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 Objections
Applicant is advised that should
claim 7 be found allowable, claim 6 will be objected to,
claim 10 be found allowable, claim 9 will be objected to,
claim 13 be found allowable, claim 12 will be objected to,
under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 1-3 & 14-26 are rejected under 35 U.S.C. 103 as being obvious over Chen et al., Pub. No.: US 20230082760 A1, in view of Sharifi et al., Pub. No.: US 20260119284 A1.
Regarding claims 1 & 25-26, Chen et al. discloses a method implemented by one or more processors), & A system comprising: at least one processor; and memory storing instructions that, when executed, cause the at least one processor to be operable to. & A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to execute the instructions ([0046] “Various means … can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s) …“non-transitory computer-readable storage media”) to: (the method & system comprising):
receiving an incoming electronic communication directed to an autonomous vehicle, the incoming electronic communication being initiated by a remote communication participant that is located remotely from the autonomous vehicle ([0045] “autonomous computing system that can request remote assistance from a remote autonomous vehicle assistance system. In some examples, the autonomous computing system can be configured to transmit resource parameters or data from which resource parameters can be derived when sending a request for autonomous vehicle assistance to an autonomous vehicle assistance system.”); and
in response to receiving the incoming electronic communication directed to the autonomous vehicle ([0050] “to determine a first operator of the plurality of operators to facilitate one or more assisted autonomy tasks in response to the request for remote autonomous vehicle assistance based at least in part on the respective plurality of operator attributes for the first operator and the one or more request parameters associated with the request for remote autonomous vehicle assistance.” & [0051] The means can be configured to initiate remote autonomous vehicle assistance for the first autonomous vehicle facilitated by the first operator in response to the request for remote autonomous vehicle assistance.”):
conducting a conversation with the remote communication participant ([0055] The vehicle computing system 100 can communicate with the one or more remote computing devices via one or more communications networks including a communications network. The communications network can exchange (send or receive) signals (e.g., electronic signals) or data (e.g., data from a computing device) and include any combination of various wired (e.g., twisted pair cable) and/or wireless communication mechanisms (e.g., cellular, wireless, satellite, microwave, and radio frequency) and/or any desired network topology (or topologies).),
Chen et al. is not explicit on “generative model”, however, Sharifi et al., US 20260119284 A1, teaches EFFICIENTLY SHARING CALLS TO GENERATIVE MODEL(S) BETWEEN MULTIPLE AGENTS and discloses, wherein conducting the conversation with the remote communication participant comprises:
processing, using a generative model, instances of generative model input to generate instances of generative model output ([0039] “a generative model (GM) inference engine 150, and a GM signature engine 160.” & [0042] “the generative content system 140 can access a GM which can process GM input including the joint query to generate corresponding GM output. The generative content system 140 can use the GM inference engine 150 to perform this processing. Based on this GM output, responsive content which is responsive to the joint query can be determined.” & [0051] The GM processing engine 152 can process, using one or more cloud-based GM(s) from the GM(s) database 140A the GM input(s) 203 to generate the GM output(s) 204.”);
determining, based on the instances of generative model output, instances of content to be rendered as part of the conversation with the remote communication participant ([0028] “the client device 130A can utilize one or more machine learning (ML) model(s) to process the user input. For example, the user input received at the client device 130A can be a spoken utterance. In these examples, the user input engine 131A can process, using automatic speech recognition (ASR) model(s) (e.g., a recurrent neural network (RNN) model, a transformer model, and/or any other type of ML model capable of performing ASR), audio data that capture the spoken utterance and that is generated by microphone(s) of the client device 130A to generate ASR output. … determine recognized text that corresponds to the spoken utterance” & [0032] “the context engine 133A can determine a current context based on a current state of a dialog session (e.g., considering one or more recent inputs provided by a user during the dialog session), profile data, and/or a current location of the client device 130A. … content currently or recently rendered by the active software application. A context determined by the context engine 133A can be utilized, … in determining to submit an implied NL input and/or to render result(s) (e.g., responsive content) for an implied NL input.” & [0033] “the client device 130A can include an implied input engine 134A that is configured to: generate an implied NL input …cause rendering of a response for the NL input, optionally independent of any explicit NL input that requests rendering of the response. For example, the implied input engine 134A can use one or more past or current contexts, from the context engine 133A, in generating an implied NL input, determining to submit the implied NL input, and/or in determining to cause rendering of a response that is responsive to the implied NL input. … cause them to be automatically rendered or can automatically push a notification of the response, such as a selectable notification that, when selected, causes rendering of the response.”); and
