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 .
Response to Amendment
The Amendment filed 04/23/2026 has been entered. Claims 1-20 remain pending in this application.
Response to Arguments
Applicant’s arguments filed on 04/23/2026 have been fully considered but are not persuasive.
With respect to the 35 U.S.C. 101 abstract idea rejection, on pages 1-10, the Applicant asserts that none of the co-based claims are an observation, evaluation, judgement, or opinion that can be performed in the human mind. They also reference the 2019 Guidance, stating that a claim falls under the “mental process” category only if the claim can be “performed by humans without a computer.” They list amended claim 1 as an example by reciting “one or more language models” that “generate, via one or more attention layers, each word or token of an enriched query”. They state that generating tokens via attention layers requires execution of a trained machine learning architecture that performs numerical operations over vector representations and therefore are not analogous to any mental step performed by a human. The Applicant asserts that the claims do not merely recite generating text, but requires that the enriched query is generated token-by-token via attention layers of the language model, which a human cannot replicate mentally. The Applicant further asserts that the amended limitation of using an “optimization function” similarly cannot be performed mentally. They further assert that the claims recite a specific interaction between the aforementioned components: the language model generates an enriched query using attention-based token generation, and that enriched query is then used as structured input to an optimization function that computes a route. They assert that this reflects a specific technical workflow that improves how mapping systems process ambiguous or incomplete natural language requests. They state that the claims do not recite steps that can be performed mentally or with pen and paper. The Applicant further asserts that the claims improve both computer retrieval operations and existing technologies such as mapping technologies, and they reference Specification paragraphs [0020]-[0025] to discuss the problems for existing technologies, as well as Specification paragraphs [0033]-[0037] to discuss the improvement over existing mapping technologies and computer retrieval operations.
The Examiner respectfully disagrees. The original claims, and the claims as amended, are merely utilizing computer devices, in this specific case “one or more language models”, “one or more attention layers”, and “an optimization function”, as tools to perform a method which is directed to an abstract idea. The claim, under its broadest reasonable interpretation, recites a system and method for receiving, analyzing, extracting, inputting, modifying, and displaying data (e.g., a natural language question or command, contextual data, user preference). This is an abstract idea in the form of certain methods of organizing human activity (i.e. mental processes such as observation, evaluation, judgement, and opinion) as well as mathematical formulations. The steps of receiving a question or command from a user, extracting data from that question or command that corresponds to a user preference, generating words that include part of the question or command and part of the user preference, using those generated words to compute a travel route, and presenting that travel route could be performed by a human using pen and paper or by purely mental reasoning, save for the recitation of generic computing components. Further, the claims do not integrate the judicial exception into a practical application. The recitation of “one or more language models”, “one or more attention layers”, and “an optimization function” are generic instructions to perform the abstract idea on a computer or using computer devices and does not impose a meaningful limit on the judicial exception. The “one or more language models”, “one or more attention layers”, and “an optimization function” are recited as such high-levels of generality and are merely used as tools to perform the abstract idea faster or more efficiently. With respect to Specification paragraphs [0033]-[0037], which the Applicant asserts provides improvement over existing mapping technologies and computer retrieval operations, while the claims do not need to explicitly recite the improvements shown in the Specification, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvements. Based on the plain reading of the claim itself, there is no reasonable improvement to the functioning of the data receiving, the data extraction, data modification, the data displaying, the language models themselves, the optimization, or to any other technology or technical field. The claims do not include any additional elements that amount to significantly more than the judicial exception. The claims, as written and amended, do not include more than mere instructions to perform the abstract method using generic computer components. Hence, Applicant’s arguments are not persuasive.
