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 .
DETAILED ACTION
1. This initial office action is based on the application filed on 09/27/2024, which claims 1-20 have been presented for examination.
Status of Claim
2. Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 8 and 15 are presented in independent form.
Priority
3. This application is a CON of application 17/674,477 filed on 02/17/2022 PAT 12141562.
Information Disclosure Statement
4. No information disclosure statement (IDS) has been submitted in this application.
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in 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.
Double Patenting
6. 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.
7. Claims (1-5), (8-12) and (15-19) are rejected on the ground of nonstatutory double patenting as being unpatentable over claims (1-5), (7-11) and (13-17) of U.S. Patent No. 12,141,562 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims (1-5), (7-11) and (13-17) of U.S. Patent No. 12,141,562 B2 recite the elements of claims (1-5), (8-12) and (15-19) of the instant application 18/900,614. Both claim features of the instant application 18/900,614 and US Patent No. 12,141,562 B2 can be compared as follows:
Instant Application
18/900,614
U.S. Patent No.
12141562
1. A computer-implemented method comprising: identifying an updated artifact to replace an outdated artifact on a target device, the updated artifact storing data for running a neural network, the outdated artifact storing data for running an earlier version of the neural network;
1. A computer-implemented method comprising: scanning a repository to identify an updated artifact stored in the repository, the updated artifact storing data for running a neural network; producing a declaration that indicates that the updated artifact is to be installed on a target device; querying the target device to obtain state information associated with an outdated artifact stored on the target device, the outdated artifact being an earlier version of the updated artifact, wherein an outdated program installed on the target device pairs the outdated artifact with an outdated serving program for interpreting and using the updated artifact; determining, from a comparison between the state information and the declaration, that the outdated artifact stored on the target device is not up to date;
deploying, to the target device, a deployment item pairing the updated artifact with an updated serving program for interpreting and using the data stored by the updated artifact to run the neural network, the deployment item prompting installation of both the updated artifact and the updated serving program to run the neural network as a new program on the target device.
deploying, to the target device, a deployment item pairing the updated artifact with an updated serving program for interpreting and using the data stored by the updated artifact to run the neural network, the deployment item prompting installation of both the updated artifact and the updated serving program to run the neural network as a new program on the target device.
2. The computer-implemented method of claim 1, wherein the updated serving program is separate and distinct from the updated artifact.
2. The computer-implemented method of claim 1, wherein the updated serving program is separate and distinct from the updated artifact.
3. The computer-implemented method of claim 1, further comprising:
preparing a declaration in accordance with policy configuration information, the declaration configured to be produced by the neural network to indicate that the updated artifact is to be installed on the target device.
3. The computer-implemented method of claim 1, wherein producing the declaration comprises: preparing the declaration in accordance with policy configuration information.
4. The computer-implemented method of claim 1, further comprising: querying the target device to obtain updated state information after deploying the deployment item.
4. The computer-implemented method of claim 1, further comprising: querying the target device to obtain updated state information after deploying the deployment item.
5. The computer-implemented method of claim 4, further comprising: determining that the target device includes an extraneous artifact based on the updated state information and policy configuration information.
5. The computer-implemented method of claim 4, further comprising: determining that the target device includes an extraneous artifact based on the updated state information and the policy configuration information.
System claims 8-12 recite the same limitations as claims 1-5.
System claims 7-11 recite the same limitations as claims 1-5.
A hardware storage device claims 15-19 recite the same limitations as claims 1-5.
A hardware storage device claims 13-17 recite the same limitations as claims 1-5.
Patent No. 12141562 does not teach identifying an updated artifact to replace an outdated artifact on a target device, the updated artifact storing data for running a neural network, the outdated artifact storing data for running an earlier version of the neural network. However, Phillippe discloses the indication of failure, a reason for failure states that the version reference file numbers of the component version file in the model artifact and the latest version file of YAML do not match in reference number…suggested action which has been performed as an automated remedial function, namely that the updated version reference file - See paragraph [0125]).