causing the instances of content to be rendered at a computing device associated with the remote communication participant ([0033] In various implementations, the client device 130A can include an implied input engine 134A that is configured to: generate an implied NL input …independent of any user explicit NL input provided by a user of the client device 130A; submit an implied NL input, optionally independent of any user explicit NL input that requests submission of the NL input; and/or cause rendering of a response for the NL input, … in determining to cause rendering of a response … the implied input engine 134A can automatically push the response that is generated responsive to the implied query or implied prompt to cause them to be automatically rendered or can automatically push a notification of the response, such as a selectable notification that, when selected, causes rendering of the response. Additionally, or alternatively, the implied input engine 134A can submit respective implied NL input at regular or non-regular intervals, and cause respective responses to be automatically provided (or a notification thereof to be automatically provided).).
Before the Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to use these above mentioned features disclosed by Sharifi et al with the system disclosed by Chen et al 3 to provide a respective agent query, wherein each respective agent query includes a respective natural language request for performance of a respective generative task; aggregating each respective agent query to form a joint query; causing the joint query to be processed using a generative model to generate responsive content (see Abstract & para. [0003]-[0004]).
Regarding claims 2-…, Chen et al. discloses the method of claim 1.
Chen et al. is not explicit on “prompt for authenticating”, however, Sharifi et al., US 20260119284 A1, teaches EFFICIENTLY SHARING CALLS TO GENERATIVE MODEL(S) BETWEEN MULTIPLE AGENTS and discloses,
wherein an initial instance of the generative model input, of the instances of generative model input, includes at least a prompt for authenticating the remote communication participant, and wherein an initial instance of the content, of the instances of content, that is determined based on an initial instance of the generative model output, of the instances of generative model output, includes a request to authenticate the remote communication participant ([0027] Some instances of input (e.g., agent queries described herein) can be a query for a response that is formulated based on user input provided by a user of the client device 130A and detected via user input engine 131A. … Other instances of NL input described herein can be a prompt for content that is formulated based on user input provided by a user of the client device 130A and detected via the user input engine 131A. For example, the prompt can be a typed prompt that is typed via a physical or virtual keyboard, a suggested prompt that is selected via a touch screen or a mouse of the client device 130A, a spoken prompt that is detected via microphone(s) of the client device 130A, or an image or video prompt that is based on an image or video captured by a vision component of the client device 130A.).
Regarding claim 3, Chen et al. discloses the method of claim 2, wherein the request to authenticate the remote communication participant requests that the remote communication participant complete one or more of: short message service token authentication, email token authentication, hardware token authentication, software token authentication, biometric authentication, password authentication, or personal identification number authentication ([0026] “the client device 130A can include a user input engine 131A that is configured to detect user input provided by a user of the client device 130A using one or more user interface input devices” & [0051] The GM processing engine 152 can process, using one or more cloud-based GM(s) from the GM(s) database 140A the GM input(s) 203 to generate the GM output(s) 204. In these implementations, the GM output(s) 204 can include a probability distribution over a sequence of tokens, such as words, phrases, or other semantic units that are predicted to be necessary for determining responsive content 205 ... This enables the GM(s) to generate the GM output(s) 204 as the probability distribution over the sequence of tokens. The GM(s) can be initially trained and/or fine-tuned to enable the GM(s) to generate the GM output including the probability distribution over the sequence of tokens.” & [0073] “second feedback (or e.g., a confirmation message based on the second feedback)” & [0077] “microphone(s) to generate audio data based on spoken utterances and/or other audible input, … (e.g., hardware and/or software interface elements)” & [0078] “a generative assistant system provides the user with a notification or message 510” & [0087] “The generative assistant system may provide the user with a notification or message 530 … e.g., using a client GM corresponding to the query issuing agent” & [0098] In situations in which the systems described herein collect or otherwise monitor personal information about users”).
