DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 05/08/2026. Claims 1-18 and 20 have been amended. Therefore, claims 1-20 are pending and have been addressed below. 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 § 101
1. 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.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract without significantly more.
3. Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a machine (i.e., concrete thing, consisting of parts, or of certain devices and combination of devices), claim 15 recites a manufacture (i.e., an article that is given a new form, quality, property, or combination through man-made or artificial means), and claim 18 recites a process (i.e., an act or step, or a series of acts or steps). Thus, each of the claims fall within one of the four statutory categories.
4. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claim 1 recites:
“obtaining a description of an incident relating to an entity, wherein the description is provided from a user”, and “wherein the entity comprises a product or a service that is offered by an organization to a plurality of users that includes the user;”, “storing the description…in association with an incident report;”, “determining, based on at least one load metric whether to scale down computational resource usage, wherein the at least one load metric comprise a queue depth;”, “responsive to a determination to scale down the computational resource usage based on the at least one load metric, causing…to be deactivated…is not used to process incident reports while a load threshold continues to be satisfied;”, “performing load balancing to distribute incident reports, including the incident report”, “based on the load balancing”, “generating…a recommended classification for the incident, wherein the generating involves converting the description into a vector and performing semantic searching for the vector;” and “based on the recommended classification”
Claim 15 recites:
“using results of statistical exception analysis of incident data relating to a plurality of entities”, “generate recommended classifications of incidents associated with one or more of the plurality of entities”, “is based on text in the incident data”, and “wherein the plurality of entities comprise products or services that are offered by an organization to a plurality of users;”, “causing…to facilitate…classification or resolution of reported incidents”, “determining, based on at least one load metric associated with…that stores incident reports, whether to scale down computational resource usage…, wherein the at least one load metric comprises a queue depth;”, “responsive to a determination to scale down the computational usage based on the at least one load metric, causing…to be deactivated such that…is not used to process incident reports while a load threshold continues to be satisfied;”, “performing load balancing to distribute incident reports…;”, “based on the load balancing,…generating recommended classifications for the incident reports, wherein generating of a recommended classification for a first incident report involves converting a description of the first incident report into a vector and performing semantic searching for the vector…;”, and “based on the recommended classification”
Claim 18 recites:
“receiving…a description of an incident relating to an entity”, “wherein the description is provided…a user”, and “wherein the entity comprises a product or a service that is offered by an organization to a plurality of users that includes the user;”, “storing…the description…in association with an incident report;”, “determining…and based on one load metric…, whether to scale down computational resource usage…, wherein the at least one load metric comprise a queue depth…;”, “responsive to a determination to scale down the computational resource usage based on the alt least one load metric, causing…to be deactivated…is not used to process the incident reports while a load threshold continues to be satisfied;”, “performing…load balancing to distribute incident reports…, including the incident report”, “causing…a prediction of a classification to be made for the incident based on the description, wherein the prediction comprises converting the description into a vector and performing semantic searching for the vector…;” and “based on the classification”
The limitations under the broadest reasonable interpretation recite the abstract idea of performing incident classification and searching tasks for an entity which encompasses mental processes, (i.e., observations, evaluations, judgments, and opinions), commercial or legal interactions (i.e. legal obligations, marketing or sales activities or behaviors, business relations), managing personal behavior or interactions between people. As such, the limitations cover concepts that fall within the mental processes and certain methods of organizing human activity groupings enumerated in MPEP 2106.04 II
The Applicant’s Specification in at least [0002] Many organizations offer software products (e.g., enterprise software applications, e-mail systems, mobile device management applications, etc.) to users, such as employees or clients, and staff a help desk with a support ticket system or scheme to service any incidents (i.e., problems or issues) that the users may experience when using these products. Oftentimes, general issues that impact many users go unidentified for days or weeks, which delays root cause analyses and the identification of corresponding resolutions and workarounds. Such delays can lead to prolonged disruptions and decreased user satisfaction. Even if a particular issue with a product is identified, the problem is usually not well linked to relevant solutions and identifiable using keyword searches. This makes it difficult to automatically provide a resolution to affected users at support ticket creation time. The manual process of reviewing and categorizing tickets is also time consuming and prone to errors. Furthermore, manually reviewing all of the data to identify significant trends and anomalies relating to support tickets on a daily basis can be an insurmountable task, even if many human data analysts are available for this purpose. The large volume of data and the complexity of identifying meaningful patterns therefore make reliance solely on human reviewers insufficient. This limitation hinders the help desk’s ability to detect and respond to issues promptly, which results in operational inefficiencies. Additionally, existing help desk operations are prone to inefficient resource utilization. The supporting analytical tools and systems, such as databases, search algorithms, etc., that analysts manually access to help address incidents can require significant resources to run. This inefficiency is compounded by the lack of a scalable architecture, as the typical help desk system cannot dynamically adjust to fluctuating volumes of incident reports. During peak times, the system may become overwhelmed, whereas during off-peak times, systems generally remain fully operational, which results in wasted resources. Help desk analysts are also prone to treating all incoming data equally without necessarily prioritizing high-impact incidents. This can result in further undue and inefficient consumption of computing resources and power resources.
Consistent with the disclosure, the limitations in the context of the claim as a whole pertain to mental processes for receiving an incident reports, recognizing/searching for certain information from within the incident reports, generating/predicting classification results from the information described within the incident reports. The steps of “determining…at least one load metric”, “responsive to a determinization”, performing…load balancing to distribute incident reports”, “causing…a prediction of a classification to be made for the incident” are acts or tasks that may be performed by manually by human administrators concerned with managing workloads for the entity. Thus, the limitations can be performed in the human mind or by a person with use of pen and paper, and can be reasonably characterized as falling within the mental processes grouping. Similarly, the limitations recite steps for allowing a user to provide a description of an incident which is then searched for solutions related to resolving the incident by comparing vector mappings. Thus, the limitations correspond to a business practices normally performed by entities that manage helpdesk or support tickets which may be reasonably characterized as a commercial interaction, i.e., marketing/sales activities, business relations, and managing personal behavior or interactions between people. The recited limitations in the claim(s) is/are an abstract idea.
5. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “a system, comprising:”, “a plurality of environments that respectively include a prediction service configured to utilize a corresponding artificial intelligence (AI) model instance of a plurality of AI model instances”, “at least one processor;”, “a memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising:”, “from a user device”, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “a corresponding prediction service in the at least one environment”, “across a remainder of the plurality of environments;“, “one or more databases of vectors”– see claims 1, “a non-transitory machine-readable medium, comprising executable instructions that, when executed”, “by a processor of a system that includes a plurality of environments each configured with a respective prediction service, facilitate performance of operations, the operations comprising:”, “training an artificial intelligence (AI) model”, “wherein the AI model is trained to”, “wherein the training of the AI model involves fine tuning of a transformer-based pre-trained model that understands natural language, wherein the fine tuning”, “causing a respective instance of the AI model to be deployed with each respective prediction service”, “resulting in a plurality of instance of the AI model;, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “the respective prediction service configured in the at least one environment”, “the incident queue across a remainder of the plurality of environments”, “via a remainder of the plurality of instance of the AI model in the remainder of the plurality of environments”, “one or more databases of vectors”, “a user device” – see claim 15, “by a processor of a system that includes a plurality of environments each configured with a respective prediction service associated with a respective artificial intelligence (AI) model instance”, “a user device”, “by the processor”, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “the respective prediction service in the at least one environment”, “across a remainder of the plurality of environments;”, “using a first AI model instance in the remainder of the plurality of environments”, “one or more databases of vectors”, “the user device” – see claim 18 are recited at a high-level of generality in light of the specification. Thus, because the specification describes the additional elements in general terms without any of the particulars, the additional elements may be broadly construed as reciting generic computer components performing conventional computer functions in light of the applicant’s specification. At best, the additional elements add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f).
The other additional elements of “causing…pre-programmed script to be deployed…”, wherein deployment of the…pre-programmed script triggers…to execute the automated pre-programmed script to resolve the incident.” adds insignificant extra-solution activity, i.e., data transmitting, to the judicial exception, as discussed in MPEP 2106.05(g).
The other additional elements of: “automated”, “automatic” is/are an attempt to limit claim to a particular technological environment or field of use, as discussed in MPEP 2106.05(h)
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
6. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “a system, comprising:”, “a plurality of environments that respectively include a prediction service configured to utilize a corresponding artificial intelligence (AI) model instance of a plurality of AI model instances”, “at least one processor;”, “a memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising:”, “from a user device”, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “a corresponding prediction service in the at least one environment”, “across a remainder of the plurality of environments;“, “one or more databases of vectors”– see claims 1, “a non-transitory machine-readable medium, comprising executable instructions that, when executed”, “by a processor of a system that includes a plurality of environments each configured with a respective prediction service, facilitate performance of operations, the operations comprising:”, “training an artificial intelligence (AI) model”, “wherein the AI model is trained to”, “wherein the training of the AI model involves fine tuning of a transformer-based pre-trained model that understands natural language, wherein the fine tuning”, “causing a respective instance of the AI model to be deployed with each respective prediction service”, “resulting in a plurality of instance of the AI model;, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “the respective prediction service configured in the at least one environment”, “the incident queue across a remainder of the plurality of environments”, “via a remainder of the plurality of instance of the AI model in the remainder of the plurality of environments”, “one or more databases of vectors”, “a user device” – see claim 15, “by a processor of a system that includes a plurality of environments each configured with a respective prediction service associated with a respective artificial intelligence (AI) model instance”, “a user device”, “by the processor”, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “the respective prediction service in the at least one environment”, “across a remainder of the plurality of environments;”, “using a first AI model instance in the remainder of the plurality of environments”, “one or more databases of vectors”, “the user device” – see claim 18 at best amount to no more than mere instructions in which to apply the judicial exception and do not provide an inventive concept.
The other additional elements of “causing…pre-programmed script to be deployed…”, wherein deployment of the…pre-programmed script triggers…to execute the automated pre-programmed script to resolve the incident” were considered extra-solution activity under Step 2A-Prong Two, and must be re-evaluated at Step 2B to determine whether the additional elements are well-understood, routine, and/or conventional.
MPEP 2106.05(d)(II) cites the Symantec, TLI Communications, OIP Techs, buySAFE court decisions which indicate: “receiving or transmitting data over a network” is/are considered computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner. Thus, when viewed individually and as an ordered combination, the additional elements do not provide an inventive concept under Step 2B.
