DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 31, 2026 has been entered.
Response to Amendment
Applicant’s Amendments, filed July 7, 2026, have been entered. No claims have been amended, and claims 1-19 are currently pending.
Response to Arguments
Applicant’s arguments, filed July 7, 2026, with respect to the Double Patenting rejections are persuasive, in light of the Terminal Disclaimer being filed and approved. The nonstatutory double patenting rejection of claims 1, 7, 8, 10, 16, 17 and 19 is withdrawn.
Applicant's arguments filed, filed July 7, 2026, with respect to the 35 U.S.C. 103 rejections of claims 1-19 have been fully considered but they are not persuasive.
Applicant argues that the cited prior art does not account for the integrated arrangement recited in independent claims 1, 10, and 19 and sets forth several arguments. The first argument set forth is that Tan et al. (Pub. No. US 2024/0303711 A1, hereinafter “Tan”) does not supply the claimed isolated telematics-query architecture, specifically arguing that Tan does not disclose an isolated LLM that generates a telematics-database query while lacking access to the telematics database, or responding to a telematics-data request without providing the LLM access to both the telematics data and the context database (Remarks pp. 9-10). In response, examiner respectfully submits that Xu et al. (Pub. No. US 2024/0095460 A1, hereinafter “Xu”) is cited as teaching “generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query…” (see Final Rejection, dated April 8, 2026, p. 13, “Final Rejection”). Also, Nia and Xu are cited as teaching “whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database”, with Xu cited as teaching telematics data stored on the at least one telematics database (see Final Rejection p. 14, 18-20).
The second argument set forth is that Nia et al. (Pub. No. US 2025/0094787 A1, hereinafter “Nia”) does not isolate the model from the data stores used to obtain the information in the first place because Nia’s model accesses the hierarchical vector store and uses retrieved context to generate the response (Remarks p. 11). In response, examiner respectfully submits that the last limitation recites “…without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database”, and using the broadest reasonable interpretation examiner interprets that the LLM does not have access to certain telematics data, rather than does not have access to the at least one telematics database and the at least one context database.
The third argument set forth is that the Office Action has not identified where the combined references teach or suggest the specific claimed division of functionality in the independent claims (Remarks p. 11). In response, examiner respectfully submits that MPEP 2141 III. provides that “the prior art reference (or references when combined) need not teach or suggest all the claim limitations…in determining obviousness, neither the particular motivation to make the claimed invention nor the problem the inventor is solving controls.”
The fourth argument set forth is that the motivation to combine the references does not provide the articulated reasoning for modifying Xu and Tan into an architecture in which the LLM is affirmatively isolated from both the telematics database and the context database while another component executes the LLM-generated query against the telematics database (Remarks p. 11). In response, examiner respectfully directs Applicant to MPEP 2141 III., as done above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-12, 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Tan further in view of Nia.
Regarding claim 1, Xu teaches:
at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database (Xu – the vehicle may further include the infotainment SoC which may include a combination of hardware and software that may be used to provide audio, video, phone, network connectivity, and/or information services (e.g. navigation systems, vehicle related information such as fuel level, brake fuel level, oil level, i.e. telematics) to the vehicle, and may include a telematics device [0176]. The vehicle may further include data stores (see Fig. 9c, 928) [0147]. Retrieval component may use one or more techniques to retrieve, from the information database(s) 112, contextual information that is associated with the text data (see Fig. 1, 112 information database, which may store information associated with the vehicle and Fig. 1, 116 contextual data) [0047-0048].)
and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database (Xu – in Fig. 7, B702, text data is obtained representing a question associated with a vehicle. For instance, the vehicle may use one or more microphones to generate audio data representing speech from a passenger of the vehicle (i.e. natural language request). The vehicle may then process the audio data, using the speech-processing component, in order to generate the text data representing the speech, which may include a question associated with the vehicle [0068].)
generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM (Xu – block B706 may include inputting the text data and data representing the one or more question/answer pairs into a language model [0070]. The language model may include a large language model [0029].)