Before the Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to use these above mentioned features disclosed by Sharifi et al with the system disclosed by Chen et al 3 to provide a respective agent query, wherein each respective agent query includes a respective natural language request for performance of a respective generative task; aggregating each respective agent query to form a joint query; causing the joint query to be processed using a generative model to generate responsive content (see Abstract & para. [0003]-[0004]).
Claim 4 is rejected under 35 U.S.C. 103 as being obvious over Chen et al., Pub. No.: US 20230082760 A1, in view of Sharifi et al., Pub. No.: US 20260119284 A1, further in view of KOBAYASHI et al., Pub. No.: US 20190317491 A1.
Regarding claim 4, Chen et al. discloses the method of claim 2.
wherein subsequent instances of the generative model input, of the instances of the generative model input, are dynamically determined based on at least a type of the remote communication participant ([0031] “the autonomous vehicle assistance system and its associated operators may provide remote assistance over a wide range of request types. …The autonomous vehicle assistance system can communicate with the autonomous vehicle (e.g., in at least near real-time) to provide classification assistance” & [0088] “the remote assistance interface can provide imagery such as one or more images and/or video from an image sensor on board the autonomous vehicle (e.g., in at least near real-time while providing remote assistance)”).
Chen et al. is not explicit on “a teleassist operator, a shipper or a carrier associated with a trailer of the autonomous vehicle”, however, KOBAYASHI et al., US 20190317491 A1, teaches REMOTE-OPERATION APPARATUS AND REMOTE-OPERATION METHOD and discloses, and
wherein the type of the remote communication participant is one of: a dispatcher associated with the autonomous vehicle, a teleassist operator associated with the autonomous vehicle, a shipper associated with a payload the autonomous vehicle, or a carrier associated with a trailer of the autonomous vehicle ([0018] “a remote operator intuitively understand all these characteristics and remotely operate vehicles, driving sensation may become different in actuality.” & [0032] “control circuit 11 changes an autonomous driving mode to a remote-operation mode. Note that a remote operator may manually change the mode.” & [0033] In the remote-operation mode, control circuit 11 transmits data of a video captured by the visible light camera to remote-operation apparatus 30 via the network through streaming. ... Control circuit 11 controls actuator 14 in accordance with a control command received from remote-operation apparatus 30 via the network.” & [0035] “Autonomous vehicle information holding circuit 221 holds information of autonomous vehicle 1 which is monitored and controlled by remote monitoring center ... Remote operator information holding circuit 222 holds information of a remote operator belonging to remote monitoring center 3.”).
Before the Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to use these above mentioned features disclosed by KOBAYASHI et al. with the system disclosed by Chen et al. to provide a remote-operation apparatus and a remote-operation method which are used to remotely control an autonomous vehicle.. The communication circuit transmits a control command including an amount of operation obtained by correcting, based on characteristics of the autonomous vehicle, an amount of operation applied to an operation accepter by a remote operator. (see Abstract & para. [0002] & [0005]).
Regarding claim 14, Chen et al. discloses the method of claim 4, wherein the contextual autonomous vehicle data comprises one or more of: conversation logs associated with the autonomous vehicle, historical sensor data associated with the autonomous vehicle, maintenance logs associated with the autonomous vehicle, event logs associated with the autonomous vehicle, dispatch records associated with the autonomous vehicle; or operational logs associated with the autonomous vehicle ([0130] The memory 1120 can store data 1130 that can be obtained (e.g., received, accessed, written, manipulated, created, generated, etc.) and/or stored. The data 1130 can include, for instance, services data (e.g., trip data, route data, user data, etc.), sensor data, map data, perception data, prediction data, motion planning data, object states and/or state data, object motion trajectories, feedback data, fault data, log data, and/or other data/information as described herein. In some implementations, the computing device(s) 1105 can obtain data from one or more memories that are remote from the autonomous vehicle 105.).