7. Claims 2-14, 16-17, and 19-20 are dependents of claims 1, 15, and 18.
Claim 2 recites “wherein the recommended classification comprises classification of the incident as being resolvable using the automated pre-programmed script.” which further describes the data or information recited in the abstract idea, but does not make the claim any less abstract, Claim 3 recites “wherein the operations further comprise: based on the load balancing…generating a second recommended classification for a second incident, wherein the generating the second recommended classification involves converting a description associated with the second incident into a second vector and performing semantic searching for the second vector…, wherein the semantic searching for the second vector comprises sequential semantic searching that involves (i) a first semantic searching relative to vectors relating to…pre-programmed scripts, (ii) a second semantic searching relative to vectors relating to known major incidents if the first semantic searching fails to satisfy a similarity threshold, and (iii) a third semantic searching relative to vectors relating to technical working groups if the second semantic searching fails to satisfy the similarity threshold, and wherein the second recommended classification is generated based on which of the first semantic searching, the second semantic searching, or the third semantic searching satisfies the similarity threshold; and causing an action to be performed in relation to the second incident based on the second recommended classification.” which further narrows how the abstract idea may be performed but does not make the claimed invention any less abstract, Claim 4 recites “wherein the second recommended classification comprises classification of the second incident as belonging to a known major incident” which further describes the data or information recited in the abstract idea, but does not make the claim any less abstract, Claim 5 recites “wherein the action comprises associating the second incident with the known major incident.” which further narrows how the abstract idea may be performed but does not make the claimed invention any less abstract., Claim 6 recites “wherein the second recommended classification comprises classification of the second incident as corresponding to a technical working group” which further describes the data or information recited in the abstract idea, but does not make the claim any less abstract, Claim 7 recites “wherein the action comprises routing data regarding the second incident to the technical working group” which further narrows how the abstract idea may be performed but does not make the claimed invention any less abstract, Claim 8 recites “wherein the one or more databases of vectors comprise vectors relating to automated pre-programmed scripts for resolving incidents.” which further describes the data or information that may be used in the abstract idea, but does not make the claim any less abstract. Here, the additional element of “one or more databases” operates in its normal or ordinary capacity to store and retrieve information., Claim 9 recites “wherein the one or more databases of vectors comprise vectors relating to known major incidents” which further describes the data or information that may be used in the abstract idea, but does not make the claim any less abstract. Here, the additional element of “one or more databases” operates in its normal or ordinary capacity to store and retrieve information., Claim 10 recites “wherein the one or more databases of vectors comprise vectors relating to technical working groups.” which further describes the data or information that may be used in the abstract idea, but does not make the claim any less abstract. Here, the additional element of “one or more databases” operates in its normal or ordinary capacity to store and retrieve information, Claim 11 recites “wherein the operations further comprise, according to load balancing…, providing the description…to generate the recommended classification.” which describes the steps necessary to carry out the abstract idea and does not make the claim any less abstract, Claim 12 recites “wherein…based on results of statistical exception analysis of incident data relating to a plurality of entities that includes the entity.” which describes the type of information or data being used to update the AI model. Here, the AI model is trained amounts to generic data processing and adds the words “apply it” to the judicial exception. See MPEP 2106.05(f), Claim 13 recites “wherein the statistical exception analysis includes identifying trends or outlier data points in the incident data.” which further describes the data or information recited in the abstract idea, but does not make the claim any less abstract, Claim 14 recites “wherein the obtaining is responsive in real-time to a received report of the incident.” which further narrows how the abstract idea may be performed. Here, “real-time” is an attempt to limit the claimed invention to a particular technological environment or field of use. See MPEP 2106.05(h), Claim 16 recites “wherein generating of a second recommended classification for a second incident report comprises semantic searching that involves a conversion of a description associated with the second incident report into a second vector, and a comparison of the second vector with other vectors….” which further narrows how the searching step is done within abstract idea but does not make the claim any less abstract. Here, the additional element of “one or more databases of vectors” operates in its normal or ordinary capacity to store and retrieve information, Claim 17 recites “wherein the semantic searching for the second vector comprises sequential semantic searching that involves (i) a first semantic searching relative to vectors relating to…pre-programmed scripts, (ii) a second semantic searching relative to vectors relating to known major incidents if the first semantic searching fails to satisfy a similarity threshold, and (iii) a third semantic searching relative to vectors relating to technical working groups if the second semantic searching fails to satisfy the similarity threshold.” which further describes the data or information recited in the abstract idea that may be necessary for searching, but does not make the claim any less abstract, Claim 19 recites “wherein…comprise vectors relating to…pre-programmed scripts for resolving incidents, vectors relating to known major incidents, and vectors relating to technical working groups.” which further describes the data or information that may be used in the abstract idea, but does not make the claim any less abstract. Here, the additional element of “one or more databases” operates in its normal or ordinary capacity to store and retrieve information, Claim 20 recites “wherein predictions of classifications involve sequential semantic searching such that searching relative to the vectors relating to technical working groups is performed after a known major incident is unable to be identified from searching relative to the vectors relating to known major incidents, and the searching relative to the vectors relating to known major incidents is performed after an automated pre-programmed script is unable to be identified from searching relative to the vectors relating to automated pre-programmed scripts.” which further narrows how the prediction step in abstract idea may be performed in the abstract idea, but does not make the claim any less abstract. Accordingly, when considered individually and as a whole none of the limitations recited by the dependent claims integrate the abstract idea into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
8. 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. 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.
10. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mordovtsev (US 2025/0298582 A1) in view of Rathod (US 2020/0202302 A1) in view of Bhat (US 2025/0383970 A1) in further view of Shen (US 2013/0198319 A1)
With respect to claims 1 and 18, Mordovtsev discloses
a system and a method (abstract, Fig. 1: discloses system 100), comprising:
at least one processor (¶ 0020); and
a memory (¶ 0020) that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising:
obtaining a description of an incident relating to an entity (¶ 0080,0086: discloses receiving a request),
wherein the description is provided (¶ 0080: discloses the request comprises input describing a purpose or other text-based description) from a user device associated with a user (¶ 0021-0022, 0087: discloses computing device 102), and
wherein the generating involves converting the description into a vector (¶ 0095, 0111, 0121: discloses vectorization can be utilized to convert the text into a numerical vector. The embeddings model generates a high-dimensional vector that represents the semantic meaning of the request in numerical form, for example as a request vector. LLM embeddings model 212 is utilized to convert the request into one or more embeddings.) and
performing semantic searching for the vector in one or more databases of vectors (¶ 0096-0097, 0111-0112: discloses the request vector can be used in a similarity search. Receive details about the request and search in vector store 108.); and based on the recommended classification, causing an automated pre-programmed script to be deployed to the user device (¶ 0098: discloses provide tailored script to computing device 102. For example, communicates a script generated by or tailored by AI model 114 to a user of computing device.),