Patent No. 12141562 and Tocchini are analogous art because they are in the same field of technology such that identify and deploy artifact. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify Patent No. 12141562's teaching with Tocchini teaching of identify and deploy artifact.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
8. Claims 4-7, 11-14 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims are being presented below.
Claims 4, 11 and 18 recite "querying the target device to obtain updated state information after deploying the deployment item" as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. "querying"/extracting/analyzing can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the "Mental Processes" grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 4, 11 and 18 are ineligible.
Claims 5, 12 and 19 recite " determining that the target device includes an extraneous artifact based on the updated state information and policy configuration information" as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. "determining" can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the "Mental Processes" grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 5, 12 and 19 are ineligible.
Claims 7 and 14 recite " querying the target device to obtain state information; and determining, based on the state information, that the outdated artifact is not up to date, wherein the deployment item is subsequently deployed at least partially based on the determination that the outdated artifact is not up to date" as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “querying”/extracting/analyzing and "determining" can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the "Mental Processes" grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 7 and 14 are ineligible.
Claims 6, 13 and 20 are also rejected under 35 USC 101 as depending upon rejected claims 5, 12 and 19.
Claim Rejections - 35 USC § 103
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.
9. Claim(s) 1-5, 8-12 and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Phillippe et al. (US Pub. No. 2023/0132501 A1 – herein after Phillipe) in view of Paravatha et al. (US Pub. No. 2023/0229412 A1 – herein after Paravatha).
Regarding claim 1.
Phillippe discloses
A computer-implemented method comprising:
identifying an updated artifact to replace an outdated artifact on a target device (require that the new models received or current models that are updated are validated prior to deployment in the model catalog – see paragraph [0061]. A model artifact stored on a computing device as part of sources of model validation requests in client domain 200 to be sent to MVS 100 in server domain 210 – See paragraphs [0079-0082]. Below the indication of failure, a reason for failure states that the version reference file numbers of the component version file in the model artifact and the latest version file of YAML do not match in reference number. The report further includes a suggested action which has been performed as an automated remedial function, namely that the updated version reference file has been incorporated to “fix” the version mismatch. A third check performed may be a runtime environment validation check. This check may validate whether a YAML file contains a valid reference to a runtime environment – See paragraph [0125]), the updated artifact storing data for running a neural network (compute instance selector 140 may then use a selection algorithm, such as an artificial neural network, to select one or more particular compute instances to provision from the compute instance pool 130 for performing validation on the model artifact – See paragraph [0064]. The model catalog may require that the new models received or current models that are updated are validated prior to deployment in the model catalog – See paragraph [0061]. Runtime.yaml file 620 is a file that may contain a listing of references, such as a definition of a runtime conda environment reference necessary for deploying a model with certain services – See paragraphs [0118-0119]), the outdated artifact storing data for running an earlier version of the neural network (checks may be performed on the runtime.yaml file. A first check performed may be a YAML lint validation check. This check may validate whether a YAML file contains errors, bugs, stylistic errors, and/or suspicious constructs – See paragraph [0125]. Examiner respectfully notes that YAML file contains errors is as outdated artifact. Compute instance selector 140 may then use a selection algorithm, such as an artificial neural network, to select one or more particular compute instances to provision from the compute instance pool 130 for performing validation on the model artifact – See paragraph [0064]); and
deploying, to the target device (a destination for the model artifact to be deployed – See paragraph [0094]), a deployment item pairing the updated artifact with an updated serving program for interpreting and using the data stored by the updated artifact to run the neural network (generate the model artifact, the file type, the number of underlying components, an input schema, and output schema, a data size, a data type, a programming language, a client owner of the model artifact, a source from which the model artifact was received, and/or a destination for the model artifact to be deployed – See paragraph [0094]);
Phillippe does not disclose
the deployment item prompting installation of both the updated artifact and the updated serving program to run the neural network as a new program on the target device.
