLM in the robotic system includes sending a query to an LLM onboard the robot.
However, Hausman teaches wherein the sending a query to an LLM in the robotic system includes sending a query to an LLM onboard the robot. [(see at least Fig.3, paragraphs 83-90, 103) As in 85 “the system processes, using an LLM, an LLM prompt that is based on the FF NL instruction, to generate LLM output. Block 354 optionally includes sub-block 354A and/or sub-block 354B. At sub-block 354A, the system includes scene descriptor(s in the LLM prompt. At sub-block 354B, the system generates an explanation and includes the explanation in the LLM prompt.” As in 86 “the system generates, based on the LLM output of block 354 and a corresponding NL skill description for each of multiple candidate robotic skills, a corresponding task-grounding measure. Block 356 optionally includes sub-block 356A, in which the system generates the task-grounding measure, of a candidate robotic skill, based on a probability of the NL skill description as reflected in a probability distribution of the LLM output.” As in 103 “The robot control system 560 may be implemented in one or more processors, such as a CPU, GPU, and/or other controller(s) of the robot 520. In some implementations, the robot 520 may comprise a “brain box” that may include all or aspects of the control system 560. For example, the brain box may provide real time bursts of data to the operational components 540a-n, with each of the real time bursts comprising a set of one or more control commands that dictate, inter alia, the parameters of motion (if any) for each of one or more of the operational components 540a-n. In some implementations, the robot control system 560 may perform one or more aspects of method(s) described herein, such as method 300 of FIG. 3”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of modified Cupersmith to incorporate the teachings of Hausman of wherein the sending a query to an LLM in the robotic system includes sending a query to an LLM onboard the robot in order to respond to various free-form natural language inputs of a user attempting to control the robot. [(Hausman 2)]
Regarding claim 17, Modified Cupersmith has all of the elements of claim 1 as discussed above.
Cupersmith does not explicitly teach wherein the receiving, by the interface, a response from the LLM includes receiving a natural language statement from the LLM, and parsing the natural language statement.
However, Hausman teaches wherein the receiving, by the interface, a response from the LLM includes receiving a natural language statement from the LLM, and parsing the natural language statement. [(see at least paragraph 7) “For instance, the prompt can include some or all of the terms of the FF NL instruction, but can additionally include: scene descriptor(s) of the current environment (e.g., NL descriptor(s) of object(s) detected in the environment); an explanation generated in a prior pass utilizing the LLM (e.g., based on a prior LLM prompt of “explain how you would help when a user says ‘I spilled my drink on the table, can you help’”); and/or term(s) to encourage prediction of step(s) by the LLM (e.g., including, at the end of the prompt “I would 1.”).”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of modified Cupersmith to incorporate the teachings of Hausman of wherein the receiving, by the interface, a response from the LLM includes receiving a natural language statement from the LLM, and parsing the natural language statement in order to respond to various free-form natural language inputs of a user attempting to control the robot. [(Hausman 2)]
Regarding claim 18, In view of the above combination of references, Cupersmith further teaches wherein the initiating, by the robot, an interim interaction with the human includes initiating autonomously, by the robot, an interim interaction with the human. [(see at least paragraph 355) “robots 300 may display certain persona interactions based on a current celebration activity or task currently being performed by robot 300. For example, upon activation (e.g., by receiving a remote celebration task), robot 300 may display a moving face, facial expressions, or animations while robot 300 is moving to a destination (e.g., bobbing back and forth, searching eye movements, perspiring, or the like). Robot 300 may display facial expressions and lip movements while interacting with other robots and/or players at a celebration scene (e.g., emulating articulation of audible interactions such as lip-syncing to songs). Such persona animations allow nearby players to understand what robot 300 are currently doing, as well as comforting and easing human interaction with robot 300.”]
Regarding claim 19, In view of the above combination of references, Cupersmith further teaches wherein the initiating, by the robot, an interim interaction with the human includes initiating, by the robot, a diversion. [(see at least paragraph 239) “As such, the robots 300 may be configured to emulate aspects of human interactions and social conventions through various persona animations (e.g., via graphical displays and audio outputs). For example, the robots 300 may be configured to display facial features such as, for example, eyes, nose, mouth, lips, eye brows, and the like. The robots 300 may use and alter the display of these facial features during interactions with users, thereby making the interactions seem more like human to human interactions and easing discomfort of the user (e.g., smiling, mouth and lip movements while talking, or the like). In some embodiments, the robots 300 may display certain persona interactions based on a current activity or task currently being performed by the robot 300.”]
Regarding claim 20, In view of the above combination of references, Cupersmith further teaches wherein the initiating, by the robot, an interim interaction with the human includes at least one of telling a joke, sharing a fact, posing a brainteaser, and initiating a conversation. [(see at least paragraph 107) “In some embodiments, the robot 300 may be configured to provide entertainment functions through use of the displays 350 and speakers, including playing songs or videos, telling jokes, performing animated movements or dances (e.g., alone or with other robots 300), or any combination thereof.”]
The Examiner has cited particular paragraphs or columns and line numbers in the references applied to the claims above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the Applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2141.02 [R-07.2015] VI. A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed Invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED YOUSEF ABUELHAWA whose telephone number is (571)272-3219. The examiner can normally be reached Monday-Friday 8:30-5:00 with flex.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at 571-270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMED YOUSEF ABUELHAWA/Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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