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
Claims 1-20 are pending in this application. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 01/23/2026 have been fully considered but they are not persuasive.
Applicant’s argue on pages 8-9, the cited references operate in a fundamentally different technical domain schema management and not telemetry instrumentation and therefore cannot reasonably be combined. The examiner respectfully disagrees.
The examiner notes that the rejection does not rely on Eberlein to teach telemetry instrumentation itself. Rather Hulick teaches monitoring applications using monitoring agents that collect application data including metrics and metadata. Eberlein is relied upon for teaching recommendation workflows in which data types are identified, recommendations corresponding to such data types are generated, user interactions occur regarding the recommendations, and configurations/extensions are generated responsive thereto. Guiterrez is relied upon for language-model teachings. Accordingly, the rejection relies upon each reference for its respective teachings rather than equating data base schema management with telemetry instrumentation.
Applicant further argues that Eberlein utilizes historical data while the claims require prospective data collection. The examiner notes that claim 1 does not recite prospective data collection, future telemetry collection, runtime instrumentation, or temporal limitations regarding when data is collected. Claim 1 recites maintaining a catalog of attributes, receiving a prompt requesting collection of a particular type of data, generating a recommendation.
Applicant’s also argue that claim 1 requires natural language prompts requesting telemetry collections whereas Eberlein provides recommendations that users accept or reject. Claim 1 does not recite conversational interfaces, free-form natural language prompts, direct interaction with a large language model, telemetry-specific prompts, or any particular structure, timing, or format of the claimed prompt. Eberlein’s recommendation workflow utilizes user interactions concerning data-type functionality reasonably teaches or suggests receiving user input associated with requested data functionality. In addition, Gutierrez discloses language-model processing responsible to prompts and recommendation generation.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the claimed invention solves this by providing language model assisted interface for configuring agents to collect telemetry without code changes) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Eberlein teaches generating recommendations and corresponding executable configurations/extensions responsive to identified data type and user interactions. It would have been obvious to one of ordinary skill that Eberlein suggests generating recommended configurations responsive to requested data functionality. Hulick provides the monitoring-agent framework to which such recommendation/configuration teachings are applied.
Applicant’s argues that Eberlein fails to each or suggest the claimed “prompt for input language model that requests collection of a particular type of data” because Eberlein provides recommendations prior to user interactions and thereafter receives user acceptance of rejection. However, claim 1 does not positively recite a particular order, format, structure, or timing of the claimed limitations beyond simply reciting receiving a prompt for input to a language model requesting collection of a particular type of data. Claim 1 further does not recite conversational interfaces, natural language prompting, telemetry specific prompting workflows, direct interaction with a language model, or any specific mechanism by which the prompt is generated or received. Eberlein teaches workflows involving identifying data types, providing recommendations corresponding to such data types, receiving user interactions regarding recommendations, and generating responsive extensions/configurations. One of ordinary skill in the art would have known such teachings suggest receiving user interactions associated with requested data. Furthermore, Guiterrez teaches language-model processing responsive to prompts and recommendation generation.
Applicant further argues that the database schema management and telemetry instrumentation are different technical domains and therefore there is no motivation to combine. However, obviousness does not require references to originate from identical technical domains or address identical problems. The rejection relies upon Hulick for monitoring-agent collection of application data, Eberlein for recommendation workflows and responsive generation or extensions/configurations, and Guiterrez for language-model recommendation generation. One of ordinary skill in the art would have been motivated to combine these teachings in order to automate generation of recommendations/configurations corresponding to requested application data types, thereby improving configurability, reducing manual configuration effort, and improving usability.
Applicant additionally argues that adding Guiterrez’s language model to Eberlein would still result in database schema modifications rather than telemetry instrumentation. The examiner respectfully disagrees as the rejection does not rely upon modifying Eberlein in isolation. Rather Gutierrez language-model teaches are combined with Hulick’s monitoring-agent framework and Eberlein’s recommendation/configuration teachings. This rejection relies upon the references collectively for their respective teachings rather than substituting one reference as a whole for another.
The examiner notes that it seems applicant’s intends the claimed “prompt” to specifically require a natural-language request submitted to a large language model prior to generation of the recommendation. The examiner does not interpret the claimed limitations to have such features. Should applicant’s intention require such prompt, the examiner welcomes applicant’s to request an interview where such language maybe needed to more particularly define such features of the invention.
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.
Claims 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hulick (US 2023/0033681) and in view of Eberlein (US 2022/0335031) and in view of Guitierrez (US 2023/0252233).
Re Claim 1, Hulick discloses a method, comprising: maintaining, by a device, a catalog of attributes that can be collected via a monitoring agent from an application ([0027], monitoring applications using agents installed at individual machines. The agents collect data associated with the applications of interest. The collected data may include performance data, metrics, metadata, topology data, etc)
Hulick does not disclose, however Eberlein discloses receiving, at the device and from a user interface, a prompt for input to a language model that requests collection of a particular type of data from the application ([0016], identify the set of data types of the set of data objects and providing a recommendation for a first extension code corresponding to a first data type in the set of data types);
generating, by the device, a response to the prompt that includes a recommended configuration for the monitoring agent to collect the particular type of data from the application ([0016], after providing a recommendation for a first extension code, receiving user input indicating acceptance of the recommendation for the first extension); and
providing, by the device, the response to the user interface for display ([0016], after providing a recommendation for a first extension code, receiving user input indicating acceptance of the recommendation for the first extension. In response, automatically providing extension code that is executable and modify a user interface of an application).
