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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/31/2024 is considered by the examiner.
Drawings
The drawing submitted on 07/31/2024 is considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims1, 11, and 20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s), receiving, at a device, a query from a user for input to a large language model; matching, by the device, a pattern associated with the query with one or more prior chat interactions between the large language model and one or more other users; generating, by the device, an adjusted query based on the query and the one or more prior chat interactions; and providing, by the device, an answer to the adjusted query from the large language model to the user”, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting "device" and "large language model" nothing in the claim element precludes the step from practically being formed in the mind. For example, but for the recitation "device" and "large language model" language, "receive", "matching" “generating” and "providing" in the context of this claims encompasses a person verbally receiving from a second person an initial request to provide a direction for an address. However, upon recalling past conversation with other people where the person provided the other people with a response associated with an entity address and direction to a request for the entity address where the entity’s address is same as the address of second person’s request. The person then provide the second person with the entity address and direction based on recalling the address from past conversation.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, in claim the claim 1, recitation of –“receiving at a device a query…”, matching by the device…”, generating by the device…”, and “providing by the device…”, are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components or merely uses a computer as a tool (i.e. generic computer receiving a request, then selecting a request from history database, similar to the request and then providing an answer with the selected request).Therefore the limitation “by the device”, is generally apply the abstract idea without limitation specifying how the device function to achieve the results. The limitation of “large language model” does not provide any meaningful limitation beyond generally linking the use of judicial exception to a field of use or technology environment (neural network).
Similarly in Claim 11, the recitation of processor, memory, network interfaces for processing, storing and retrieving information of the recited steps are, are well-understood, routine, and conventional activity.
Similarly in Claim 20, the recitation of non-transitory computer-readable media and device are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components or merely uses a computer as a tool (i.e. generic computer receiving a request, then selecting a request from history database, similar to the request and then providing an answer with the selected request). The limitation of “large language model” similarly does not provide any meaningful limitation beyond generally linking the use of judicial exception to a field of use or technology environment (neural network).
Accordingly, this additional elements does not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 1, 11, and 20, do not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements device, non-transitory computer-readable media, amounts to no more than mere instructions to apply the exception using a generic computer component.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP § 2106.05 (f)).
The use of processor, memory and network interface to achieve the results is well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, not enough to qualify as "significantly more" (see MPEP § 2106.05(d))
The use of “large language model” does not provide any meaningful limitation beyond generally linking the use of judicial exception to a field of use or technology environment (neural network). Generally linking the use of the judicial exception to a particular technological environment or field of use not to be enough to qualify as "significantly more" (see MPEP § 2106.05(h)).
When considered in combination, these additional elements represent mere instruction to apply an exception, well-understood, routine, conventional activities and linking the use of the judicial exception to a particular technological environment or field, which cannot provide an inventive concept. Therefore, the claims 1, 11, and 20 are not patent eligible.