one or more features of the at least one telematics database (Xu - the vehicle may further include the infotainment SoC which may include a combination of hardware and software that may be used to provide audio, video, phone, network connectivity, and/or information services (e.g. navigation systems, vehicle related information such as fuel level, brake fuel level, oil level, i.e. telematics) to the vehicle, and may include a telematics device [0176]. The vehicle may further include data stores (see Fig. 9c, 928) [0147]. Retrieval component may use one or more techniques to retrieve, from the information database(s) 112, contextual information that is associated with the text data (see Fig. 1, 112 information database, which may store information associated with the vehicle and Fig. 1, 116 contextual data) [0047-0048].)
telematics data from the at least one telematics database (Xu - the vehicle may further include the infotainment SoC which may include a combination of hardware and software that may be used to provide audio, video, phone, network connectivity, and/or information services (e.g. navigation systems, vehicle related information such as fuel level, brake fuel level, oil level, i.e. telematics) to the vehicle, and may include a telematics device [0176]. The vehicle may further include data stores (see Fig. 9c, 928) [0147]. Retrieval component may use one or more techniques to retrieve, from the information database(s) 112, contextual information that is associated with the text data (see Fig. 1, 112 information database, which may store information associated with the vehicle and Fig. 1, 116 contextual data) [0047-0048].)
and return at least the portion of the telematics data to the user, (Xu – the output data may represent information associated with the question. The vehicle may then provide the information to the passenger [0071].)
telematics data stored on the at least one telematics database (Xu - the vehicle may further include the infotainment SoC which may include a combination of hardware and software that may be used to provide audio, video, phone, network connectivity, and/or information services (e.g. navigation systems, vehicle related information such as fuel level, brake fuel level, oil level, i.e. telematics) to the vehicle, and may include a telematics device [0176]. The vehicle may further include data stores (see Fig. 9c, 928) [0147]. Retrieval component may use one or more techniques to retrieve, from the information database(s) 112, contextual information that is associated with the text data (see Fig. 1, 112 information database, which may store information associated with the vehicle and Fig. 1, 116 contextual data) [0047-0048].)
Xu does not appear to teach:
identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one [telematics] database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request
and inputting the portion of contextual information into the LLM
execute the executable query for retrieving the portion of the [telematics] data from the at least one [telematics] database
whereby the natural language request is responded to without providing the LLM with access to the [telematics] data stored on the at least one [telematics] database and the at least one context database
However, Tan teaches:
identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one [telematics] database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request (Tan – in Fig. 3, 300, the system receives a natural language query from a client device of the user [0054]. In Fig. 3, 310 contextual information associated with the natural language query is determined, which includes information based on information obtained in one or more previous interactions of the user with the online system [0056-0058]. The data store 240 stores data used by the online system, including user data, item data, order data and trained machine learning models, and may use databases to organize the stored data [0051]. Training data is stored in the data store 240 [0052], and includes natural language questions concatenated with contextual data describing users further concatenated with lists of items, item types, or categories of items associated with the natural language question [0044].)
and inputting the portion of contextual information into the LLM (Tan – at Fig. 3, 320, the system generates a prompt for input to a machine learning based language model based on the natural language query and the contextual information, and at Fig. 3, 330, the prompt to the system provides the prompt to the machine learning based language model for execution [0061]. The response may be a parse-able string of text that the system may further analyze to identify individual attributes or may include a set of attributes including specific details of the types of items that the user may be interested in [0061]. At Fig. 3, 350, the system further generates a search query based on the set of attributes [0063].)
execute the executable query for retrieving the portion of the [telematics] data from the at least one [telematics] database (Tan – at Fig. 3, 350, the system further generates a search query based on the set of attributes. The search query may be a query for searching for items in a database. By searching through a database, the system is able to identify specific items. At Fig. 3, 360, the systems sends the search query for execution. For example, the search query may be executed using the database or the search engine [0063].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu and Tan before them, to modify the system of Xu of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, one or more features of the at least one telematics database with the teachings of Tan of identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one [telematics] database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the [telematics] data from the at least one [telematics] database. One would have been motivated to make such a modification to respond to search queries with more relevant responses and provide a good user experience (Tan - [0003]).
