Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/01/2024 is considered by the examiner.
Drawings
The drawing submitted on 07/26/2024 is considered by the examiner.
Examiner Comments On Patent Subject Matter Eligibility under 35 USC § 101
Independent Claim 1, recites a method for facilitating conversational interaction with a user through the user device corresponding receiving a request from the user to perform a task. Although certain steps such as interaction, transmitting, receiving, identifying, and processing could be performed mentally, however the combination of technical steps and specifically generating a machine learning model input based on the user request and information which input to the machine learning model for an output and storing the machine learning model input along with the request, and the response in a storing device, that could not be practically performed as an abstract idea such as a mental process under the broadest reasonable interpretation (BRI). Accordingly, the independent claim 1 and their dependents by virtue of their dependency, are directed towards patent eligible subject matter under step 2A prong 1.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 recites the limitation "the processing device" in line 15. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s)1-20, are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dilipkumar et al.(US 2025/0086213 A1).
Regarding Claim 1, Dilipkumar teach: A method for facilitating conversational interaction with users to help the users, the method comprising ([0016] The client device 110 is a client device through which a user may interact with other client devices 100, or the online system 140. The client device 110 can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or desktop computer. In some embodiments, the client device 110 executes a client application that uses an application programming interface (API) to communicate with the online system 140. ): transmitting, using a communication device (Fig.1, Network 130), a conversational interaction interface (API or chat interface or Fig.1, interface system 160 ) for conversationally interacting with at least one user to at least one user device (a client device or client device 100 or 110) associated with the at least one user ([0015] The client device 100 is a client device through which a user may interact with another client device 110, or the online system 140. [0017] The client devices 100, 110, and the online system 140 can communicate with each other via the network 130. [0038] The online system allows users to ask natural language questions based on information such as order data, customer data, item data, and various combinations of different type of information. [0056] The natural language question may be asked using a user interface, for example, the user interface illustrated in FIG. 4A. [0063] The system may request the machine learned language model to provide a comment describing the query. The system uses the comment to perform a conversation with the user via the user interface. [0068] FIG. 4A shows a screenshot of a user interface for allowing users to specify natural language queries to extract information from a database, according to one or more embodiments. The user interface may present a chat interface that allows a user to perform a natural language conversation with the system.); receiving, using the communication device, at least one request (a natural language question) of the at least one user through the conversational interaction interface from the at least one user device ([0018] The online system 140 receives natural language questions requesting information stored in a database. [0056] The system receives 310 a natural language question requesting information stored in the database tables of the database schema. The natural language question may be asked using a user interface, for example, the user interface illustrated in FIG. 4A. [0027] The interface system 160 receives one or more queries from the online system 140 on the external data. The interface system 160 constructs one or more prompts for input to the model serving system 150.); identifying, using the processing device (Fig.1, online system 140), at least one information (information describing relevant database tables) based on the at least one request ; generating, using the processing device, at least one input (prompt) for at least one machine learning model(a prompt for the machine leaned language model) based on the at least one request and the at least one information, wherein the at least one input comprises the at least one request and the at least one information ([0027] The interface system 160 receives one or more queries from the online system 140 on the external data. The interface system 160 constructs one or more prompts for input to the model serving system 150. [0057] The system generates 320 a prompt for the machine learned language model, requesting the machine learned language model to generate a database query for processing the natural language question. According to one or more embodiments, the system includes information describing relevant database tables and database queries in the prompt. The system obtains the information describing relevant database tables from the database table index and the information describing relevant database queries from the database query index.); processing, using the processing device, the at least one input using the at least one machine learning model, wherein the at least one machine learning model is configured for generating at least one output based on the at least one input; generating, using the processing device, at least one response for the at least one request based on the processing of the at least one input ([0058] The system sends 330 the prompt to the machine learned language model for execution. If the machine learned language model is stored on the system, the system may execute the machine learned language model by providing the prompt as the input. [0060] According to one or more embodiments, the system determines the set of database tables and the set of database queries by generating one or more prompts requesting the set of database tables and the set of database queries relevant to the natural language question and sending the prompt to the machine learned language