With respect to the 35 U.S.C. 102 rejection, on pages 10-19, of claims 1-7, 9-15, and 17-20 under Zhuang et al. (CN117892828A), hereinafter referred to as Zhuang, the Applicant asserts the Zhuang fails to describe “in response to the receiving of the natural language question or command corresponding to the request by the user, extracting contextual data including a user preference of the user derived from a conversation that occurred prior to the receiving of the natural language question or command…”. They also assert that Zhuang does not disclose “providing the user preference and the natural language question or command as input into the one or more language models, wherein the one or more language models generate, via one or more attention layers, each word or token of an enriched query that includes a first portion of the natural language question or command and a second portion of the natural language question or command that has been replace or supplemented with the user preference”. They also assert that Zhuang does not disclose “based at least in part on the one or more language models generating, via the one or more attention layers, each word or token of the enriched query, providing the enriched query as input into an optimization function, wherein the optimization function computes a route based on the enriched query”. The Applicant asserts that none of the other references cure these deficiencies. The Applicant goes on to assert that each and every single dependent claim is also not taught by either Zhuang nor Mallick et al. (US Patent Application Publication No. 2025/0209281). These are detailed below in the respective 35 U.S.C. 102 and 103 sections.
Concerning the independent claim and Zhuang’s failure to disclose “in response to the receiving of the natural language question or command corresponding to the request by the user, extracting contextual data including a user preference of the user derived from a conversation that occurred prior to the receiving of the natural language question or command…”, Zhuang para [0027] states "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained". Zhuang paragraph [n0030] also states that the history of user interactions is stored, and Zhuang paragraph [0092] illustrates an interaction of requesting a route that is near a coffee shop. These, per Zhuang, show that historical conversation data between the user and the large language model is utilized in order to perceive and fulfill user’s questions or commands, thus teaching the amended claim. In response to Zhuang not disclosing “providing the user preference and the natural language question or command as input into the one or more language models, wherein the one or more language models generate, via one or more attention layers, each word or token of an enriched query that includes a first portion of the natural language question or command and a second portion of the natural language question or command that has been replaced or supplemented with the user preference”, Zhuang para [0092] states “When a user inputs a command, the system forwards it to a large language model (GLAM), such as GLM-4, via the ChatCompletion method,” which teaches inputting the natural language question or command into a language model, and that being processed using one or more attention layers, as GLM-4, as well as most other widely known and used large language models, utilize layers of attention. Zhuang para [n0034] states "In this embodiment, as shown in Figure 2, when the user's needs are not clearly stated or lack key information, the advantages of the language model can be used to guide the user's needs. By continuously asking users questions, leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained”, which shows that the historical conversation can be utilized by the language model, thereby teaching that the user preference be also input into the language model. Language models, including GLM-4, output words or tokens. Also, Zhuang Fig. 2 shows a cycle in which the prompt continues to evolve, i.e., becoming "enriched". In response to Zhuang not disclosing “based at least in part on the one or more language models generating, via the one or more attention layers, each word or token of the enriched query, providing the enriched query as input into an optimization function, wherein the optimization function computes a route based on the enriched query”, Zhuang para [n0039] states "This enables a multi-round function call mechanism to automatically break down tasks when handling complex tasks, flexibly call multiple tools, automatically sense task execution progress and results, optimize the execution process through front-end recursion mechanism, reduce redundant steps and execution costs, and effectively understand user needs through a large language model", and Zhuang para [0092] states "The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks”. This shows that Zhuang teaches an optimized process of computing a route through dialogue between a user and a large language model. As “optimization function” is taught within the claim, it is a very broad terminology that is not specific within the art. Thus, Zhuang discloses this optimization function, as well as computing and displaying a route in this regard. Hence, Applicant’s arguments are not persuasive.
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.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 10, and 18 recite “receiving a natural language question or command corresponding to a request by a user”, “in response to the receiving of the natural language question or command corresponding to the request by a user, extracting contextual data”, “providing the user preference and the natural language question or command as input into [[the]] one or more language models”, “providing the enriched query as input into an optimization function”, and “causing presentation”. These limitations, as drafted, are a process that, under a broadest reasonable interpretation, covers the abstract idea of “mental processes” because they cover concepts performed in the human mind, including observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2). That is, other than reciting “one or more language models”, “one or more attention layers”, and “an optimization function”, nothing in the claimed elements preclude the steps from practically being performed by a person listening to a natural language prompt, taking in contextual data surrounding the natural language prompt, and answering the prompt based on the prompt itself and its context.
This judicial exception is not integrated into a practical application because the additional elements “one or more language models”, “one or more attention layers”, and “an optimization function” are all recited at a high-level of generality. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claims 1, 10, and 18 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical applications, the additional elements of “one or more language models”, “one or more attention layers”, and “an optimization function” amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (Step 2B).