wherein deployment of the automated pre-programmed script triggers the user device to execute the automated pre-programmed script to resolve the incident. (¶ 0098: discloses automatically by computing device 102 can apply the script to the affected system, i.e., computing device 102. For example, a script generated by or tailored by AI model 114 can be executed on or in coordination with computing device 102)
The Mordovtsev reference does not explicitly disclose the following limitations. In the same field of endeavor, the Rathod reference is related to a system for classifying and routing incident tickets (abstract) and teaches:
wherein the entity comprises a product or a service that is offered by an organization to a plurality of users that includes the user (¶ 0010, 0016: discloses enterprises are commonly supported by IT service management systems that allow users to file incident tickets related to IT service issues.);
generating a recommended classification for the incident (¶ 0034: discloses incident categories 114 may be defined and/or assigned to incident tickets using an artificial neural network, support vector model, or any other type of machine learning or modeling technique.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system and methods of Mordovtsev, to include wherein the entity comprises a product or a service that is offered by an organization to a plurality of users that includes the user; generating a recommended classification for the incident, as disclosed by Rathod to achieve the claimed invention. As disclosed by Rathod, the motivation for the combination would have been to improve the accuracy and efficiency with which incident tickets are routed and handled. (¶ 0012)
The combination of Mordovtsev and Rathod does not explicitly disclose the following limitations. However, Bhat is related to systems and methods that orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical model cascade to classify queries into agent domains (abstract) and teaches:
a plurality of environments that respectively include a prediction service (¶ 0264) configured to utilize a corresponding artificial intelligence (AI) model instance of a plurality of Al model instances (¶ 0061, 0063-0064); storing the description in an incident queue in association with an incident report (¶ 0074: discloses stores records in a log database.); determining, based on at least one load metric associated with the incident queue (¶ 0038, 0073), wherein the at least one load metric comprises a queue depth of the incident queue (¶ 0073); performing load balancing to distribute incident reports in the incident queue, including the incident report, across a remainder of the plurality of environments (¶ 0078, 0264: discloses the architecture can implement load balancing across multiple instances of a single model, where one of multiple instances of a model is chosen based on a queue depth of each instance.); based on the load balancing (¶ 0078, 0264), and via a first Al model instance of a remainder of the plurality of Al model instances, generating a recommended classification for the incident (¶ 0063, 0075: discloses each level can be configured to perform a certain task or characterize a certain aspect of the query, and can produce a classification and a confidence score. The classification can correspond to one or more AI agents.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mordovtsev and Rathod to include the load balancing techniques, as disclosed by Bhat to achieve the claimed invention. As disclosed by Bhat, the motivation for the combination would have been to leverage the instances to improve efficiency of resource use and prevent bottlenecks (¶ 0078, 0264)
The combination of Mordovtsev, Rathod, and Bhat does not explicitly disclose the following limitations. The Shen reference is related to dynamic allocation and deallocation of computing resources in a computing cluster (¶ 0014)
determining whether to scale down computational resource usage of the system (¶ 0014, 0024: discloses determining an action including a scale down action based on the application performance metric.),
responsive to a determination to scale down the computational resource usage based on the at least one load metric (¶ 0014, 0047: discloses multiple instances of a software application may be executed in a computing cluster, and when a performance metric associated with the software application violates a redetermined threshold value, an action, such as a scale-up action or a scale-down action, may be performed.),
causing at least one environment of the plurality of environments to be deactivated such that a corresponding prediction service in the at least one environment is not used to process incident reports while a load threshold continues to be satisfied (¶ 0014, 0047-0048: discloses a scale down action indicates that one or more working virtual application instances should be deactivated.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mordovtsev, Rathod, and Bhat to include the scaling techniques, as disclosed by Shen to achieve the claimed invention. As disclosed by Shen, the motivation for the combination would have been to facilitate automatically and dynamically scaling the quantity of web application instances according to the observed or measured workload and distributing workloads to all web servers because dynamic scaling may conserve energy, resource consumption, and cost. (¶ 0016)
With respect to claim 2, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1,
wherein the recommended classification comprises a classification of the incident as being resolvable using the automated pre-programmed script. (¶ 0097: Mordovtsev discloses performing a similarity search between the request and description and matches a most suitable script based on the specific incident and other factors)
With respect to claim 3, Mordovtsev discloses the system of claim 1, wherein the operations further comprise:
converting a description associated with the second incident into a second vector (¶ 0095, 0111: discloses vectorization can be utilized to convert the text into a numerical vector. The embeddings model generates a high-dimensional vector that represents the semantic meaning of the request in a numerical form, for example, as a request vector.) and performing semantic searching for the second vector in the one or more databases of vectors (¶ 0096-0097, 0112, 0126: discloses conducting a similarity search in a vector store 108. A cosine similarity search is used to compare the vector with the vectors of vector store 108)
The Mordovtsev reference does not explicitly disclose the following limitations. However, Rathod discloses:
generating a second recommended classification for a second incident (¶ 0022, 0024, 0029-0030: discloses categorization apparatus 102 uses clusters of related words to generate incident categories 114 to which the incident tickets can be assigned.),
wherein the semantic searching for the second vector comprises sequential semantic searching (¶ 0011-0012) that involves (i) a first semantic searching relative to vectors relating to automated pre-programmed scripts (¶ 0019: discloses initial filtering of the tickets, "removing types of words and/or inflections" which represents semantic searching relative to vectors relating to automated pre-programmed scripts), (ii) a second semantic searching relative to vectors relating to known major incidents if the first semantic searching fails to satisfy a similarity threshold (¶ 0025-0026), and
(iii) a third semantic searching relative to vectors relating to technical working groups if the second semantic searching fails to satisfy the similarity threshold (¶ 0025-0026), and wherein the second recommended classification is generated based on which of the first semantic searching, the second semantic searching, or the third semantic searching satisfies the similarity threshold (¶ 0025-0026: discloses a comparison of the incident ticket, with multiple categories, and then ranking the categories....i.e., during the "matching", Rathod determines that the incident ticket, if not matching a first category, calculating a distance to a second/third/fourth/fifth category...,the distance-among-categories, is effectively, a threshold measurement -- the "threshold" being, 'a closest distance value', compared to the other distance calculations); and
causing an action to be performed in relation to the second incident based on the second recommended classification. (¶ 0016-0017, 0026-0027: discloses may assign each incident ticket to an agent and/or group of agents with experience and/or expertise in handling issues described in the incident ticket )
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the system and methods of Mordovtsev, to include the features for sequential semantic searching, as disclosed by Rathod to achieve the claimed invention. As disclosed by the Rathod reference, the motivation for the combination would have been for improving the accuracy and efficiency with which incident tickets are routed and handled. (¶ 0011-0012)
The combination of Mordovtsev and Rathod does not explicitly disclose the following limitations.