Regarding claim 15, Chen et al. discloses the method of claim 4, wherein the subsequent instances of the generative model input, of the instances of the generative model input, are also dynamically determined based on instances of additional content provided by the remote communication participant ([0031] “the autonomous vehicle assistance system and its associated operators may provide remote assistance over a wide range of request types. …The autonomous vehicle assistance system can communicate with the autonomous vehicle (e.g., in at least near real-time) to provide classification assistance” & [0036] “the remote assistance interface can provide imagery such as one or more images and/or video from an image sensor on board the autonomous vehicle (e.g., in at least near real-time while providing remote assistance).” & [0088] “the remote assistance interface can provide imagery such as one or more images and/or video from an image sensor on board the autonomous vehicle (e.g., in at least near real-time while providing remote assistance)”).
Regarding claim 16, Chen et al. discloses the method of claim 1, wherein the one or more processors are local to the autonomous vehicle ([0057] An indication, record, and/or other data indicative of the state of the vehicle, the state of one or more passengers of the vehicle, and/or the state of an environment including one or more objects (e.g., the physical dimensions and/or appearance of the one or more objects) can be stored locally in one or more memory devices of the vehicle 105.” & [0058] “The vehicle computing system 100 can include one or more computing devices located onboard the vehicle 105. For example, the one or more computing devices of the vehicle computing system 100 can be located on and/or within the vehicle 105. … the one or more computing devices of the vehicle computing system 100 can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices).”).
Regarding claim 17, Chen et al. discloses the method of claim 1, wherein the one or more processors are located remotely from the autonomous vehicle, and wherein the one or more processors are located remotely from the computing device associated with the remote communication participant ([0054] “the vehicle computing system 100 can include and/or otherwise be associated with one or more computing devices that are remote from the vehicle 105. The one or more computing devices of the vehicle computing system 100 can include one or more processors and one or more memory devices.” & [0127] “one processor or a plurality of processors that are operatively connected.”).
Regarding claim 18, Chen et al. discloses the method of claim 1, wherein the one or more processors are located remotely from the method of claim 1, wherein causing the content to be rendered at the computing device associated with the remote communication participant comprises: causing one or more of the instances of the content to be audibly rendered via one or more speaker components of the computing device associated with the remote communication participant ([0041] “The user in some instances may initiate a communication to the autonomous vehicle assistance system using a user computing device such as a smartphone or other computing device. …the user may place a phone call that is routed to the autonomous vehicle assistance system, … Other techniques can be used to determine a vehicle service corresponding to a telephone call. For instance, the operations computing system may receive identifying information from a user via the phone (e.g., by audible commands or input actions).).
Regarding claim 19, Chen et al. discloses the method of claim 1, wherein causing the content to be rendered at the computing device associated with the remote communication participant comprises: causing one or more of the instances of the content to be visually rendered via a display of the computing device associated with the remote communication participant ([0036] “the remote assistance interface system can generate one or more user interfaces to display the sensor data to the selected operator via one or more operator computing devices. By way of example, the remote assistance interface can provide imagery such as one or more images and/or video from an image sensor on board the autonomous vehicle (e.g., in at least near real-time while providing remote assistance).”).
Regarding claim 20, Chen et al. discloses the method of claim 1, further comprising: determining a result of the conversation with the remote communication participant; determining, based on the result of the conversation with the remote communication participant, to modify control of the autonomous vehicle; and causing the control of the autonomous vehicle to be modified based on the result of the conversation with the remote communication participant ([0026] “an autonomous vehicle can include various systems and devices configured to control the operation of the vehicle. … generate an appropriate motion plan through the vehicle's surrounding environment.” & [0063] The autonomy computing system 130 … determine a motion plan for controlling the motion of the vehicle 105 … and generate an appropriate motion plan through the surrounding environment. The autonomy computing system 130 can control the one or more vehicle control systems 135 to operate the vehicle 105 according to the motion plan. & [0114] “the operator selection system can modify at (808) one or more attribute scores based on a time associated with the corresponding operator attribute.”).