With respect to Claims 2 and 12, limitation " wherein the one or more prior chat interactions include at least one follow up query to an answer provided by the large language model to the one or more other users " similarly, other than reciting "large language model" nothing in the claim element precludes the step from practically being formed in the mind. For example, the person’s past conversation with other people where the person provided the other people with a response associated with an entity address and direction to a request for the entity address, included follow-up query prior to providing associated with an entity address and direction. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 3 and 13, limitation " wherein the device generates the adjusted query based further in part on one or more prior chat interactions between the user and the large language model” similarly, other than reciting “device” and "large language model" nothing in the claim element precludes the step from practically being formed in the mind. For example, a person verbally receiving from a second person an initial request to provide a direction for an address. However, upon recalling past conversation with other people where the person provided the other people with a response associated with an entity address and direction to a request for the entity address where the entity’s address is same as the address of second person’s request. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 4 and 14, limitation “wherein the device matches the query to the one or more prior chat interactions based on their semantic similarity”, similarly, other than reciting “device” nothing in the claim element precludes the step from practically being formed in the mind. For example, a person verbally receiving from a second person an initial request to provide a direction for an address. However, upon recalling past conversation with other people where the person provided the other people with a response associated with an entity address and direction to a request for the entity address where the entity’s address is same as the address of second person’s request. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 5 and 15, limitation “wherein the device generates the adjusted query in part by merging the query with another query in the one or more prior chat interactions” similarly, other than reciting “device” nothing in the claim element precludes the step from practically being formed in the mind. For example, a person verbally receiving from a second person an initial request to provide a direction for an address. However, upon recalling past conversation with other people where the person provided the other people with a response associated with an entity address and direction to a request for the entity address where the entity’s address is same as the address of second person’s request. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 6 and 16, limitation “wherein the device generates the adjusted query based in part on a success metric associated with the one or more prior chat interactions” similarly, other than reciting “device” nothing in the claim element precludes the step from practically being formed in the mind. For example, the person having the knowledge of being successful on the past conversation query and response to the direction and address for the entity, provide the other person the direction for the same address. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 7 and 17, limitation, “wherein the success metric is computed based on a count of follow up queries in the one or more prior chat interactions”, similarly, nothing in the claim element precludes the step from practically being formed in the mind. For example, the person having the knowledge of being successful was based on the other person finding the address without further clarification question or query of being lost again. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 8 and 18, limitation, “maintaining, by the device, an interactions registry that includes the one or more prior chat interactions”, similarly, other than reciting “device” nothing in the claim element precludes the step from practically being formed in the mind. For example, the person having a sharp memory can remember the past conversations and queries from other people. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claims 9 and 19, limitation, “wherein the query requests information regarding a computer network” similarly, nothing in the claim element precludes the step from practically being formed in the mind. For example, a person verbally receiving from a second person an initial request to help setup his GPS for a direction of an address since he could not find the address in his GPS. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to Claim 10, limitation, ”wherein the query requests information regarding a particular networking entity in the computer network”, similarly, nothing in the claim element precludes the step from practically being formed in the mind. For example, a person verbally receiving from a second person an initial request to help setup his GPS for a direction of an address since he could not find the address in his GPS. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it fall with the "Mental Process" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claims 2-10, and 12-19, do not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements device, non-transitory computer-readable media, amounts to no more than mere instructions to apply the exception using a generic computer component.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP § 2106.05 (f)).
The use of processor, memory and network interface to achieve the results is well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, not enough to qualify as "significantly more" (see MPEP § 2106.05(d))
The use of “large language model” does not provide any meaningful limitation beyond generally linking the use of judicial exception to a field of use or technology environment (neural network). Generally linking the use of the judicial exception to a particular technological environment or field of use not to be enough to qualify as "significantly more" (see MPEP § 2106.05(h)).
When considered in combination, these additional elements represent mere instruction to apply an exception, well-understood, routine, conventional activities and linking the use of the judicial exception to a particular technological environment or field, which cannot provide an inventive concept. Therefore, the claims 2-10 and 12-19, are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s)1, 4-6, 8, 11, 14-16, 18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Almaer et al.(US 2024/0362209 A1).