Paravatha discloses
deploying, to the target device, a deployment item (a given deployment template may comprise (i) one or more identifiers of the deployment template – See paragraph [0058]) pairing the updated artifact (executable model package – See paragraphs [0058-0062]) with an updated serving program for interpreting (a respective set of execution instructions for the respective executable model package – See paragraph [0058-0062]) and using the data stored by the updated artifact to run the neural network (facilitate more streamlined updates to the deployment templates. For instance, when the deployment templates are stored in conjunction with the back-end code of the disclosed software application– See Abstract. A data science model may comprise a machine learning model that has been created by applying one or more machine learning techniques to a set of training data – See paragraph [0029]. one or more machine learning techniques that are applied could take any of various forms, examples of which may include a neural network technique – See paragraph [0045]), the deployment item prompting installation of both the updated artifact and the updated serving program to run the neural network as a new program on the target device (each available deployment template may take the form of a data object (e.g., a .yaml file or blob, a .json file or blob, etc.) comprising data that specifies a predefined configuration of a data science model in terms of how a data science model should be executed (e.g., the particular configuration of the data science model's multi-stage pipeline) and perhaps also how to integrate the data science model into the production environment of the data platform. For instance, in accordance with the present disclosure, a given deployment template may comprise (i) one or more identifiers of the deployment template, (ii) data specifying a particular executable model package that forms the basis for the predefined model configuration, and (iii) data specifying a particular set of execution instructions to apply to the executable model package in order to deploy a data science model based on the executable model package, among other possible information that may be included in a deployment template – See paragraphs [0057-0058])
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Paravatha's teaching into Phillippe's inventions because incorporating Paravatha's teaching would enhance Phillippe to enable to apply to the executable model package in order to deploy a data science model based on the executable model package, among other possible information that may be included in a deployment template as suggested by Paravatha (See paragraphs [0057-0058]).
Regarding claim 2, the computer-implemented method of claim 1,
Paravatha discloses
wherein the updated serving program is separate and distinct from the updated artifact (the deployment templates may be maintained together with the back-end code for the disclosed software application in a code repository (e.g., a Git repository or the like), while the respective set of configuration parameters corresponding to each deployment template may be stored in a database (e.g., a relational or NoSQL database) that is accessible by the model deployment subsystem – See paragraph [0072]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Paravatha's teaching into Phillippe's inventions because incorporating Paravatha's teaching would enhance Phillippe to enable to maintain the deployment templates together with the back-end code for the disclosed software application in a code repository, while the respective set of configuration parameters corresponding to each deployment template may be stored in a database as suggested by Paravatha (See paragraph [0072]).
Regarding claim 3, the computer-implemented method of claim 1, further comprising:
Phillippe discloses
preparing a declaration in accordance with policy configuration information, the declaration configured to be produced by the neural network to indicate that the updated artifact is to be installed on the target device (supply a variety of services to accompany those infrastructure components (e.g., billing, monitoring, logging, security, load balancing and clustering, etc.). Thus, as these services may be policy-driven, IaaS users may be able to implement policies to drive load balancing to maintain application availability and performance – See paragraph [0132]).
Regarding claim 4, the computer-implemented method of claim 1, further comprising:
Paravatha discloses
querying the target device to obtain updated state information after deploying the deployment item (the model deployment subsystem 202a may also begin tracking the status of the deployed data science model. The status of the deployed data science model may be expressed in terms of execution status information, such as its runtime, CPU usage, RAM usage, whether any computational errors have occurred (e.g., logging errors), etc. Further, this type of execution status information could be tracked at each stage of the data science model's multi-stage pipeline, such that a user may separately assess the model's execution status during pre-processing operations, during data analytics operations performed by the trained model object, and during post-processing operations, among other possibilities – See paragraphs [0113-0117]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Paravatha's teaching into Phillippe's inventions because incorporating Paravatha's teaching would enhance Phillippe to enable to track the status of the deployed model artifacts as suggested by Paravatha (See paragraphs [0113-0117]).