Xu modified by Tan does not appear to teach:
whereby the natural language request is responded to without providing the LLM with access to the [telematics] data stored on the at least one [telematics] database and the at least one context database
However, Nia teaches:
whereby the natural language request is responded to without providing the LLM with access to the [telematics] data stored on the at least one [telematics] database and the at least one context database (Nia – the hierarchical vector store may be protected by fine-grained access control rules, and may include multiple individual stores. Each vector store may have different access control rules for accessing the embeddings therein. A user may access data in a particular vector store if the user’s access privileges meet the access control rules for that vector store [0016]. The machine learning model may access the hierarchical vector store to generate responses to prompts received from users. The machine learning model may be an LLM. The retrieval is based on the prompt and is subject to any relevant access control rules that should be applied to the user [0034]. The machine learning model may generate responses to prompts from users using context retrieved from the hierarchical vector store [0032].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu and Tan of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database with the teachings of Nia of whereby the natural language request is responded to without providing the LLM with access to the [telematics] data stored on the at least one [telematics] database and the at least one context database. One would have been motivated to make such a modification to share knowledge while protecting private, proprietary and sensitive data (Nia - [0006]).
Claims 10 and 19 correspond to claim 1 and are rejected accordingly.
Regarding claim 2, Xu modified by Tan does not appear to teach:
wherein the context database is a vector database and the contextual information stored therein is represented by a plurality of vectors
However, Nia teaches:
wherein the context database is a vector database and the contextual information stored therein is represented by a plurality of vectors (Nia – the hierarchical vector store may be protected by fine-grained access control rules, and may include multiple individual stores. Each vector store may have different access control rules for accessing the embeddings therein. A user may access data in a particular vector store if the user’s access privileges meet the access control rules for that vector store [0016]. The machine learning model may access the hierarchical vector store to generate responses to prompts received from users. The machine learning model may be an LLM. The retrieval is based on the prompt and is subject to any relevant access control rules that should be applied to the user [0034]. The machine learning model may generate responses to prompts from users using context retrieved from the hierarchical vector store [0032].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database with the teachings of Nia of wherein the context database is a vector database and the contextual information stored therein is represented by a plurality of vectors. One would have been motivated to make such a modification to share knowledge while protecting private, proprietary and sensitive data (Nia - [0006]).
Claim 11 corresponds to claim 2 and are rejected accordingly.
Regarding claim 3, Xu modified by Tan does not appear to teach:
wherein the at least one processor is operable to identify the portion of contextual information useful for generating the executable query from the at least one context database based on the parsing of the natural language request by: generating a vector representation of the parsing of the natural language request by the LLM; identifying one or more vectors of the plurality of vectors of the vector database that are similar to the vector representation of the parsing of the natural language request by the LLM; and selecting the contextual information represented by the one or more vectors that are similar to the vector representation for input into the LLM
However, Nia teaches:
wherein the at least one processor is operable to identify the portion of contextual information useful for generating the executable query from the at least one context database based on the parsing of the natural language request by: generating a vector representation of the parsing of the natural language request by the LLM; identifying one or more vectors of the plurality of vectors of the vector database that are similar to the vector representation of the parsing of the natural language request by the LLM; and selecting the contextual information represented by the one or more vectors that are similar to the vector representation for input into the LLM (Nia – in Fig. 2, 203, the machine learning model receives a prompt which may be a natural language prompt. In Fig. 2, 206, the machine learning model queries the hierarchical data store for information related to the prompt. As one example, the machine learning model may encode the prompt into a query vector in a same vector space as the embeddings stored in the hierarchical vector store (i.e. context database). In Fig. 2, 209, the machine learning model generates a response to the prompt [0050-0052].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database, wherein the context database is a vector database and the contextual information stored therein is represented by a plurality of vectors with the teachings of Nia of wherein the at least one processor is operable to identify the portion of contextual information useful for generating the executable query from the at least one context database based on the parsing of the natural language request by: generating a vector representation of the parsing of the natural language request by the LLM; identifying one or more vectors of the plurality of vectors of the vector database that are similar to the vector representation of the parsing of the natural language request by the LLM; and selecting the contextual information represented by the one or more vectors that are similar to the vector representation for input into the LLM. One would have been motivated to make such a modification to share knowledge while protecting private, proprietary and sensitive data (Nia - [0006]).