However, Bhat discloses:
based on the load balancing, and via a second AI model instance of the plurality of AI model instances (¶ 0063, 0075, 0078, 0264: discloses the architecture can implement load balancing across multiple instances of a single model, where one of multiple instances of a model is chosen based on a queue depth of each instance. Each level can be configured to perform a certain task or characterize a certain aspect of the query, and can produce a classification and a confidence score. The classification can correspond to one or more AI agents)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mordovtsev and Rathod to include the load balancing techniques, as disclosed by Bhat to achieve the claimed invention. As disclosed by Bhat, the motivation for the combination would have been to leverage the instances to improve efficiency of resource use and prevent bottlenecks (¶ 0078, 0264)
With respect to claim 4, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 3,
wherein the second recommended classification comprises a classification of the second incident as belonging to a known major incident. (¶ 0016, 0026: Rathod discloses a subset of incident categories 114 with highest match scores. The issue may include, but is not limited to, a software bug, a disruption in service, an outage, a crash, an authentication issue, a hardware issue, and/or another problem related to access to or use of projects 126, hardware 128, software 130, and/or other components of enterprise system 118.)
With respect to claim 5, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 4, wherein the action comprises associating the second incident with the known major incident. (¶ 0016, 0026: Rathod discloses a subset of incident categories 114 with highest match scores. The issue may include, but is not limited to, a software bug, a disruption in service, an outage, a crash, an authentication issue, a hardware issue, and/or another problem related to access to or use of projects 126, hardware 128, software 130, and/or other components of enterprise system 118.)
With respect to claim 6, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 3, wherein the second recommended classification comprises a classification of the second incident as corresponding to a technical working group. (¶ 0017, 0027: Rathod discloses generates routings 144 of incident tickets to agents and/or groups of agents according to incident categories 114 assigned to the incident tickets)
With respect to claim 7, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 6, wherein the action comprises routing data regarding the second incident to the technical working group. (¶ 0017, 0027: Rathod discloses may assign each incident ticket to an agent and/or group of agents with experience and/or expertise in handling issues described in the incident ticket )
With respect to claims 8 and 19, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1,
wherein the one or more databases of vectors comprise vectors relating to automated pre-programmed scripts for resolving incidents. (¶ 0010, 0076: Mordovtsev discloses vector store 108 comprises one or more storage repositories, such as a database, logical disk space, file, or other suitable storage medium configured to store data output by AI models 114. For example, vector store 108 can comprise a database (such as those described with respect to script library 106). In an embodiment, vector store 108 is configured to store remediation scripts. In an embodiment, vector store 108 is configured to descriptions associated with given remediation scripts. In an embodiment, vector store 108 is configured to store vectors associated with given descriptions of remediation scripts.)
With respect to claim 9, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1, wherein the one or more databases of vectors comprise vectors relating to known major incidents. (¶ 0016, 0020, 0026: Rathod discloses words that share common contexts in filtered incident tickets 136 may be closer to one another in the vector space of embeddings 140 than words that are used in different contexts within filtered incident tickets 136. The issue may include, but is not limited to, a software bug, a disruption in service, an outage, a crash, an authentication issue, a hardware issue, and/or another problem related to access to or use of projects 126, hardware 128, software 130, and/or other components of enterprise system 118.)
With respect to claim 10, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1, wherein the one or more databases of vectors comprise vectors relating to technical working groups. (¶ 0016, 0020, 0026-0027: Rathod discloses words that share common contexts in filtered incident tickets 136 may be closer to one another in the vector space of embeddings 140 than words that are used in different contexts within filtered incident tickets 136. Management apparatus 110 may assign each incident ticket to an agent and/or group of agents with experience and/or expertise in handling issues described in the incident ticket. Management apparatus 110 may also update incident repository 134 and/or another data store with the assignment of the ticket to the agent(s).)
With respect to claim 11, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1, wherein the operations further comprise, according to load balancing across the plurality of AI model instances (¶ 0078, 0264: Bhat discloses the architecture can implement load balancing across multiple instances of a single model, where one of multiple instances of a model is chosen based on a queue depth of each instance.), providing the description to the first AI model instance to generate the recommended classification. (¶ 0063, 0075: Bhat discloses each level can be configured to perform a certain task or characterize a certain aspect of the query, and can produce a classification and a confidence score. The classification can correspond to one or more AI agents.)
With respect to claim 12, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1,
wherein the plurality of AI model instances is trained based on results of statistical exception analysis of incident data relating to a plurality of entities that includes the entity. (¶ 0021, 0024, 0030: Mordovtsev discloses training engine 112 is configured to train AI models 114 using existing remediation script data. The existing remediation script data is related to a cybersecurity incident. Each script can be annotated with metadata or comments that explain its purpose, parameters, and expected outcomes. This helps the models learn not just the scripting syntax but also the semantic purpose behind different scripts.)
With respect to claim 13, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 12,
wherein the statistical exception analysis includes identifying trends or outlier data points in the incident data. (¶ 0021, 0024, 0030: Mordovtsev discloses using existing remediation script data. The existing remediation script data is related to a cybersecurity incident. Each script can be annotated with metadata or comments that explain its purpose, parameters, and expected outcomes.)
With respect to claim 14, the combination of Mordovtsev, Rathod, Bhat, Shen discloses the system of claim 1,
wherein the obtaining is responsive in real-time to a received report of the incident. (¶ 0006: Mordovtsev discloses remediation scripts can be generated in real-time without user involvement. A security monitoring system can identify a specific vulnerability. The security monitoring system can communicate a request including a description of the script purpose, or a system environment such as operating system type, an incident type, a username, or a vulnerability code (e.g. MITRE) or to a script generation service. The script generation service searches the vector database for existing remediation scripts, and if the script exists in the repository, it is tailored for the user environment.)