Regarding Claim 1, Almaer et al. teach: A method comprising (Abstract: The method includes: receiving a request for retrieval of data satisfying one or more criteria, the request including at least one data request parameter; searching a database storing example queries based on the request to identify at least one matching query; providing, to a large language model (LLM), an input prompt to generate a query purporting to retrieve data satisfying the one or more criteria, the input prompt including the at least one data request parameter and the at least one matching query as an example; and receiving, from the LLM, a result including the generated query.): receiving, at a device, a query from a user for input to a large language model ([0024] The computing system includes a processor and a memory coupled to the processor. The memory stores computer-executable instructions that, when executed by the processor, may cause the processor to: receive a request for retrieval of data satisfying one or more criteria, the request including at least one data request parameter; search a database storing example queries based on the request to identify at least one matching query; provide, to a large language model (LLM), an input prompt to generate a query purporting to retrieve data satisfying the one or more criteria…); matching, by the device, a pattern associated with the query(matching query) with one or more prior chat interactions (stored queries of the queries database) between the large language model and one or more other users (first user request may be compared with one or more user requests that are identified in the queries database) ([0090] The computing system searches the queries database based on the request to identify at least one matching query, in operation 204. In at least some implementations, the computing system may perform comparisons between the first user request and the user requests corresponding to the stored queries of the queries database to determine a closest match, or “matching” user request. That is, the first user request may be compared with one or more user requests that are identified in the queries database. A query which corresponds to a matching user request may be determined to a matching query. [0092] In some implementations, the computing system may implement a text similarity algorithm which may be used for measuring a degree to which the first user request is semantically related to each user request of the queries database.); generating, by the device, an adjusted query (modifying an input prompt to the LLM) based on the query and the one or more prior chat interactions (a previous query that was accepted by the endpoint) ([0035] The system may match a user request (e.g., a data retrieval request) to a “best” prompt template, out of a set of such templates, for an LLM. A prompt template may, for example, comprise a previous query that was accepted by the endpoint or an example of a properly constructed query for the endpoint. The matched template may then be provided in an input prompt to the LLM with instructions to generate a query for submitting to the endpoint. [0039] The retrieved previous query is provided, along with the first data request, as input to the LLM. In particular, the first data request and the retrieved previous query may be included as part of an input prompt to the LLM, with instructions for the LLM to generate a query for the endpoint. [0041] This process of instructing the LLM to generate a query corresponding to the first data request based on modifying an input prompt to the LLM may proceed iteratively until a successful response is received from the endpoint. [0066] ChatGPT is built on top of a GPT-type LLM, and has been fine-tuned with training datasets based on text-based chats (e.g., chatbot conversations). ChatGPT is designed for processing natural language, receiving chat-like inputs and generating chat-like outputs. ); and providing, by the device, an answer (response) to the adjusted query from the large language model to the user ( [0093] For example, the LLM may be instructed explicitly to use the at least one matching query as an example when generating the new query corresponding to the first user request. [0094] In operation 208, the computing system receives, from the LLM, a result including the generated query. The result may indicate information about the generated query, such as the query language, data fields, arguments, etc. The generated query may be provided to the user device as a response to the first user request. That is, the computing system may output the generated query responsive to receiving the first user request via the user device. [0101] In operation 312, the computing system receives, from the LLM, a result including the generated query. The result may indicate information about the generated query, such as the query language, data fields, arguments, etc. The generated query may be provided to the user device as a response to the first user request.).
Regarding Claim 4, Almaer et al. teach: The method as in claim 1, wherein the device matches the query to the one or more prior chat interactions based on their semantic similarity (See rejection of claim 1 and [0092] In some implementations, the computing system may implement a text similarity algorithm which may be used for measuring a degree to which the first user request is semantically related to each user request of the queries database.).
Regarding Claim 5, Almaer et al. teach: The method as in claim 1, wherein the device generates the adjusted query in part by merging the query with another query in the one or more prior chat interactions (See rejection of claim 1 and [0039] The retrieved previous query is provided, along with the first data request, as input to the LLM. In particular, the first data request and the retrieved previous query may be included as part of an input prompt to the LLM, with instructions for the LLM to generate a query for the endpoint.).