Regarding claim 21, Chen et al. discloses the method of claim 1, wherein the incoming electronic communication is one of: an incoming telephone call, or an incoming text-based communication ([0041] “the user may place a phone call that is routed to the autonomous vehicle assistance system, may text a message to the autonomous vehicle assistance system, or may provide input via an application or other computer program provided by the service entity or vehicle vendor, etc. In some examples, a telephone call can be matched with a vehicle service, such as by matching a telephone number of the incoming call with a telephone number included in a service record.”).
Regarding claims 22-24, Chen et al. discloses the method of claim 1, further comprising:
(claim 22) as part of the conversation: determining whether the remote communication participant is authorized to engage in the conversation; and in response to determining that the remote communication participant is authorized to engage in the conversation: continuing the conversation with the remote communication participant [0021] “An autonomous vehicle may request remote autonomous vehicle assistance from a remote autonomous vehicle assistance system including one or more computing devices configured to assist the autonomous vehicle with one or more autonomous computing tasks. By way of example, an autonomy computing system may request remote autonomous assistance for autonomous computing tasks such as object classification, object prediction, mapping, motion planning, vehicle status checks, etc. Such tasks may typically be performed onboard the autonomous vehicle by the vehicle computing system. ... The remote vehicle assistance system may engage one or more operators (e.g., human or computer) to at least partially facilitate responses to requests for remote assistance. For example, sensor data from the autonomous vehicle may be transmitted to the autonomous vehicle assistance system and viewed by an operator. The operator can provide input to the autonomous vehicle assistance system which can generate control messages for the autonomous computing system to assist with the autonomous computing task.
(claim 23) in response to determining that the remote communication participant is not authorized to engage in the conversation: terminating the conversation with the remote communication participant; and causing an onboard configuration update to be performed at the autonomous vehicle ([0132] Computing tasks discussed herein as being performed at computing device(s) remote from the autonomous vehicle can instead be performed at the autonomous vehicle (e.g., via the vehicle computing system), or vice versa. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. … Computer-implements tasks and/or operations can be performed sequentially or in parallel.”),
(claim 24) transmitting, to one or more additional autonomous vehicles in a fleet of autonomous vehicles with the autonomous vehicle, a message that, when received by the one or more additional autonomous vehicles, cause the one or more additional autonomous vehicles to perform a corresponding onboard configuration update ([0022] “one or more operators can be selected in response to a request for remote autonomous vehicle assistance based on operator attributes associated with the operators, as well as request parameters associated with a context of the request for remote assistance. ... In an environment where different types of autonomous vehicles are utilized in diverse geographic areas, it can be important that requests for remote assistance be handled by an appropriate operator.” & [0026] “an autonomous vehicle can include an onboard vehicle computing system (e.g., located on or within the autonomous vehicle) that is configured to operate the autonomous vehicle. … an autonomous vehicle can include a communications system that can allow the vehicle to communicate with a computing system that is remote from the vehicle such as, for example, that of a service entity. The autonomous vehicle can be various types of vehicles.” & [0053] The vehicle computing system 100 can be associated with a service provider that can provide one or more vehicle services to a plurality of users via a fleet of vehicles” & [0054] “the vehicle computing system 100 can include and/or otherwise be associated with one or more computing devices that are remote from the vehicle 105. The one or more computing devices of the vehicle computing system 100 can include one or more processors and one or more memory devices.” & [0081] The service infrastructure 200 can include a plurality of software development kits (SDKs)… that provide access to the public platform 202 for use by both the service provider autonomous vehicles (208a, 208b) and the third-party entity autonomous vehicles (214a, 214b, 216a, 216b). … all external communication with the platforms can be done via the SDKs. ... the SDKs can provide a single entry point into the service provider infrastructure …a public SDK can provide secured access to the public platform 202 by both service provider vehicles and third-party entity (and/or systems) and access to capabilities” & [0082] “the SDKs can include a command-line interface to provide an entry point into the SDK components and act as a gateway for SDK related work, integration, testing, and authentication. … the command-line tools can provide for bootstrapping, managing authentication, updating SDK version, testing, debugging, and/or the like. In some implementations, a command-line interface can require an authentication certificate before being able to bootstrap an SDK, download components, and/or access a service provider's services. For example, based on the authentication certificate, a command-line interface can determine which version of the SDK (e.g., public or private) to which to provide access.).