With respect to claim 15, Mordovtsev discloses
a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor (¶ 0021-0022: discloses computing device 102) of a system (¶ 0021: discloses system 100) that includes a plurality of environments each configured with a respective prediction service (¶ 0024: discloses one or more AI models), facilitate performance of operations, the operations comprising:
training an artificial intelligence (AI) model using results of statistical exception analysis of incident data relating to a plurality of entities (¶ 0024: discloses training engine 112 is configured to train or retrain the one or more AI models 114. In an embodiment, training engine 112 is configured to train AI models 114 using existing remediation script data. The training data can include scripts for system administration tasks (user management, disk cleanup, system monitoring), scripts for network management (firewall configuration, port scanning, network diagnostics), or scripts for security purposes (log analysis, malware removal, encryption tasks). Each script can be annotated with metadata or comments that explain its purpose, parameters, and expected outcomes.),
wherein the training of the AI model involves fine tuning of a transformer-based pre-trained model that understands natural language (¶ 0031: discloses each of the AI models can be trained on the same data. AI models comprises an LLM model and an embeddings model which are bonded and trained on the same data.),
wherein the fine tuning is based on text in the incident data (¶ 0030: discloses training data can include metadata or comments that explain its purpose, parameters, and expected outcomes.), and
wherein generating of a recommended classification for a first incident report involves converting a description of the first incident report into a vector (¶ 0095, 0111, 0121: discloses vectorization can be utilized to convert the text into a numerical vector. The embeddings model generates a high-dimensional vector that represents the semantic meaning of the request in numerical form, for example as a request vector. LLM embeddings model 212 is utilized to convert the request into one or more embeddings.) and
performing semantic searching for the vector in one or more databases of vectors (¶ 0096-0097, 0111-0112: discloses the request vector can be used in a similarity search. Receive details about the request and search in vector store 108.); and
based on the recommended classification, causing an automated pre-programmed script to be deployed to a user device (¶ 0098: discloses provide tailored script to computing device 102. For example, communicates a script generated by or tailored by AI model 114 to a user of computing device.),
wherein deployment of the automated pre-programmed script triggers the user device to execute the automated pre-programmed script for incident resolution. (¶ 0098: discloses automatically by computing device 102 can apply the script to the affected system, i.e., computing device 102. For example, a script generated by or tailored by AI model 114 can be executed on or in coordination with computing device 102)
The Mordovtsev reference does not explicitly disclose the following limitations. In the same field of endeavor, the Rathod reference is related to a system for classifying and routing incident tickets (abstract) and teaches:
wherein the AI model is trained to generate recommended classifications of incidents associated with one or more of the plurality of entities (¶ 0034: discloses incident categories 114 may be defined and/or assigned to incident tickets using an artificial neural network, support vector model, or any other type of machine learning or modeling technique.), wherein the plurality of entities comprise products or services that are offered by an organization to a plurality of users (¶ 0010, 0016: discloses enterprises are commonly supported by IT service management systems that allow users to file incident tickets related to IT service issues.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system and methods of Mordovtsev, to include wherein the AI model is trained to generate recommended classifications of incidents associated with one or more of the plurality of entities; wherein the plurality of entities comprise products or services that are offered by an organization to a plurality of users, as disclosed by Rathod to achieve the claimed invention. As disclosed by Rathod, the motivation for the combination would have been to improve the accuracy and efficiency with which incident tickets are routed and handled. (¶ 0012)
The combination of Mordovtsev and Rathod does not explicitly disclose the following limitations. However, Bhat is related to systems and methods that orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical model cascade to classify queries into agent domains (abstract) and teaches:
causing a respective instance of the AI model to be deployed with each respective prediction service to facilitate automatic classification or resolution of reported incidents, resulting in a plurality of instances of the AI model (¶ 0061, 0063-0064);
wherein the at least one load metric comprises a queue depth of the incident queue (¶ 0073);
performing load balancing to distribute the incident reports in the incident queue across a remainder of the plurality of environments (¶ 0078, 0264: discloses the architecture can implement load balancing across multiple instances of a single model, where one of multiple instances of a model is chosen based on a queue depth of each instance.);
based on the load balancing, and via a remainder of the plurality of instances of the AI model in the remainder of the plurality of environments, generating recommended classifications for the incident reports (¶ 0063, 0075: discloses each level can be configured to perform a certain task or characterize a certain aspect of the query, and can produce a classification and a confidence score. The classification can correspond to one or more AI agents.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mordovtsev and Rathod to include the load balancing techniques, as disclosed by Bhat to achieve the claimed invention. As disclosed by Bhat, the motivation for the combination would have been to leverage the instances to improve efficiency of resource use and prevent bottlenecks (¶ 0078, 0264)
The combination of Mordovtsev, Rathod, and Bhat does not explicitly disclose the following limitations.
The Shen reference is related to dynamic allocation and deallocation of computing resources in a computing cluster (¶ 0014)
determining, based on at least one load metric associated with an incident queue that stores incident reports, whether to scale down computational resource usage of the system (¶ 0014, 0024: discloses determining an action including a scale down action based on the application performance metric.);
responsive to a determination to scale down the computational resource usage based on the at least one load metric, causing at least one environment of the plurality of environments to be deactivated such that the respective prediction service configured in the at least one environment is not used to process incident reports while a load threshold continues to be satisfied; (¶ 0014, 0047-0048: discloses multiple instances of a software application may be executed in a computing cluster, and when a performance metric associated with the software application violates a redetermined threshold value, an action, such as a scale-up action or a scale-down action, may be performed. A scale down action indicates that one or more working virtual application instances should be deactivated)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mordovtsev, Rathod, and Bhat to include the scaling techniques, as disclosed by Shen to achieve the claimed invention. As disclosed by Shen, the motivation for the combination would have been to facilitate automatically and dynamically scaling the quantity of web application instances according to the observed or measured workload and distributing workloads to all web servers because dynamic scaling may conserve energy, resource consumption, and cost. (¶ 0016)
With respect to claim 16, the combination of Mordovtsev, Rathod, Bhat, and Shen discloses the non-transitory machine-readable medium of claim 15,
wherein generating of a second recommended classification for a second incident report comprises semantic searching that involves a conversion of a description associated with the second incident report into a second vector (¶ 0088, 0095: Mordovtsev discloses the request can be converted to a request vector to be used in searching vector store 108.), and a comparison of the second vector with other vectors in the one or more databases of vectors. (¶ 0076-0078, 0097-0098: Mordovtsev discloses the similarity search compares the angle between two vectors, i.e., the request vector and the vectors from the vector store.)