Regarding Claim 6, Almaer et al. teach: The method as in claim 1, wherein the device generates the adjusted query based in part on a success metric (a previous query that was accepted by the endpoint) associated with the one or more prior chat interactions (See rejection of claim 1 and ([0035] The system may match a user request (e.g., a data retrieval request) to a “best” prompt template, out of a set of such templates, for an LLM. A prompt template may, for example, comprise a previous query that was accepted by the endpoint or an example of a properly constructed query for the endpoint. The matched template may then be provided in an input prompt to the LLM with instructions to generate a query for submitting to the endpoint. [0036] When a user provides a first data request (expressed using natural language) for an endpoint, the system is configured to instruct an LLM to generate a query for the endpoint, i.e., by converting the first data request to a corresponding query. In at least some implementations, the system generates a first embedding of the first data request in a relevant embedding space. The embedding space may comprise embeddings associated with all or a subset (e.g., only correctly formed queries) of previous data requests to the endpoint. [0038] More generally, the system identifies an embedding that matches (e.g., nearest neighbor or otherwise closest to) the first embedding, and retrieves a previous query (in the specified query language) that is associated with the identified embedding. In at least some implementations, the system may only search embeddings associated with previous queries that are known to have invoked a successful response from the endpoint. That is, the system may identify the closest one of the embeddings associated with correctly formed queries for the endpoint.).
Regarding Claim 8, Almaer et al. teach: The method as in claim 1, further comprising: maintaining, by the device, an interactions registry (a knowledge base i.e. Queries database) that includes the one or more prior chat interactions (See rejection of claim 1 and [0041] Upon determining a successful query, i.e., a query that is accepted by the endpoint, the system may update a knowledge base, such as a queries database, storing query information of queries for the endpoint.).
Regarding Claim 11, Almaer et al. teach: An apparatus, comprising: one or more network interfaces (LLM); a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor, the process when executed configured to ([0023] In another aspect, the present application discloses a computing system. The computing system includes a processor and a memory coupled to the processor. The memory stores computer-executable instructions that, when executed by the processor, may cause the processor to: receive a request for retrieval of data satisfying one or more criteria, the request including at least one data request parameter; search a database storing example queries based on the request to identify at least one matching query; provide, to a large language model (LLM), an input prompt to generate a query purporting to retrieve data satisfying the one or more criteria, the input prompt including the at least one data request parameter and the at least one matching query as an example; and receive, from the LLM, a result including the generated query.): receive a query from a user for input to a large language model; match a pattern associated with the query with one or more prior chat interactions between the large language model and one or more other users; generate an adjusted query based on the query and the one or more prior chat interactions; and provide an answer to the adjusted query from the large language model to the user (See rejection of claim 1).
Regarding Claim 14, Almaer et al. teach: The apparatus as in claim 11, wherein the apparatus matches the query to the one or more prior chat interactions based on their semantic similarity (Same rejection of claim 4 would be applied).
Regarding Claim 15, Almaer et al. teach: The apparatus as in claim 11, wherein the apparatus generates the adjusted query in part by merging the query with another query in the one or more prior chat interactions(Same rejection of claim 5 would be applied).
Regarding Claim 16, Almaer et al. teach: The apparatus as in claim 11, wherein the apparatus generates the adjusted query based in part on a success metric associated with the one or more prior chat interactions(Same rejection of claim 6 would be applied).
Regarding Claim 18, Almaer et al. teach: The apparatus as in claim 11, wherein the process when executed is further configured to: maintain an interactions registry(a knowledge base i.e. Queries database) that includes the one or more prior chat interactions(Same rejection of claim 8 would be applied).
Regarding Claim 20, Almaer et al. teach: A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising([0023] In another aspect, the present application discloses a computing system. The computing system includes a processor and a memory coupled to the processor. The memory stores computer-executable instructions that, when executed by the processor, may cause the processor to: receive a request for retrieval of data satisfying one or more criteria, the request including at least one data request parameter; search a database storing example queries based on the request to identify at least one matching query; provide, to a large language model (LLM), an input prompt to generate a query purporting to retrieve data satisfying the one or more criteria, the input prompt including the at least one data request parameter and the at least one matching query as an example; and receive, from the LLM, a result including the generated query.): receiving, at the device, a query from a user for input to a large language model; matching, by the device, a pattern associated with the query with one or more prior chat exchanges between the large language model and one or more other users; generating, by the device, an adjusted query based on the query and the one or more prior chat exchanges; and providing, by the device, an answer to the adjusted query from the large language model to the user (See rejection of claim 1).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2 and 12, are rejected under 35 U.S.C. 103 as being unpatentable over Almaer et al. in view of Andrew et al.(WO2026/020009 A1) .