Regarding claim 5, the computer-implemented method of claim 4, further comprising:
Paravatha discloses
determining that the target device includes an extraneous artifact based on the updated state information and policy configuration information (the model deployment subsystem 202a may function to track the status of a deployed data science model and then provide status updates to a user. This function may take various forms. As one example, this function may involve calling an API of a fourth service supported by the model deployment subsystem 202a that is tasked with tracking status of each deployed data science model and then causing status updates to be provided to certain users (e.g., the user that requested deployment of a data science model and perhaps also other users that have been subscribed to status updates for a data science model) – See paragraph [0086].
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Paravatha's teaching into Phillippe's inventions because incorporating Paravatha's teaching would enhance Phillippe to enable to tracking status of each deployed model artifact and then causing status updates to be provided to certain users as suggested by Paravatha (See paragraphs [0086]).
Regarding claim 8.
A system comprising:
a processor; and
a memory that contains instructions that are readable by the processor to cause the processor to:
Regarding claim 8, recites the same limitations as rejected claim 1 above.
Regarding claim 9, recites the same limitations as rejected claim 2 above.
Regarding claim 10, recites the same limitations as rejected claim 3 above.
Regarding claim 11, recites the same limitations as rejected claim 4 above.
Regarding claim 12, recites the same limitations as rejected claim 5 above.
Regarding claim 15.
A hardware storage device comprising instructions that are readable by a processor to cause the processor to:
Regarding claim 15, recites the same limitations as rejected claim 1 above.
Regarding claim 16, recites the same limitations as rejected claim 2 above.
Regarding claim 17, recites the same limitations as rejected claim 3 above.
Regarding claim 18, recites the same limitations as rejected claim 4 above.
Regarding claim 19, recites the same limitations as rejected claim 5 above.
10. Claim(s) 6-7, 13-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Phillippe and Paravatha as applied to claims 1, 8 and 15 respectively above, and further in view of Vassenkov et al. (US Pub. No. 2021/0224107 A1 – herein after Vassenko).
Regarding claim 6, the computer-implemented method of claim 5,
Vassenkov discloses
wherein an outdated program, installed on the target device prior to deployment of the deployment item, paired the outdated artifact with an outdated serving program for interpreting and using the updated artifact (laaS deployment is the process of putting a new application, or a new version, onto a prepared application server or the like. It may also include the process of preparing the server (e.g., installing libraries, daemons, etc.). This is often managed by the cloud provider, below the hypervisor layer (e.g., the servers, storage, network hardware, and virtualization). Thus, the customer may be responsible for handling (OS), middleware, and/or application deployment (e.g., on self-service virtual machines (e.g., that can be spun up on demand) or the like - See paragraphs [0037-0040]. CIOS may always run all three of these steps when executing the declarative provisioner. The refresh operation helps recover from any updates or deletions that weren't recorded. CIOS inspects the result of the plan operation and compares it to the approved release plan - See paragraphs [0124]. A process 700 of the operations performed by the CIOS Declarative Provisioner shown in FIG. 5 to resume an execution of a deployment of a resource. routines, programs, objects, components, data structures and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the process - See paragraphs [0158-0159]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Vassenkov's teaching into Phillippe's and Paravatha’s inventions because incorporating Vassenkov's teaching would enhance Phillippe and Paravatha to enable to combine the functionality of code deployment as suggested by Vassenkov (paragraph [0082]).