It would have been obvious for one of ordinary skill in the art before the date the current invention was effectively filed to have modified the teachings of Hulick’s OpenTelemetry with Eberlein’s recommendation extension in order to provide additional microservices. One of ordinary skill in the art would have been motivated to incorporate the teachings with one another in order to allow the users to provide additional microservices without having to alter/change the code.
Eberlein discloses in [0046] that is uses a machine learning model platform to train. Hulick and Eberlein does not disclose, however Guitierrez discloses using a large language model ([0474], [0467], System usings a large language model trainted with data from SKL libraries. Leveraging large language models to suggest the SKL library). It would have been obvious for one of ordinary skill in the art before the date the current invention was effectively filed to have modified the teachings of Hulick’s OpenTelemetry with Guiterrez’s OpenTelemetry to utilize LLM to train machine models. One of ordinary skill in the art would have been motivated to incorporate the teachings with one another in order to allow the machine models to learn from users using a LLM.
Re claim 2, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick for the same reasons identified in the rejection of claim 1. In addition, Guiterrez discloses storing representations of the attributes along with a reverse index that associates each representation with corresponding metadata for configuring the monitoring agent to collect a corresponding type of data (Guitierrez [0428]-[0431], the libraries index the schemas which include the metadata to reference the locations and versions for the machine learning model. [0607]-[0608], The analytics server generate unique identifiers for the described files, identify related files, periodically pull or collect data, and update the data structure and use the unique identifiers to display related files).
Re claim 3, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick and Eberlein for the same reasons identified in the rejection of claim 1. In addition, Guiterrez discloses wherein the corresponding metadata of an attribute includes one or more of: a file name, a method name, an attribute name, or a function return value associated with the attribute ([0397], the SKL library may relate to different SKL attributes that represents FILE Schema).
Re claim 4, Hulick discloses customize data collection by the monitoring agent to include collection of the particular type of data without altering code of the monitoring agent ([0096]-[0098], the circuit breaker (microservice) can be inserted without making any changes to the code of the application). One of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick for the same reasons identified in the rejection of claim 1. In addition, Eberlein discloses wherein the recommended configuration defines an extension configuration ([0016], after providing a recommendation for a first extension code, receiving user input indicating acceptance of the recommendation for the first extension.
Re claim 5, Hulick discloses the catalog of attributes that correspond to the particular type of data specified in the prompt ([0132], naming the circuit breaker microservices based on the OpenTelemetry attributes).
One of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick and Eberlein for the same reasons identified in the rejection of claim 1. In addition, Guiterrez discloses identifying attributes ([0400], the SKL library servers as distribution of attributes)
Re claim 6, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick for the same reasons identified in the rejection of claim 1. In addition, Eberlein discloses generating the recommended configuration based on metadata ([0016], after providing a recommendation for a first extension code, receiving user input indicating acceptance of the recommendation for the first extension).
One of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick and Eberlein for the same reasons identified in the rejection of claim 1. In addition, Guiterrez discloses associated with the attributes within the catalog of attributes that correspond to the particular type of data specified in the prompt ([0400], the SKL library servers as distribution of attributes).
Re claim 7, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick for the same reasons identified in the rejection of claim 1. In addition, Eberlein discloses generating a recommended configuration for the monitoring agent to collect an additional type of data based on a relationship of the additional type of data to the particular type of data ([0016], after providing a recommendation for a first extension code, receiving user input indicating acceptance of the recommendation for the first extension).
Re claim 8, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Hulick for the same reasons identified in the rejection of claim 1. In addition, Eberlein discloses prioritizing configurations for recommendation based on a frequency of corresponding data collected at the application ([0035], [0038], The extension generator is done by reading metadata of the DO fields includes utilizing the frequency of value types for historical).
Re claim 10, Hulick discloses wherein the recommended configuration causes the particular type of data collected from the application to be added to a tracing span ([0013], device instruments an application to generate Open Telemetry trade data during execute of the application to insert a circuit breaker microservices for the particular method).
Re claims 11-18 and 20, they are similar claims to 1-8 and 10 and therefore are rejected for the same reasons above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hulick and in view of Eberlein and in view of Guitierrez and in view of Dalgaard (US 12,321,250).
Re Claims 9 and 19, Hulick, Eberlein, and Guitierrez does not disclose, however Dalgaard discloses wherein the recommended configuration causes the particular type of data collected from the application to be added to a tracing span ([0013], device instruments an application to generate Open Telemetry trade data during execute of the application to insert a circuit breaker microservices for the particular method).
It would have been obvious for one of ordinary skill in the art before the date the current invention was effectively filed to have modified the teachings of Hulick, Eberlein, and Guitierrez’s OpenTelemetry data collection with Dalgaard’s collected agents under control plane. One of ordinary skill in the art would have been motivated to incorporate the teachings with one another in order to allow the collected agents to perform actions under the control of the control plane.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HO T SHIU whose telephone number is (571)270-3810. The examiner can normally be reached Mon-Fri (9:00am - 5:00pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas Taylor can be reached at 571-272-3089. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HO T SHIU/Examiner, Art Unit 2443
HO T. SHIU
Examiner
Art Unit 2443
/NICHOLAS R TAYLOR/Supervisory Patent Examiner, Art Unit 2443
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