Regarding Claims 2 and 12, Almaer et al. teach: generating, by the device, an adjusted query (modifying an input prompt to the LLM) based on the query and the one or more prior chat interactions (a previous query that was accepted by the endpoint) ([0035] The system may match a user request (e.g., a data retrieval request) to a “best” prompt template, out of a set of such templates, for an LLM. A prompt template may, for example, comprise a previous query that was accepted by the endpoint or an example of a properly constructed query for the endpoint. The matched template may then be provided in an input prompt to the LLM with instructions to generate a query for submitting to the endpoint. [0039] The retrieved previous query is provided, along with the first data request, as input to the LLM. In particular, the first data request and the retrieved previous query may be included as part of an input prompt to the LLM, with instructions for the LLM to generate a query for the endpoint. [0041] This process of instructing the LLM to generate a query corresponding to the first data request based on modifying an input prompt to the LLM may proceed iteratively until a successful response is received from the endpoint. [0066] ChatGPT is built on top of a GPT-type LLM, and has been fine-tuned with training datasets based on text-based chats (e.g., chatbot conversations). ChatGPT is designed for processing natural language, receiving chat-like inputs and generating chat-like outputs. )
Almaer et al.do not teach: wherein the one or more prior chat interactions include at least one follow up query to an answer provided by the large language model to the one or more other users.
Andrew et al. teach: wherein the one or more prior chat interactions include at least one follow up query (parameter clarification exchanges) to an answer provided by the large language model to the one or more other users ([0081] For example, the chat history database 312 may be configured to capture and store a complete or partial trace of one or more user interactions, including user prompts, the LLM’s outer loop responses and/or inner loop function executions, parameter clarification exchanges, function calls, script executions, and validation outcomes…).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filling date of the invention was made for Almaer et al. to include the teaching of Andrew et al. above in order to enable contextual continuity for multi-turn conversations, allowing users to refer back to prior answers or build on earlier queries without restarting the interaction).
Claim(s) 3 and 13, are rejected under 35 U.S.C. 103 as being unpatentable over Almaer et al. in view of Trinh et al.(US 2025/0390492 A1)
Regarding Claims 3 and 13, Almaer et al. teach: The method as in claim 1, wherein the device generates the adjusted query based further in part on one or more prior chat interactions between other users and the large language model (See rejection of claim 1).
Almaer et al. do not teach: The method as in claim 1, wherein the device generates the adjusted query based further in part on one or more prior chat interactions between the user and the large language model.
Trinh et al, teach: wherein the device generates the adjusted query based further in part on one or more prior chat interactions between the user and the large language model ([0035] In some examples, each user question 202 may be stored in the query data store 120 (from a past (or prior) or current session) and labeled with the user. In some embodiments, the agent assistant system 104 summarizes, classifies, and compares each user question 202. For example, each user question 202 may be compared against past questions asked by the user to determine patterns, interests, or behaviors of the user. The agent assistant system 104 may determine a similarity of each user question 202 with past questions stored in the query data store 120. In some embodiments, the agent assistant system 104 utilizes or accesses a model to determine a similarity (e.g., semantic similarity, cosine distance or cosine similarity based on a vector derived from a new question 202 and one or more vectors derived from previous question(s), etc.) between a new question 202 and a previous question or a previous set of questions. Each question may be associated with a similarity score, or may be stored in the query data store 120 within a vector space or other embedding space.[0038] In some embodiments, the agent assistant system 104 accesses similar or related queries that have already been generated and stored (e.g., where two queries can each be converted into vectors based on the strings therein and may be similar if the vectors are within a threshold distance of each other). The agent assistant system 104 may utilize similar queries in generating a prompt for input into the LLM(s) for generation of an answer to the user question 202. [0040] In response to the generation of an answer based on the query, the agent assistant system 104 may output or transmit the answer. The agent assistant system 104 may output the answer to the user via the frontend 116, such as in a chat interface.).
