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 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 1-20 are 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 1 recites the limitation “the parameter of a database” in the last line. However, only “a parameter of a computing system” has been defined. There is insufficient antecedent basis for this limitation in the claim. Claims 2-20 depend on claim 1 or recite commensurate subject matter; therefore, they are indefinite for the same reason.
The term “skilled developer” in claim 5 is a relative term which renders the claim indefinite. The term “skilled developer” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claim 13 corresponds to claim 5; therefore, it is indefinite for the same reason.
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.
Claim(s) 1-6, 8-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raju (US 2020/0349178) and further in view of Nguyen (US 2023/0409346).
Regarding claim 1, Raju teaches: A method comprising:
applying an input to a natural language model (¶ 73, “the captured voice instructions 716 can be distributed to the machine learning server 309 by the front-end server 210, as an input to a trained DNN model 712. The DNN model 712 can include one or more layer for voice recognition 711 and one or more layers for natural language processing (NLP) 713”) to generate a modification action (¶ 97, “a natural language process (NLP) is also performed on the text stream to recognize data to be updated and a target task to be updated”), wherein the modification action includes a command and a database update (¶ 97, “a speech-to-text (STT) process is performed on the voice instructions to convert the voice instructions to a text stream, and a natural language process (NLP) is also performed on the text stream to recognize data to be updated and a target task to be updated”), and wherein the database update is associated with a parameter of a computing system (¶ 97, “In operation 1005, the data to be updated and a target task to be updated to the mobile device is transmitted over the network for confirmation by a user associated with the mobile device”);
determining that user credential data satisfies an update condition with respect to the command (¶ 27, “updating one or more data entries of a database comprises accessing the database and authenticating the user based on a device identifier (ID) of the mobile device”);
in response to determining satisfaction of the update condition, transmitting the command to the computing system (¶ 27, “in response to successfully authenticating the user, identifying the one or more data entries based on a category associated with the content data categorized according to the predetermined category list; and modifying one or more fields of the identified data entries based on the content data”), and receiving, from the computing system, a value for the parameter (¶ 97, “In operation 1005, the data to be updated and a target task to be updated to the mobile device is transmitted over the network for confirmation by a user associated with the mobile device”); and
updating the parameter of a database based on the value (¶ 97, “In operation 1007, in response to a confirmation received from the mobile device, a database update command is transmitted to the task database system to update one or more fields of the target task based on the data to be updated”).
Raju does not teach as clearly as Nguyen discloses: in response to determining satisfaction of the update condition, transmitting the command to the computing system (¶ 37, “the GUI 14 allows the user 12 to approve one or more modification requests 20. Here, via a user interaction 24, the user 12 may interact with an approval user input 420 (e.g., a button). Interaction with the approval user input 420 generates the approval response 314a (FIG. 3), allowing the SCM manager 300 to merge the updated configuration files 152 into the repository via the SCMS 150”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of in response to determining satisfaction of the update condition, transmitting the command to the computing system, as taught by Nguyen, in the same way to transmitting the command to the computing system, as taught by Raju. Both inventions are in the field of transmitting commands to computing systems, and combining them would have predictably resulted in “generating an approval request requesting approval of the update to the configuration file from a second user of a second user device,” as indicated by Nguyen (¶ 9).
Regarding claim 2, Nguyen teaches: The method of claim 1, wherein the user credential data is second user credential data (¶ 36, “the GUI 14 solicits identifiers, user credentials (e.g., usernames, passwords, etc.), approvers, etc”), and wherein the method further comprises: obtaining, from a first user, first user credential data and an indication that the modification action is accepted (¶ 43, “The method 500, at operation 502, includes receiving, from a user 12 of a user device 10, a cloud infrastructure modification request 20 requesting modification to cloud infrastructure 50 executing on the data processing hardware 144”); obtaining, from a second user, the second user credential data and a further input to perform the modification action (¶ 38, “The modified modification request 20 may require approval from another user 12 (e.g., the original author of the modification request 20 or a third user 12)”); and determining that the first user credential data satisfies a further update condition with respect to the database update (¶ 32, “The approval manager 310 generates approval requests 312 requesting approval of updates or additions of configuration files 152 prior to committing the updates or additions to the SCMS 150”), wherein transmitting the command to the computing system is performed in response to (i) obtaining the indication that the modification action is accepted and determining that the first user credential data satisfies the further update condition with respect to the database update (¶ 33, “The approval manager 310 may generate the approval requests 312 for each approver of a modification request 20”) and (ii) determining that the second user credential data satisfies the update condition with respect to the command (¶ 33, “The approval manager 310 may determine the appropriate approvers for a modification request 20 based on a number of factors” and ¶ 40, “with proper permissions, the administrator may review, approve, and/or reject the modification requests 20”).
Regarding claim 3, Nguyen teaches: The method of claim 2, wherein the second user credential data does not satisfy the update condition with respect to the database update (¶ 40, “In addition, with proper permissions, the administrator may review, approve, and/or reject the modification requests 20”).