Claim 12 corresponds to claim 3 and are rejected accordingly.
Regarding claim 5, Xu does not appear to teach:
wherein the at least one data storage is further operable to store at least one chat database, the at least one chat database storing each natural language request received from the user
However, Tan teaches:
wherein the at least one data storage is further operable to store at least one chat database, the at least one chat database storing each natural language request received from the user (Tan – in Fig. 3, 300, the system receives a natural language query from a client device of the user [0054]. In Fig. 3, 310 contextual information associated with the natural language query is determined, which includes information based on information obtained in one or more previous interactions of the user with the online system [0056-0058]. The data store 240 stores data used by the online system, including user data, item data, order data and trained machine learning models, and may use databases to organize the stored data [0051]. Training data is stored in the data store 240 [0052], and includes natural language questions (i.e. chat database) concatenated with contextual data describing users further concatenated with lists of items, item types, or categories of items associated with the natural language question [0044].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database with the teachings of Tan of wherein the at least one data storage is further operable to store at least one chat database, the at least one chat database storing each natural language request received from the user. One would have been motivated to make such a modification to respond to search queries with more relevant responses and provide a good user experience (Tan - [0003]).
Claim 14 corresponds to claim 5 and are rejected accordingly.
Regarding claim 6, Xu does not appear to teach:
wherein the at least one processor is further operable to identify additional contextual information based on one or more previous natural language requests received from the user
However, Tan teaches:
wherein the at least one processor is further operable to identify additional contextual information based on one or more previous natural language requests received from the user (Tan - in Fig. 3, 310 contextual information associated with the natural language query is determined, which includes information based on information obtained in one or more previous interactions of the user with the online system [0056-0058]. The data store 240 stores data used by the online system, including user data, item data, order data and trained machine learning models, and may use databases to organize the stored data [0051]. Training data is stored in the data store 240 [0052], and includes natural language questions concatenated with contextual data describing users further concatenated with lists of items, item types, or categories of items associated with the natural language question [0044].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database, wherein the at least one data storage is further operable to store at least one chat database, the at least one chat database storing each natural language request received from the user with the teachings of Tan of wherein the at least one processor is further operable to identify additional contextual information based on one or more previous natural language requests received from the user. One would have been motivated to make such a modification to respond to search queries with more relevant responses and provide a good user experience (Tan - [0003]).
Claim 15 corresponds to claim 6 and are rejected accordingly.
Regarding claim 7, Xu does not appear to teach:
wherein the at least one processor is further operable to modify the executable query based on database identifying information, a type of the portion of the telematics data that is responsive to the natural language request, an identity of the user, or a combination thereof
However, Tan teaches:
wherein the at least one processor is further operable to modify the executable query based on database identifying information, a type of the portion of the telematics data that is responsive to the natural language request, an identity of the user, or a combination thereof (Tan – the system determines contextual information associated with the natural language query. The system may determine contextual information that allows the system to identify specific details of the items that the user should purchase. The system may determine contextual information including user profile information of the user (i.e. identity of the user) of the client device [0056].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database with the teachings of Tan of wherein the at least one processor is further operable to modify the executable query based on database identifying information, a type of the portion of the telematics data that is responsive to the natural language request, an identity of the user, or a combination thereof. One would have been motivated to make such a modification to respond to search queries with more relevant responses and provide a good user experience (Tan - [0003]).
Claim 16 corresponds to claim 7 and are rejected accordingly.