Therefore, it would have been obvious one of ordinary skilled in the art before the effective filling date of the invention was made for Almaer et al. to include the teaching of Trinh et al. above in order to compare each user question against past questions asked by the user to determine patterns, interests, or behaviors of the user.
Claim(s) 9-10 and 19, are rejected under 35 U.S.C. 103 as being unpatentable over Almaer et al. in view of Surya et al.(WO2025188559 A1)
Regarding Claims 9 and 19, Almaer et al. do not teach: The method as in claim 1, wherein the query requests information regarding a computer network.
Surya et al. teach: a query requests information regarding a computer network ([0001] Currently, for operations management of network-based services, users create multiple dashboards for each operational monitoring tool. These dashboards present metrics associated with functionalities of a given network-based service or device, as generated by a given operational monitoring tool. In case of issues such as outages, users can generate relevant dashboards in the dashboard lists and/or directories and look at key performance metrics to determine the root-cause of the issue. [0002] In some network-management systems, free-form or natural language text originating from end-user devices can be analyzed, e.g., using large language models (LLMs), to identify which dashboards are requested by any given user.).
Therefore, it would have obvious to one of ordinary skilled in the art before the effective filling date of the invention was made for Almear et al. to include the teaching of Surya et al. above in order to in case of issues such as outages, users can generate relevant dashboards in the dashboard lists and/or directories and look at key performance metrics to determine the root-cause of the issue.
Regarding Claim 10, Almaer et al. do not teach: The method as in claim 9, wherein the query requests information regarding a particular networking entity in the computer network.
Surya et al. teach: a query requests information regarding a particular networking entity in the computer network ([0021] The metrics generator 130, in an implementation, is configured to generate the performance data for display in real-time. The performance data can facilitate a user of a given user device 110 (such as an IT administrator), to root-cause an issue with a given service 104 and troubleshoot the issue, without the requirement of an additional monitoring tools or applications on the user device 110. In an implementation, an end-user can simply send plain text messages (e.g., using an instant messaging application) from their user device 110, to request for performance data for a service 104. Based on the received messages, the metrics generator 130 can generate the performance data in real- time, e.g., in the form of dashboards, to provide important insights regarding performance of the service 104. [0051] In one implementation, the databases 320 store annotated data specific to various entities, such that the data for any given entity is used to train the NER model 308 to generate entity-specific database queries associated. In one example, each entity (e.g., customer) can have different domain or entity-specific terminologies or keywords for the same parameter, e.g., “installation site” may be simply referred to as “site” by a given entity and “location” by some other entities.).
Therefore, it would have obvious to one of ordinary skilled in the art before the effective filling date of the invention was made for Almear et al. to include the teaching of Surya et al. above in order to in case of issues such as outages, users can generate entity-specific database queries associated relevant dashboards in the dashboard lists and/or directories and look at key performance metrics to determine the root-cause of the issue.
Allowable Subject Matter
Applicant has to overcome the 101 rejection in order for the below claims to be considered for allowable subject matter.
Claims 7 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art of record Porras et al. (US 10050868 B2) teach: “Multimodal Help Agent For Network Administrator” (Network management technology as disclosed herein generates and dynamically updates an intuitive, interactive visualization of a computer network in live operation. The network management technology interprets human user interactions, such as gestures, conversational natural language dialog, and combinations of gestures and natural language dialog, as network directives. The technology can implement the network directives to, for example, facilitate analysis of network activity or to respond to network security events).
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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2653
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