model. The response (or responses) to the one or more prompts include(s) the set of database tables and the set of database queries relevant to the natural language question.); transmitting, using the communication device, the at least one response through the conversational interaction interface for conversationally interacting with the at least one user to the at least one user device ([0062] The system receives 340 the response obtained as a result of execution of the machine learned language model based on the prompt. The response includes the database query generated by the machine learned language model based on the prompt for processing the natural language question. [0066] The system sends the generated database query to a database system, for example, Snowflake™ for execution. The system presents the results obtained by executing the database query to the user via a user interface, for example, the user interface illustrated in FIG. 4B.); and storing, using a storage device(Fig.2, a data store 240), the at least one machine learning model, the at least one request, and the at least one response ([0043] The data store 240 stores data used by the online system 140. [0049] According to one or more embodiments, the system builds two indexes—a database table index for storing information describing database tables of a database schema and a database query index storing database queries based on the database tables of the database schema. [0050] The system builds the database query index by accessing a store of historical data comprising past queries that were executed by users. The system analyzes each database query to identify the table or tables used in the database query and stores the database query in the database query index in association with each database table used in the database query. The database query index may store database queries that process data in a single database query as well as complex database queries that join multiple database tables. [0074] Pairs of prompt and generated output of database queries is collected, for example, based on past executions that may be stored in system logs.).
Regarding Claim 2, Dilipkumar teach: The method of claim 1 further comprising: analyzing, using the processing device, the at least one output; and modifying, using the processing device, the at least one output by incorporating at least one modification in the at least one output based on the analyzing of the at least one output, wherein the generating of the at least one response is further based on the modifying of the at least one output (See rejection of claim 1 and [0064] According to one or more embodiments, the system analyzes the database query returned by the machine learned language model to determine whether the machine learned language model generated a valid query. If the system determines that the database query is not a valid database query, the system sends a subsequent request to the machine learned language model to regenerate the database query. [0065] The system may check if the database query returns any data that the user who provided the natural language question has access to. If the system determines that the database query provides data that the user does not have access to, the system sends a subsequent request to the machine learned language model, requesting the machine learned language model to regenerate the database query that limits the results returned to values that the user has access to. [0073] If the system determines as part of validation that the generated database query is not valid, the system sends a request to the machine learned language model to regenerate the database query. The system may modify the prompt to the machine learned language model to assist the machine learned language model in generating a valid database query.).
Regarding Claim 3, Dilipkumar teach: The method of claim 1 further comprising: analyzing, using the processing device, the at least one request; identifying, using the processing device, at least one instruction for the at least one request based on the analyzing of the at least one request; and appending, using the processing device, the at least one instruction (the system may include the validation error and specify that the generated database query or the JSON object had a specific issue as identified and the machine learned language model should generate a result without the stated issue) to the at least one request, wherein the generating of the at least one input (modify the prompt to state that the generated database query should not return results based on identified columns) is further based on the appending of the at least one instruction (See rejection of claim 1 and [0064] According to one or more embodiments, the system analyzes the database query returned by the machine learned language model to determine whether the machine learned language model generated a valid query. [0073] If the system determines as part of validation that the generated database query is not valid, the system sends a request to the machine learned language model to regenerate the database query. The system may modify the prompt to the machine learned language model to assist the machine learned language model in generating a valid database query. For example, the system may include the validation error and specify that the generated database query or the JSON object had a specific issue as identified and the machine learned language model should generate a result without the stated issue. If the system determines that the generated database query accesses database tables that the user should not have access to, the system modifies the prompt and sends it again to the machine learned language model for regenerating the database query. For example, the system may modify the prompt to mention database tables that the user does not have access to and request the machine learned language model to generate a database query that does not access the identified database tables. If the database query returns values that should not be presented to an end user (e.g., internal identifiers used by the database tables), the system may modify the prompt to state that the generated database query should not return results based on identified columns and the machine learned language model should regenerate the database query.).