Regarding claim 7, the computer-implemented method of claim 5, further comprising:
Vassenkov discloses
querying the target device to obtain state information (Using CIOS, there are a few phases of a representative customer experience: onboarding, pre-release, world-wide release, and tactical release. For the pre-release phase, the below is an example of what happens between a new artifact being built and releasing artifacts to release one (e.g., R1). This should replace some or most of current change management processes. As relevant artifacts are built, CIOS can automatically generate releases using "the latest version of everything in the flock." A release is a specific version of the flock config with specific inputs (e.g. artifact versions, realm, region, and ad). A release contains one roll- forward plan per region and metadata describing region ordering - See paragraph [0115]); and
determining, based on the state information, that the outdated artifact is not up to date, wherein the deployment item is subsequently deployed at least partially based on the determination that the outdated artifact is not up to date (inspect and approve releases through the CIOS UI. Teams can approve some but not all of the regional plans within a release. If “the latest version of everything” yielded no suitable plans, teams can ask CIOS to generate a plan for cherry-picked artifact versions – See paragraphs [0115-0117]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Vassenkov's teaching into Phillippe's and Paravatha’s inventions because incorporating Vassenkov's teaching would enhance Phillippe and Paravatha to enable to ensure any artifacts required for a successful release are present in the target's region ahead of release as suggested by Vassenkov (paragraph [0089]).
Regarding claim 13, recites the same limitations as rejected claim 6 above.
Regarding claim 14, recites the same limitations as rejected claim 7 above.
Regarding claim 20, recites the same limitations as rejected claim 6 above.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chawda et al. (US Patent No. 11,620,128 B1) discloses a software analysis service that obtains, for one or more software applications undergoing evaluation, a collection of application artifacts, application profiling metrics, and other application profile data. A collection of features is extracted from the application artifacts and metrics and used as input to a ML model trained to determine whether a software application likely is monolithic – See Abstract and specification for more details.
Ramalingam et al. (US Pub. No. 2023/0205510 A1) discloses operate an application upgrader for the target software platform, the application upgrader to: consume application artifacts from a continuous integration (CI) system, wherein the target software platform runs a version of the application artifacts; obtain a signed manifest output from the CI system, the signed manifest identifying the consumed application artifacts; and compare a signature of the application artifacts from the CI system to a signature of the version running on the target software platform to determine if an update of the version running on the target software platform is required – see Abstract and specification for more details.
Malvankar et al. (US Pub. No. 2022/0147333 A1) discloses sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file – See Abstract and specification for more details.
Dattatri et al. (US Pub. No. 2020/0034133 A1) discloses determine based at least in part on the telemetry data, (1) a device (e.g., hardware and software) configuration associated with the computing device and (2) one or more events (e.g., an installation log, a memory dump, or the like) associated with installing a software package on the computing device – See Abstract and specification for more details.
Allen et al. (US Patent No. 11,016,749 B1) discloses determines a continuation deployment action that describes information about a target machine to which the software artifacts are applicable. When appropriate, the deployment proxy service provides information to the deployment service that enables the deployment service to deploy the software artifacts to the target machine (e.g., to update an application of the target machine) – See Abstract and specification for more details.
Karpoff et al. (US Pub. No. 2019/0205113 A1) discloses retiring a dynamically updatable function includes receiving, by a collector-thread, a registration of the function, wherein the registration indicates to the collector-thread addresses of memory locations for counters that count a number of calls currently being made to a previous version of the function by a plurality of execution threads; reading, by the collector-thread, values of the counters; and when the values of all the counters are zero, deleting, by the collector-thread, the function from a storage medium on a device previously executing the previous version of the function – See Abstract and specification for more details.
Hawrylo et al. (US Pub. No. 2019/0129701 A1) discloses identify a release and pertinent information thereof for a software application delivery model and determine dependencies among at least some of the pertinent information. Tracking records may be generated at least by tracking the release based in part or in whole upon the dependencies. The release or a portion of the release may be advanced from a current stage to a next stage along a release pipeline based in part or in whole upon the tracking records – See Abstract and specification for more details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONGBAO NGUYEN whose telephone number is (571)270-7180. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached at 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MONGBAO NGUYEN/ Examiner, Art Unit 2192
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