Regarding claim 8, Xu does not appear to teach:
wherein the at least one processor is further operable to revert modifying of the executable query after the execution thereof
However, Tan teaches:
wherein the at least one processor is further operable to revert modifying of the executable query after the execution thereof (Tan – the user data also may include default settings established by the user, such as a default retailer/retailer location, delivery location, or delivery timeframe (i.e. to revert) [0035].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database, wherein the at least one processor is further operable to modify the executable query based on database identifying information, a type of the portion of the telematics data that is responsive to the natural language request, an identity of the user, or a combination thereof with the teachings of Tan of wherein the at least one processor is further operable to revert modifying of the executable query after the execution thereof. One would have been motivated to make such a modification to respond to search queries with more relevant responses and provide a good user experience (Tan - [0003]).
Claim 17 corresponds to claim 8 and are rejected accordingly.
Regarding claim 9, Xu does not appear to teach:
wherein the at least one processor is further operable to send the natural language request and the executable query that is responsive thereto to the at least one data storage for storage in the context database
However, Tan teaches:
wherein the at least one processor is further operable to send the natural language request and the executable query that is responsive thereto to the at least one data storage for storage in the context database (Tan – in Fig. 3, 300, the system receives a natural language query from a client device of the user [0054]. In Fig. 3, 310 contextual information associated with the natural language query is determined, which includes information based on information obtained in one or more previous interactions of the user with the online system [0056-0058]. The data store 240 stores data used by the online system, including user data, item data, order data and trained machine learning models, and may use databases to organize the stored data [0051]. Training data is stored in the data store 240 [0052], and includes natural language questions (i.e. chat database) concatenated with contextual data describing users further concatenated with lists of items, item types, or categories of items associated with the natural language question [0044].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan and Nia before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database with the teachings of Tan of wherein the at least one processor is further operable to send the natural language request and the executable query that is responsive thereto to the at least one data storage for storage in the context database. One would have been motivated to make such a modification to respond to search queries with more relevant responses and provide a good user experience (Tan - [0003]).
Claim 18 corresponds to claim 9 and are rejected accordingly.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Tan further in view of Nia further in view of Madnani (Pub. No. US 2025/0045314 A1, hereinafter “Madnani”).
Regarding claim 4, Xu modified by Tan and Nia does not appear to teach:
wherein the at least one processor is operable to identify the one or more vectors that are similar to the vector representation based on the real distance between the vector representation and each of the plurality of vectors of the vector database, based on an angle between the vector representation and the plurality of vectors of the vector database, or a combination thereof
However, Madnani teaches:
wherein the at least one processor is operable to identify the one or more vectors that are similar to the vector representation based on the real distance between the vector representation and each of the plurality of vectors of the vector database, based on an angle between the vector representation and the plurality of vectors of the vector database, or a combination thereof (Madnani – based on the context, the embeddings store (i.e. vector database) finds similar embeddings. The embeddings store may then sort the similar embeddings by relevance. The embeddings store may then create a contextual prompt from the closest embedding. Here, the closest embeddings is the embedding with the shortest distance for similar queries [0029].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Xu, Tan, Nia and Madnani before them, to modify the system of Xu, Tan and Nia of at least one data storage operable to store at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database, and at least one processor in communication with the at least one data storage, the at least one processor operable to: receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within the at least one telematics database, generate, using an isolated large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database by: inputting the natural language request into the LLM, identifying, based on a parsing of the natural language request by the LLM, a portion of the contextual information useful for generating the executable query from the at least one context database, the portion of the contextual information comprising one or more features of the at least one telematics database and one or more example natural language requests and corresponding executable query outputs that are relevant to the natural language request, and inputting the portion of contextual information into the LLM, execute the executable query for retrieving the portion of the telematics data from the at least one telematics database, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database with the teachings of Madnani of wherein the at least one processor is operable to identify the one or more vectors that are similar to the vector representation based on the real distance between the vector representation and each of the plurality of vectors of the vector database, based on an angle between the vector representation and the plurality of vectors of the vector database, or a combination thereof. One would have been motivated to make such a modification to understand the context and intent behind a user’s query (Tan - [0003]).
Claim 13 corresponds to claim 4 and are rejected accordingly.
Conclusion
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/RANJIT P DORAISWAMY/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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