Allowable Subject Matter
Claim 5 (with dependent claims 6-13) is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 25 & 26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 25 & 27 of Urmson et al., U.S. Patent No. US12/483,522 (the reference patent). Although the claims at issue are not identical, they are not patentably distinct from each other because;
- modifying the instant claim element, such as; “an incoming electronic communication” from “a conversation”,
- modifying the instant claim element, such as; “instances of generative model” from “generative model”
- modifying the instant claim element, such as; “instances of content” from “the content”,
- modifying the instant claim element, such as; “receive an incoming electronic communication directed to an autonomous vehicle,” from “monitor sensor data associated with an autonomous vehicle or an environment of the autonomous vehicle”,
- removing some claim elements,
- perfecting the claim language;
makes the claim language compact and broader but does not make patentable distinct from the reference patent claim (See below independent claims comparison table).
Claim comparison between instant application versus parent patent claims:
Instant App. # 19/003,447
Reference App. # 19/005,334
(Reference Patent No.: US 12483522)
Independent Claim 1
Independent Claim 1
1. A method implemented by one or more processors, the method comprising:
1. A method implemented by one or more processors, the method comprising:
monitoring sensor data associated with an autonomous vehicle or an environment of the autonomous vehicle, the sensor data being generated by one or more sensors of the autonomous vehicle;
receiving an incoming electronic communication directed to an autonomous vehicle, the incoming electronic communication being initiated by a remote communication participant that is located remotely from the autonomous vehicle; and in response to receiving the incoming electronic communication directed to the autonomous vehicle:
determining, based on an instance of the sensor data, whether to initiate a conversation with a remote communication participant that is located remotely from the autonomous vehicle;
determining a type of the instance of the sensor data, the type of the instance of the sensor data being one of multiple disparate types of instances of the sensor data;
identifying, based on the type of the instance of the sensor data, the remote communication participant that is located remotely from the autonomous vehicle; and in response to determining to initiate the conversation with the remote communication participant that is located remotely from the autonomous vehicle and based on the instance of the sensor data:
initiating the conversation with the remote communication participant; and
conducting a conversation with the remote communication participant, wherein conducting the conversation with the remote communication participant comprises;
conducting the conversation with the remote communication participant, wherein conducting the conversation with the remote communication participant comprises:
processing, using a generative model,
processing, using a generative model,
instances of generative model input to generate instances of generative model output;
generative model input to generate generative model output,
the generative model input including at least an indication of the instance of the sensor data;
determining, based on the instances of generative model output, instances of content to be rendered as part of the conversation with the remote communication participant; and
determining, based on the generative model output, content to be rendered as part of the conversation with the remote communication participant; and
causing the instances of content to be rendered at a computing device associated with the remote communication participant.
causing the content to be rendered at a computing device associated with the remote communication participant.