Dependent claims 2-9, 11-17, and 19-20 are directed to the contextual data surrounding the natural language question or command, as well as the natural language question or command itself. That is, nothing in the claimed elements preclude the steps from practically being performed by a person listening to a natural language prompt, taking in contextual data surrounding the natural language prompt, and answering the prompt based on the prompt itself and its context.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-7, 9-15, and 17-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhuang et al. (CN117892828A), hereinafter referred to as Zhuang.
Regarding claim 1, Zhuang discloses a system comprising: at least one computer processor (Zhuang para [0005]);
and one or more computer storage media storing computer-useable instructions that, when used by the at least one computer processor, cause the at least one computer processor to perform operations comprising (Zhuang para [0005]):
receiving a natural language question or command corresponding to a request by a user to obtain geographical information associated with a mapping platform (Zhuang para [0077]);
in response to the receiving of the natural language question or command corresponding to the request by the user, extracting contextual data, the contextual data including a user preference of the user derived from a conversation that occurred prior to the receiving of the natural language question or command ("By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027], Zhuang para [n0030], and Zhuang para [0092]);
providing the user preference and the natural language question or command as input into [[the]] one or more language models, wherein the one or more language models generate, via one or more attention layers (“When a user inputs a command, the system forwards it to a large language model (GLAM), such as GLM-4, via the ChatCompletion method,” Zhuang para [0092], GLM-4, as well as most other widely known and used large language models, utilize layers of attention), each word or token of an enriched query that includes a first portion of the natural language question or command and a second portion of the natural language question or command that has been replaced or supplemented with the user preference (Zhuang para [0010] and [0077] and "In this embodiment, as shown in Figure 2, when the user's needs are not clearly stated or lack key information, the advantages of the language model can be used to guide the user's needs. By continuously asking users questions, leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [n0034] and Zhuang Fig. 2 shows a cycle in which the prompt continues to evolve, i.e., becoming "enriched");
[[and]] based at least in part on the one or more language models generating, via the one or more attention layers (“When a user inputs a command, the system forwards it to a large language model (GLAM), such as GLM-4, via the ChatCompletion method,” Zhuang para [0092], GLM-4, as well as most other widely known and used large language models, utilize layers of attention), each word or token of the enriched query, providing the enriched query as input into an optimization function, wherein the optimization function computes a route based on the enriched query ("This enables a multi-round function call mechanism to automatically break down tasks when handling complex tasks, flexibly call multiple tools, automatically sense task execution progress and results, optimize the execution process through front-end recursion mechanism, reduce redundant steps and execution costs, and effectively understand user needs through a large language model," Zhuang para [n0039] and "The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [0092]);
and causing presentation, at a map interface associated with the mapping platform, of an indication representing the computed route ("The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [n0026]).
Regarding claim 2, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses wherein preference includes at least one of: information from one or more previous turns that are part of a same first conversation as the natural language question or command, or one or more previous natural language questions or commands generated prior to the natural language question or command that are a part of a second conversation (Zhuang para [0077] and "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]).
Regarding claim 3, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses wherein the contextual data includes the one or more spatial [[or]] and temporal constraints within the natural language question or command (Zhuang para [0077]), and wherein the one or more spatial constraints specify a geographic location [[a]] the user asks to navigate to (Zhuang para [0077]), and wherein the one or more temporal constraints include an order or time that the user asks to navigate to the geographical location at ("Taking route planning tasks as an example, users can query using natural language commands such as 'find a route from Plaza A to Shopping Mall B on foot' or 'I want to go from Plaza A to Shopping Mall B by strolling over'," Zhuang para [0077], "strolling over" indicates a sense of leisure, relating temporally).
Regarding claim 4, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses wherein the contextual data includes the context from an output generated by the one or more language models, and wherein the output includes a clarifying question that the one or more language models generate in response to a prior turn in the question or command or a prior question or prior command issued by [[a]] the user before the natural language question or command (Zhuang para [0077] and "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]).