Regarding Claim 4, Dilipkumar teach: The method of claim 1 further comprising analyzing, using the processing device, the at least one request using the at least one machine learning model, wherein the at least one machine learning model is configured for generating at least one initial output (database query returned by the machine learned language model ) based on the at least one request, wherein the identifying of the at least one information is further based on the at least one initial output, wherein the generating of the at least one output is further based on the at least one initial output (See rejection of claim 1 specifically [0064] According to one or more embodiments, the system analyzes the database query returned by the machine learned language model to determine whether the machine learned language model generated a valid query. For example, the system may parse the generated database query to make sure that there are no syntax errors in the generated query. The system may compile the database query and generate an execution plan to ensure that the database query does not refer to database tables that are non-existent.).
Regarding Claim 5, Dilipkumar teach: The method of claim 1, wherein the at least one machine learning model comprises at least one large language model (LLM) comprising a generative pre-trained transformer (GPT) architecture (See rejection of claim 1 and [0022] In one or more embodiments, when the machine-learned model including the LLM is a transformer-based architecture, the transformer has a generative pre-training (GPT) architecture including a set of decoders that each perform one or more operations to input data to the respective decoder.).
Regarding Claim 6, Dilipkumar teach: The method of claim 1, wherein the at least one machine learning model is trained using a plurality of training samples ([0012] The online system stores information describing tables of the database as well as database queries based on the database tables in one or more vector indexes associated with the machine learned language model. The prompt requests the machine learned language model to generate a database query that extracts the data requested by the user in the natural language question. The prompt specifies the database tables relevant to a natural language question received from a user as well as sample queries based on the database tables. [0020] In one or more embodiments, the language models are large language models (LLMs) that are trained on a large corpus of training data to generate outputs for the NLP tasks. An LLM may be trained on massive amounts of text data, often involving billions of words or text units. The large amount of training data from various data sources allows the LLM to generate outputs for many tasks.), wherein the method comprises: retrieving, using the storage device(database or websites), at least one data specific to at least one domain(contextual information for the query or learned language model based on the relevant database) ([0023] An LLM may be trained on a large amount of data from various data sources. [0026] The interface system 160 receives data from the online system 140 (for example, information describing database tables and example database queries) and builds a vector index over the external data using, for example, another machine learned language model or heuristics. [0027] The interface system 160 constructs one or more prompts for input to the model serving system 150. A prompt may include the query of the user and context obtained from the structured index of the external data. In one instance, the context in the prompt includes portions of information obtained from the index as contextual information for the query. [0029] A database may store a large number of database tables and a large number of example database queries. The online system 140 generates a prompt for a machine learned language model based on the relevant database tables and database queries.); analyzing (comparing its output from input data), using the processing device, the at least one data ([0041] The machine learning training module 230 trains a machine learning model based on a set of training examples. Each training example includes input data to which the machine learning model is applied to generate an output. In these cases, the machine learning model is trained by comparing its output from input data of a training example to the label for the training example.); generating, using the processing device, at least one additional training sample (iterative process to train a machine learning model on each of the set of training examples) for the at least one machine learning model ([0042] The machine learning training module 230 may apply an iterative process to train a machine learning model whereby the machine learning training module 230 trains the machine learning model on each of the set of training examples. To train a machine learning model based on a training example, the machine learning training module 230 applies the machine learning model to the input data in the training example to generate an output. ); and tuning (update), using the processing device, the at least one machine learning model based the at least one additional training sample using at least one training technique([0042] The machine learning training module 230 scores the output from the machine learning model using a loss function. A loss function is a function that generates a score for the output of the machine learning model such that the score is higher when the machine learning model performs poorly and lower when the machine learning model performs well. The machine learning training module 230 updates the set of parameters for the machine learning model based on the score generated by the loss function. For example, the machine learning training module 230 may apply gradient descent to update the set of parameters. [0043] The data store 240 also stores trained machine learning models trained by the machine learning training module 230. [0054] As an example, the machine learning training module 230 may obtain a pre-trained transformer language model and further fine tune the parameters of the transformer model using training data stored in the data store 240.), wherein the at least one machine learning model is configured for performing at least one operation for the generating of the at least one output based on the tuning (See rejection of claim 1 and [0052] According to an embodiment, the online system 140 extracts a set of example database queries based on the set of database tables determined to be relevant to the natural language question and specifies the set of example database queries in the prompt sent to the machine learned language model for generating a database query representing the natural language question. [0053] The prompt generation module 260 extracts a set of example database queries relevant to the natural language question based on the vector index 280 and includes the example database queries along with the set of database tables relevant to the natural language question in the prompt generated for sending to the machine learned language model for generating a database query representing the natural language question.).