Independent Claim 25
Independent Claim 25
25. A system comprising: at least one processor; and memory storing instructions that, when executed, cause the at least one processor to be operable to:
26. A system comprising: at least one processor; and memory storing instructions that, when executed, cause the at least one processor to be operable to:
receive an incoming electronic communication directed to an autonomous vehicle,
monitor sensor data associated with an autonomous vehicle or an environment of the autonomous vehicle, the sensor data being generated by one or more sensors of the autonomous vehicle;
the incoming electronic communication being initiated by a remote communication participant that is located remotely from the autonomous vehicle; and
determine, based on an instance of the sensor data, whether to initiate a conversation with a remote communication participant that is located remotely from the autonomous vehicle;
in response to receiving the incoming electronic communication directed to the autonomous vehicle:
in response to determining to initiate the conversation with the remote communication participant that is located remotely from the autonomous vehicle and based on the instance of the sensor data:
initiate the conversation with the remote communication participant; and
conduct a conversation with the remote communication participant, wherein the instructions to conduct the conversation with the remote communication participant comprise instructions to:
conduct the conversation with the remote communication participant, wherein the instructions to conduct the conversation with the remote communication participant comprise instructions to:
process, using a generative model, instances of generative model input to generate instances of generative model output;
process, using a generative model, generative model input to generate generative model output,
the generative model input including at least an indication of the instance of the sensor data;
determine, based on the instances of generative model output, instances of content to be rendered as part of the conversation with the remote communication participant; and
determine, based on the generative model output, content to be rendered as part of the conversation with the remote communication participant; and
cause the instances of content to be rendered at a computing device associated with the remote communication participant.
cause the content to be rendered at a computing device associated with the remote communication participant;
‘
determine a result of the conversation with the remote communication participant determine, based on the result of the conversation with the remote communication participant, whether to initiate an additional conversation with an additional remote communication participant that is located remotely from the autonomous vehicle; and in response to determining to initiate an additional conversation with an additional remote communication participant that is located remotely from the autonomous vehicle: initiate the additional conversation with the additional remote communication participant; and conduct the additional conversation with the additional remote communication participant, wherein the instructions to conduct the additional conversation with the additional remote communication participant comprise instructions: process, using the generative model, additional generative model input to generate additional generative model output, the additional generative model input including at least an indication of the result of the conversation with the remote communication participant determine, based on the additional generative model output, additional content to be rendered as part of the additional conversation with the additional remote communication participant and cause the additional content to be rendered at an additional computing device associated with the additional remote communication participant.
Independent Claim 26
Independent Claim 27
26. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to execute the instructions to:
27. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to execute the instructions to:
receive an incoming electronic communication directed to an autonomous vehicle,
monitor sensor data associated with an autonomous vehicle or an environment of the autonomous vehicle,
the sensor data being generated by one or more sensors of the autonomous vehicle;
the incoming electronic communication being initiated by a remote communication participant that is located remotely from the autonomous vehicle; and
determine, based on an instance of the sensor data, whether to initiate a conversation with a remote communication participant that is located remotely from the autonomous vehicle;,
determine a type of the instance of the sensor data, the type of the instance of the sensor data being one of multiple disparate types of instances of the sensor data;
identify, based on the type of the instance of the sensor data
the remote communication participant that is located remotely from the autonomous vehicle; and
in response to receiving the incoming electronic communication directed to the autonomous vehicle:
in response to determining to initiate the conversation with the remote communication participant that is located remotely from the autonomous vehicle and based on the instance of the sensor data:
initiate the conversation with the remote communication participant; and
conduct a conversation with the remote communication participant,
conduct the conversation with the remote communication participant,
wherein the instructions to conduct the conversation with the remote communication participant comprise instructions to:
wherein the instructions to conduct the conversation with the remote communication participant comprise instructions to:
process, using a generative model, instances of generative model input to generate instances of generative model output;
process, using a generative model, generative model input to generate generative model output,
the generative model input including at least an indication of the instance of the sensor data;
determine, based on the instances of generative model output, instances of content to be rendered as part of the conversation with the remote communication participant; and
determine, based on the generative model output, content to be rendered as part of the conversation with the remote communication participant; and
cause the instances of content to be rendered at a computing device associated with the remote communication participant.
cause the content to be rendered at a computing device associated with the remote communication participant.
Regarding dependent claims 2-24; these claims are substantial duplicates of Reference Patent claims 2-24 & 26.
The instant claims recitations are obvious variation of the Prior Patent claims recitation in which both claims are represented by common drawings and are comingled in scope as mapped out above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of References cited.
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/Jalal C CODUROGLU/Examiner, Art Unit 3665
/DONALD J WALLACE/Primary Examiner, Art Unit 3665
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