Regarding claim 5, Zhuang discloses all of the limitations of claim 4. Zhuang further discloses wherein the operations further comprise generating, via the language model, a clarifying question, and wherein the operations further comprise: subsequent to the generation of the clarifying question, receiving a second natural language question or command ("By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]);
providing second contextual data as input into the one or more language models, wherein the second contextual data includes the clarifying question, the natural language question or command, and the contextual data, and wherein the one or more language models modify the enriched query based on the clarifying question ("By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]);
and causing presentation, at the map interface associated with the mapping platform, of an indication associated with the modified enriched query ("The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [n0026]).
Regarding claim 6, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses wherein the operations further comprising: detecting an entity associated with the user preference
and based on the detecting, populating a key-value pair data structure with one or more values representing the user preference, and wherein the key of the key-value pair data structure represents a type of the user preference associated with the entity, and wherein the key-value pair data structure is a part of the input into the one or more language models (Zhuang para [n0029]).
Regarding claim 7, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses wherein the natural language question or command includes a command to find [[a]] the route from a first location to a second location and stopping by at least a third location in between the first location and the second location, and wherein the response by the one or more language models includes at least one of ("In another scenario, if the user inputs 'I want to take the bus from shopping mall A to location 1, and then find a coffee shop nearby,' the GLAM can extract the key features 'bus, shopping mall A, location 1, coffee shop.'," Zhuang para [0092]):
a source, a destination, one or more waypoints, a temporal constraint, a travel mode, and one or more optimization objectives, and wherein the ("Based on these key features, the GLAM can generate a "function_call" message containing the function name and parameters. After receiving the feedback from the GLAM, the system parses the function name and parameters from the "function_call" message, finds the corresponding function in the GIS function library, and then executes the function with the provided parameters," Zhuang para [0092]) ("This enables a multi-round function call mechanism to automatically break down tasks when handling complex tasks, flexibly call multiple tools, automatically sense task execution progress and results, optimize the execution process through front-end recursion mechanism, reduce redundant steps and execution costs, and effectively understand user needs through a large language model," Zhuang para [n0039]), and wherein the indication ("The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [0092]).
Regarding claim 9, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses providing the enriched query as second input into the one or more language models, and wherein the one or more language models generate a query ("In this embodiment, as shown in Figure 2, when the user's needs are not clearly stated or lack key information, the advantages of the language model can be used to guide the user's needs. By continuously asking users questions, leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [n0034] and Zhuang Fig. 2 shows a cycle in which the prompt continues to evolve, i.e., becoming "enriched");
and providing the the optimization function ("This enables a multi-round function call mechanism to automatically break down tasks when handling complex tasks, flexibly call multiple tools, automatically sense task execution progress and results, optimize the execution process through front-end recursion mechanism, reduce redundant steps and execution costs, and effectively understand user needs through a large language model," Zhuang para [n0039]), and wherein the optimization function generates another response ("The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [0092]).
Regarding claim 10, Zhuang discloses a computer-implemented method comprising: extracting contextual data based at least in part on receiving a natural language sequence associated with a request by a user to obtain geographical information (Zhuang para [0077] and "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]), the contextual data including a user preference of the user derived from a conversation that occurred prior to the receiving of the natural language question or command, the user preference representing a preferred type of place (Zhuang para [0077] and "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]);
providing the user preference and the natural language question or command as input into one or more language models, wherein the one or more language models generate, via one or more attention layers (“When a user inputs a command, the system forwards it to a large language model (GLAM), such as GLM-4, via the ChatCompletion method,” Zhuang para [0092], GLM-4, as well as most other widely known and used large language models, utilize layers of attention), each word or token of an enriched query that includes a first portion of the natural language sequence and a second portion of the natural language sequence that has been replaced or supplemented with the user preference ("In this embodiment, as shown in Figure 2, when the user's needs are not clearly stated or lack key information, the advantages of the language model can be used to guide the user's needs. By continuously asking users questions, leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [n0034] and Zhuang Fig. 2 shows a cycle in which the prompt continues to evolve, i.e., becoming "enriched");
based at least in part on the one or more language models generating, via the one or more attention layers (“When a user inputs a command, the system forwards it to a large language model (GLAM), such as GLM-4, via the ChatCompletion method,” Zhuang para [0092], GLM-4, as well as most other widely known and used large language models, utilize layers of attention), each word or token of the enriched query, providing the enriched query as input into an optimization function, wherein the optimization function computes a route based on the enriched query ("This enables a multi-round function call mechanism to automatically break down tasks when handling complex tasks, flexibly call multiple tools, automatically sense task execution progress and results, optimize the execution process through front-end recursion mechanism, reduce redundant steps and execution costs, and effectively understand user needs through a large language model," Zhuang para [n0039] and "The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [0092]);
and causing presentation, at a map interface, of an indication representing the computed route ("The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [n0026]).