With respect to claim 17, the combination of Mordovtsev, Rathod, Bhat, and Shen discloses the non-transitory machine-readable medium of claim 16,
wherein the semantic searching (¶ 0011-0012 – see Rathod) for the second vector comprises sequential semantic searching that involves (i) a first semantic searching relative to vectors relating to automated pre-programmed scripts (¶ 0019: discloses initial filtering of the tickets, "removing types of words and/or inflections" which represents semantic searching relative to vectors relating to automated pre-programmed scripts), (ii) a second semantic searching relative to vectors relating to known major incidents if the first semantic searching fails to satisfy a similarity threshold (¶ 0025-0026 – see Rathod), and (iii) a third semantic searching relative to vectors relating to technical working groups if the second semantic searching fails to satisfy the similarity threshold. (¶ 0025-0026 – see Rathod)
With respect to claim 20, the combination of Mordovtsev, Rathod, Bhat, and Shen the method of claim 19,
wherein the predicting involves predictions of classifications involve sequential semantic searching (¶ 0011-0012) such that searching relative to the vectors relating to technical working groups is performed after a known major incident is unable to be identified from searching relative to the vectors relating to known major incidents (¶ 0025-0026), and the searching relative to the vectors relating to known major incidents is performed (¶ 0025-0026) after an automated pre-programmed script is unable to be identified from searching relative to the vectors relating to automated pre-programmed scripts.(¶ 0012, 0031 – see Rathod)
Response to Arguments
Applicant’s arguments filed 05/08/2026 have been considered and found unpersuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “The Office characterizes the claims as being directed to "performing incident classification tasks for an entity," and alleges that the claims encompass mental processes and/or certain methods of organizing human activity. As amended, claim 1 is not merely directed to "incident classification." Rather, amended claim 1 recites a specific distributed computing control and execution pipeline, including, among other features: storing the description in an incident queue in association with an incident report; determining, based on at least one load metric associated with the incident queue whether to scale down computational resource usage of the system, wherein the at least one load metric comprises a queue depth of the incident queue; responsive to a determination to scale down causing at least one environment of the plurality of environments to be deactivated such that a corresponding prediction service is not used to process incident reports while a load threshold continues to be satisfied; and performing load balancing to distribute incident reports across a remainder of the plurality of environments. A recommended classification is generated via semantic vectorization and semantic searching, and automated remediation is performed by deploying a pre-programmed script to the user device, where deployment triggers execution to resolve the incident. These recitations cannot practically be performed in the human mind or with pen and paper, at least because they require (i) an incident queue whose queue depth is measured, (ii) selective deactivation of one of multiple environments based on the queue depth, and (iii) load balancing across a remainder of environments in response to such deactivation. This is a concrete distributed-systems workflow, and not a mental process.” The Examiner respectfully disagrees.
Applicant’s arguments are not persuasive. After consideration of the remarks, It is important for Applicant to note that an abstract idea may be described at different levels of abstraction. In other words, Applicant’s statements made reformulate or narrow certain steps recited within the abstract idea, however, the combination of limitations do not make the claimed invention any less abstract or alter the analysis at Step 2A Prong One. Relying on features such as a specific distributed computing control and execution pipeline does not preclude the claim limitations from being within the mental processes grouping of abstract ideas. The claimed invention describes a help-desk system that is used to handle incidents more quickly. [See Spec, 0001-0005]
For example, section MPEP 2106.04(a)(2)(III)(c) explains that claims can recite a mental processes even if they are claimed as being performed on a computer. Here, the claimed invention performs a series of mental processes, i.e., tasks, according to one or more rules in a computer environment, to help a user resolve or classify an incident or route the incident to a technical group . See Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360, FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The inability for the human mind to perform each claim step does not alone confer patentability. As such, these limitations for “determining a load metric”, “responsive to the determination scaling down resources”, and “performing load balancing” are mental processes that can be performed by a human, using `observation, evaluation, judgment, and opinion,' because they involve making determinations and decisions, which are mental and manual tasks humans managing incident reports for an entity routinely do. For these reasons, the rejections under 101 are being maintained.
Applicant argues “Even assuming arguendo that the Office could identify some alleged abstract idea (which Applicant does not concede), amended claim 1 nevertheless integrates any such alleged abstract idea into a practical application. In particular, the claim is not limited to "classifying" and outputting information. Rather, the claim requires a resource-optimizing distributed architecture that, responsive to queue depth, scales down computational resource usage by deactivating at least one environment such that its prediction service is not used to process incident reports while a load threshold continues to be satisfied, and then load balances across the remainder. This is exactly the type of practical application that the USPTO's eligibility examples recognize as integrating an information-processing concept into a technical mechanism that improves computer/network operation.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The remarks focus upon features recited within the abstract idea which cannot be relied upon alone to provide the improvement. The Examiner maintains under Step 2A-Prong Two, the additional elements of “a system, comprising:”, “a plurality of environments that respectively include a prediction service configured to utilize a corresponding artificial intelligence (AI) model instance of a plurality of AI model instances”, “at least one processor;”, “a memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising:”, “from a user device”, “an incident queue”, “the system”, “at least one environment of the plurality of environments”, “a corresponding prediction service in the at least one environment”, “across a remainder of the plurality of environments;“, “one or more databases of vectors” as recited in claim 1 are generic computers and AI components. See Spec, [i.e., Figs. 1, 4, ¶ 0027-0031] In light of the Specification, each of the additional elements computing components are recited at a high-level of generality and operate in their expected manner.
Accordingly, when considered individually and in combination, they do not improve the functioning of a computer, another technology, or a technical field. They do not apply the abstract idea to effect a particular treatment or a prophylaxis for a disease or a medical condition. They do not implement the abstract idea on a particular machine or a manufacture that is integral to the claim. They do not reduce or transform a particular article to a different state or thing. Nor do they apply the abstract idea beyond linking it to a technological environment. The remarks point to improvements for computer/network operation using result-based functional language without technological details of an advance. The Examiner asserts the claim language reflected in the claim determines the focus of the claims for eligibility. At best, the remarks discuss features related a resource-optimizing distributed architecture at a high-level of generality and are merely an attempt to limit the claimed invention to a particular technological environment or field of use. See MPEP 2106.05(h) At best if there are any improvements they are recited within the abstract idea itself and not the generic computing or AI components being used to aid in performing the abstract idea. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “For example, Example 40 (Adaptive Monitoring of Network Traffic Data), used in conjunction with the Office's 2014 Interim Eligibility Guidance (IEG), finds eligibility where a comparison-to-threshold is integrated into a network- control scheme that reduces overhead and improves network performance by controlling when additional data is collected. Here, the queue-depth threshold is likewise integrated into a concrete control scheme that reduces computing/power usage by controlling when environments remain active. The present disclosure expressly describes this technical advantage in at least paragraph [0021], which explains that the system dynamically adjusts to varying incident volumes "by scaling down its computational resource usage, such as by reducing active processing units or reallocating resources to other tasks," thereby reducing computing and power resource usage especially at off-peak times. Accordingly, claim 1 is not directed to a judicial exception.“ The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The Examiner asserts MPEP 2106.07 discusses examination guidance, training, and explanatory examples discuss the substantive law and establish the policies and procedures to be followed by examiners in evaluating patent applications for compliance with the substantive law, but do not serve as a basis for a rejection. Accordingly, while it would be acceptable for applicants to cite training materials or examples in support of an argument for finding eligibility in an appropriate factual situation, applicants should not be required to model their claims or responses after the training materials or examples to attain eligibility. The evaluation of whether the claimed invention qualifies as patent-eligible subject matter should be made on a claim-by-claim basis, because claims do not automatically rise or fall with similar claims in an application.