Regarding claim 4, Raju teaches: The method of claim 1, wherein applying the input to the natural language model to generate the modification action comprises: applying a first input to the natural language model to generate a list of names of potential modification actions that includes a name of the modification action (¶ 83, “The front-end server 210 can use the texts to perform a query on the data server 106 to generate one or more tasks assigned to the user for display in the voice UI 709”); and applying a second input, that includes the name of the modification action, to the natural language model to generate the modification action (¶ 76, “User A 208 can speak to the client device through the IVR mobile application 705 as to which task of the displayed tasks 715 to update, how to update the task”).
Regarding claim 5, Nguyen teaches: The method of claim 4, wherein the first input and the second input both include text instructing the natural language model to produce output as though generated by a skilled developer (¶ 20, “Infrastructure as code (IaC) enables developers or operations teams to automatically manage, monitor and provision resources, rather than manually configure cloud resources”).
Regarding claim 6, Raju teaches: The method of claim 1, wherein the computing system is a cloud computing environment, and wherein applying the input to the natural language model, determining that the user credential data satisfies the update condition with respect to the command, transmitting the command to the computing system, and receiving the value for the parameter are performed by a local computing system that is in communication with the cloud computing environment (¶ 39, “the cloud platform server 111 can be provided between client devices 101-102 and the task database system 105. Users at client devices 101-102 can log in to the cloud server 111, which can utilize services and data provided by the task database server 105”).
Regarding claim 8, Raju teaches: The method of claim 1, further comprising: providing a virtual agent dialog to a user and obtaining user text from the user via the virtual agent dialog (¶ 57, “As further shown in FIG. 3, the texts generated by the machine learning server 309 from the image 315 can be sent back as editable text 317 to the mobile application 305, which can provide a graphical user interface 310 for the user to edit the text 317”); and determining that the user text represents a user request to generate the modification action, wherein applying the input to the natural language model to generate the modification action is performed responsive to determining that the user text represents the user request to generate the modification action (¶ 58, “the user may correct a spelling error and one or more informalities in the editable texts 317 through the graphical user interface 310. Once the text editing is completed, the user is prompted to confirm the user-edited text in another graphical user interface (e.g., confirmation page 308) for confirmation”).
Claims 9-14 and 16-20 recite commensurate subject matter as claims 1-7 and 8. Therefore, they are rejected for the same reasons.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raju and Nguyen, as applied above, and further in view of Maes (US 11,120,217).
Regarding claim 7, Raju and Nguyen do not teach; however, Maes discloses: the command of the modification action is to create, delete, or modify a virtual machine in the cloud computing environment (col. 1:67 and col. 2:1-5, “the cloud orchestration workflow may refer to a sequence of operations to perform such tasks as provisioning cloud resources (cloud services, cloud components, virtual machines, applications, servers, cloud storage, and so forth); deploying cloud resources; managing the lifecycle of cloud resources; and so forth”), wherein the database includes records relating to operation of one or more virtual machines in the cloud computing environment (col. 9:30-34, “the machine translation 200 produces a single output cloud orchestration workflow 150 that includes the following ordered sequence of operations: a first operation 254 to acquire the instance operating system for the virtual machine on DataCenter1”), and wherein updating the parameter of a database based on the value updates one or more of the records to reflect creation, deletion, or modification of the virtual machine (col. 9:33-38, “a first operation 254 to acquire the instance operating system for the virtual machine on DataCenter1; a second operation 258 to acquire the configuration (CPU, memory, disks, and so forth) of the virtual machine on the DataCenter1; an operation 262 to shut down the virtual machine instance on DataCenter1”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of the command of the modification action is to create, delete, or modify a virtual machine in the cloud computing environment, wherein the database includes records relating to operation of one or more virtual machines in the cloud computing environment, and wherein updating the parameter of a database based on the value updates one or more of the records to reflect creation, deletion, or modification of the virtual machine, as taught by Maes, in the same way to the command, as taught by Raju and Nguyen. Both inventions are in the field of receiving natural language commands and generating computer system tasks, and combining them would have predictably resulted in “sell goods or services, maintain business records, and provide individuals with access to computing resources, among other cloud-related objectives,” as indicated by Maes (col. 1:10-12).
Claim(s) 15 recite(s) commensurate subject matter as claim(s) 7. Therefore, it/they is/are rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mishchenko (US 11,922,144) discloses “integrating a particular external application programming interface (API) with a natural language model user interface” (abstract), which relates to the disclosed natural language model to generate a modification action.
Choi (US 2019/0147111) discloses “an interface for receiving a query from a client through a plurality of access channels, and delivering a response generated in response to the received query to the client” (abstract), which relates to the disclosed receiving an input from a user and generating a reply using a natural language model.
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/JACOB D DASCOMB/ Primary Examiner, Art Unit 2198