Regarding claim 11, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses wherein the user preference includes at least one of: information from one or more previous turns that are part of a same first conversation as the natural language sequence, one or more previous natural language sequences generated prior to the natural language sequence that are a part of a second conversation, or user preferences of a user that issued the natural language sequence (Zhuang para [0077] and "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]).
Regarding claim 12, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses wherein the contextual data includes [[the]] one or more spatial or temporal constraints within the natural language sequence (Zhuang para [0077]), and wherein the one or more spatial constraints specify a geographic location [[a]] the user asks to navigate to (Zhuang para [0077]), and wherein the one or more temporal constraints include an order or time that the user asks to navigate to the geographical location at ("Taking route planning tasks as an example, users can query using natural language commands such as 'find a route from Plaza A to Shopping Mall B on foot' or 'I want to go from Plaza A to Shopping Mall B by strolling over'," Zhuang para [0077], "strolling over" indicates a sense of leisure, relating temporally).
Regarding claim 13, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses wherein the contextual data includes the context from an output generated by the one or more language models (Zhuang para [0077]),
and wherein the output includes a clarifying question that the one or more language models generate in response to a prior turn in the natural language sequence or a prior question or prior command issued by [[a]] the user before to the natural language sequence (Zhuang para [0077] and "By continuously asking users questions and leveraging the rich language understanding capabilities and historical message memory of the large language model, explicit function call instructions can be gradually obtained," Zhuang para [0027]).
Regarding claim 14, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses detecting entities in at least one of: a current conversation associated with the natural language sequence, or a historical conversation (Zhuang para [n0029]);
and based on the detecting, populating a key-value pair data structure with one or more values, and wherein the key-value pair data structure is included in the contextual data (Zhuang para [n0029]).
Regarding claim 15, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses wherein the natural language sequence includes a command to find a route from a first location to a second location and stopping by at least a third location in between the first location and the second location, and wherein thegenerates a response that detects at least one of ("In another scenario, if the user inputs 'I want to take the bus from shopping mall A to location 1, and then find a coffee shop nearby,' the GLAM can extract the key features 'bus, shopping mall A, location 1, coffee shop.'," Zhuang para [0092]): a source, a destination, one or more waypoints, a temporal constraint, a travel mode, and one or more optimization objectives ("Based on these key features, the GLAM can generate a "function_call" message containing the function name and parameters. After receiving the feedback from the GLAM, the system parses the function name and parameters from the "function_call" message, finds the corresponding function in the GIS function library, and then executes the function with the provided parameters," Zhuang para [0092]),
and wherein the computer-implemented method further comprising: providing the response as an input into [[an]] the optimization function, and wherein the optimization function computes the route ("This enables a multi-round function call mechanism to automatically break down tasks when handling complex tasks, flexibly call multiple tools, automatically sense task execution progress and results, optimize the execution process through front-end recursion mechanism, reduce redundant steps and execution costs, and effectively understand user needs through a large language model," Zhuang para [n0039]), and wherein the indication associated with the response to the enriched query includes an indicator superimposed over the map interface and that represents the route ("The execution result is transmitted to the front-end map for display and the user is notified, thus completing the first round of tasks," Zhuang para [0092]).
Regarding claim 17, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses wherein the user preference includes at least one of: a cuisine type, a type of restaurant, a lodging type, a retail category, or a point-of-interest category (Zhuang para [0030] and Zhuang para [0077]).
As to claim 18, computer-readable medium (CRM) claim 18 and system claim 1 are related as system and CRM of using same, with each claimed element’s function corresponding to the system step, respectively. Accordingly, claim 18 is similarly rejected under the same rationale as applied above with respect to the system claim.