Here, the remarks directed towards the cited training example are insufficient as they do not show how the claimed invention has been integrated into a practical application. The Specification in [¶ 0021] discusses benefits for performing the abstract idea and does not provide a finding that supports technological improvements to computer or network functionality. Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). For these reasons, the rejections under 101 are being maintained.
Applicant further argues “At a minimum, the additional elements of claim 1, as an ordered combination, amount to significantly more than any alleged abstract idea. The ordered combination requires:1. incident queue storage of incident reports;
2. queue-depth-based determination whether to scale down computational resource usage; 3. deactivation of at least one environment such that a corresponding prediction service is not used while a load threshold continues to be satisfied; 4. load balancing across a remainder of environments; 5. prediction-service execution of AI model instance for vectorization and semantic search; and 6. automated script deployment and device-side execution to resolve the incident. This is not a generic "apply it on a computer" implementation. Rather, it is a specific distributed processing pipeline that constrains where and how computation occurs (environment deactivation; remainder-only routing) and when compute resources are consumed (queue-depth threshold). Such a coordinated architecture is the type of ordered combination that supplies an inventive concept. For at least the reasons above, independent claim 1 and dependent claims 2-14 recite statutory subject matter and are in compliance with 35 U.S.C. § 101. Notice to this effect is respectfully requested.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. These conclusory allegations regarding the alleged “specific distributed processing pipeline” and “coordinated architecture” are insufficient to demonstrate an inventive concept. The remarks use generic functional language to achieve these purported solutions. At best, these features are an attempt to limit the claimed invention to a particular technological environment or field of use, but do not add significantly more or anything meaningful to the claimed invention. The underlined remarks mainly focus on where, how, and when the incident classification tasks are supposed to be performed, however, the ordered combination of features of the abstract idea alone cannot be used to provide the inventive concept.
The Applicant’s Specification’s description [i.e., Figs. 1, 4, ¶ 0027-0031] evidences the generic nature of the computing components. See Weisner v. Google LLC, 51 F.4th 1073, 1083-84(Fed. Cir. 2022) (a generic description of components supported a finding they are conventional and not inventive) Two-Way Media, 874 F.3d at 1339 (claim 1 used only generic functional language to achieve the purported solutions for excessive loads on a source server, network congestion, variations in delivery times, scalability of networks, and lack of precise recordkeeping) Thus, the ordered combination recites no more than the individual elements do. See Alice, 573 U.S. at 225 (using a computer to create and maintain accounts, adjust account balances, and issued automated instructions is well understood, routine, and conventional) Elec. Power Grp., 830 F.3d at 1355 (finding no inventive concept where the claims required only off-the-shelf conventional computer, network, and display technology used for gathering, sending, and presenting desired information). Accordingly, the claimed invention is directed to an abstract idea and does not provide an inventive concept.
Applicant further argues “Applicant respectfully submits that the Office has not established, with the requisite specificity, that each of amended independent claims 15 and 18, when considered as a whole, is "directed to" an abstract idea under Step 2A of the Mayo/Alice framework. In particular, the Office has not identified a judicial exception that corresponds to the claims' specific computer- implemented workflow that includes: (i) storing incident reports in an incident queue, (ii) determining, based on a queue-depth load metric associated with the incident queue, whether to scale down computational resource usage, (iii) responsive to a low-load determination, deactivating at least one environment such that a corresponding prediction service is not used to process incident reports while a load threshold continues to be satisfied, (iv) performing load balancing to distribute incident reports across a remainder of the plurality of environments, (v) generating recommended classifications via AI model instances deployed with the prediction services using semantic vector conversion and semantic searching in one or more vector databases, and (vi) automatically causing deployment and execution of an automated pre- programmed script on a user device for incident resolution. Furthermore, amended claims 15 and 18 are not directed to a mental process. Rather, amended claims 15 and 18 are necessarily rooted in computer technology and integrate any alleged abstract idea into a practical application
by providing a specific, system-executed distributed pipeline that (a) dynamically reduces computational resource usage by deactivating one or more processing environments based on incident-queue load conditions and (b) performs real-time semantic-vector-based incident classification and automated device-side remediation, for reasons similar to those explained above with respect to amended claim 1. At a minimum, amended claims 15 and 18 recite an ordered combination of limitations that amounts to significantly more than any alleged judicial exception. Accordingly, Applicant respectfully requests withdrawal of the § 101 rejections of claims 15 and 18. It is also submitted that dependent claims 16, 17, 19, and 20 recite statutory subject matter and are also in compliance with 35 U.S.C. § 101. Notice to this effect is also respectfully requested.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In the instant case, claims 15 and 18 recite subject matter substantially similar to claim 1, and therefore are being held ineligible under the same grounds. As for the dependent claims 16, 17, 19, and 20, the Applicant’s remarks are insufficient as they do not explain how these limitations impart subject matter eligibility. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments with respect to claim(s) 1, 15, and 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Edwards (US 2025/0173359 A1)
Cameron (US 12,135,949 B1)
Cai (US 2019/0121853 A1)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 EHRIN PRATT whose telephone number is (571)270-3184. The examiner can normally be reached 8-5 EST Monday-Friday.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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