As to claim 19, CRM claim 19 and system claim 9 are related as system and CRM of using same, with each claimed element’s function corresponding to the system step, respectively. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to the system claim.
As to claim 20, CRM claim 20 and system claim 4 are related as system and CRM of using same, with each claimed element’s function corresponding to the system step, respectively. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to the system claim.
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.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhuang, in view of Mallick et al. (US Patent Application Publication No. 2025/0209281), hereinafter referred to as Mallick.
Regarding claim 8, Zhuang discloses all of the limitations of claim 1. Zhuang further discloses wherein the operations further comprising: prompting or tuning the one or more language models by: a first natural language sequence representing a user question or command (Zhuang para [0077]);
generating, via a model, a second natural language sequence representing a response to the first natural language sequence (Zhuang para [0010] and [0077]).
However, Zhuang fails to disclose generating, via a first model, generating, via a second model, and evaluating, via a third model, a quality of at least one of the first natural language sequence or the second natural language sequence based on one or more criterion, the one or more criterion including at least one of consistence, clearness, conversation flow, conciseness, completeness, closure, correctness, or relevance.
Mallick teaches an adaptive query routing system for processing queries using natural language generators.
Mallick teaches generating, via a first model ("The training includes, for given queries of a set of training queries, generating a vector-space embedding of a given query of the set of training queries. A first response is generated for the given query using the first natural language generator model. A second response is generated for the given query using the second natural language generator model," Mallick para [0126]);
generating, via a second model ("The training includes, for given queries of a set of training queries, generating a vector-space embedding of a given query of the set of training queries. A first response is generated for the given query using the first natural language generator model. A second response is generated for the given query using the second natural language generator model," Mallick para [0126]);
and evaluating, via a third model, a quality of at least one of the first natural language sequence or the second natural language sequence (Mallick para [0127]) based on one or more criterion, the one or more criterion including at least one of consistence, clearness, conversation flow, conciseness, completeness, closure, correctness, or relevance (Mallick para [0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhuang’s disclosure of natural language conversation using language models by including Mallick’s teaching of utilizing multiple language models to generate queries, respond to said queries, and then evaluate the responses based upon a quality metric. This would enable faster, more efficient, and more cost-effective training of the models used for responding to human clients. This would increase efficacy and efficiency of the models once they are employed in helping real clients.
Regarding claim 16, Zhuang discloses all of the limitations of claim 10. Zhuang further discloses prompting or tuning the one or more language models by: a first natural language sequence representing a user question or command (Zhuang para [0077]);
generating, via a model, a second natural language sequence representing a response to the first natural language sequence (Zhuang para [0010] and [0077]).
However, Zhuang fails to disclose generating, via a first model, generating, via a second model, and evaluating, via a third model, a quality of at least one of the first natural language sequence or the second natural language sequence based on one or more criterion, the one or more criterion including at least one of consistence, clearness, conversation flow, conciseness, completeness, closure, correctness, or relevance.
Mallick teaches generating, via a first model ("The training includes, for given queries of a set of training queries, generating a vector-space embedding of a given query of the set of training queries. A first response is generated for the given query using the first natural language generator model. A second response is generated for the given query using the second natural language generator model," Mallick para [0126]);
generating, via a second model ("The training includes, for given queries of a set of training queries, generating a vector-space embedding of a given query of the set of training queries. A first response is generated for the given query using the first natural language generator model. A second response is generated for the given query using the second natural language generator model," Mallick para [0126]);
and evaluating, via a third model, a quality of at least one of the first natural language sequence or the second natural language sequence (Mallick para [0127]) based on one or more criterion, the one or more criterion including at least one of consistence, clearness, conversation flow, conciseness, completeness, closure, correctness, or relevance (Mallick para [0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhuang’s disclosure of natural language conversation using language models by including Mallick’s teaching of utilizing multiple language models to generate queries, respond to said queries, and then evaluate the responses based upon a quality metric. This would enable faster, more efficient, and more cost-effective training of the models used for responding to human clients. This would increase efficacy and efficiency of the models once they are employed in helping real clients.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/ADAM MICHAEL WEAVER/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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