Regarding Claim 7, Dilipkumar teach: The method of claim 1, wherein the at least one machine learning model is associated with at least one persona (database queries relevant to the natural language question), wherein the at least one machine learning model is configured for modifying the at least one output based on the at least one persona, wherein the generating of the at least one response is further based on the modifying of the at least one output based on the at least one persona (See rejection of claim 1).
Regarding Claim 8, Dilipkumar teach: The method of claim 1 further comprising: analyzing, using the processing device, at least one interaction (query of the user ) with the at least one user through the conversational interaction interface, wherein the at least one interaction comprises the at least one request and the at least one response ([0062] The system receives 340 the response obtained as a result of execution of the machine learned language model based on the prompt. The response includes the database query generated by the machine learned language model based on the prompt for processing the natural language question. [0063] The system may request the machine learned language model to provide a comment describing the query. The system uses the comment to perform a conversation with the user via the user interface.); determining, using the processing device, at least one task (generate a database query) for the at least one user based on the analyzing (analyzes the database query) of the at least one interaction ([0064] According to one or more embodiments, the system analyzes the database query returned by the machine learned language model to determine whether the machine learned language model generated a valid query. For example, the system may parse the generated database query to make sure that there are no syntax errors in the generated query. If the system determines that the database query is not a valid database query, the system sends a subsequent request to the machine learned language model to regenerate the database query. [0065] The system may check if the database query returns any data that the user who provided the natural language question has access to. [0075] The system receives from the user, subsequent natural language requests that refine the presented results of the previous natural language request. The next natural language request may refer to the result of the previous natural language request and request the system to further refine the result, for example, by filtering the rows/columns.); and transmitting, using the communication device, the at least one task to the at least one user device through the conversational interaction interface (See rejection of claim 1 and [0066] The system executes the database query obtained from the response generated by the machine learned language model. The system sends the generated database query to a database system, for example, Snowflake™ for execution. The system presents the results obtained by executing the database query to the user via a user interface, for example, the user interface illustrated in FIG. 4B.).
Regarding Claim 9, Dilipkumar teach: The method of claim 1 further comprising: analyzing, using the processing device, the at least one request; and determining, using the processing device, at least one context associated with the helping of the at least one user based on the analyzing of the at least one request, wherein the generating of the at least one output is further based on the at least one context (See rejection of claim 1 and [0075] According to an embodiment, the system receives a natural language request, generates a database query based on the natural language request, executes the database query to generate results and presents the results. The system receives from the user, subsequent natural language requests that refine the presented results of the previous natural language request. The next natural language request may refer to the result of the previous natural language request and request the system to further refine the result, for example, by filtering the rows/columns. For example, the next natural language request may refine the previous result to only present results satisfying a certain condition. Alternatively, the next natural language request may group the previous results based on one or more fields of the result. The system may provide along with a new prompt, the context of the previous natural language request and the database query previously generated along with the new natural language request. The new prompt is provided to the machine learned language model. The machine learned language model may generate a database query that composes the previous query with a new query, or modifies the previously generated database query, or generate a new database query that effectively composes the two natural language requests. Accordingly, a user can continue providing a series of natural language requests to incrementally refine results at each step and build a complex database query using the machine learned language model.).
Regarding Claim 10, Dilipkumar teach: The method of claim 1 further comprising: retrieving, using the storage device, a plurality of historical requests and a plurality of responses associated with the plurality of requests( system builds the database query index by accessing a store of historical data comprising past queries that were executed by users); analyzing, using the processing device, the plurality of historical requests and the plurality of responses; clustering (database query index), using the processing device, the plurality of historical requests and the plurality of responses in at least one cluster (database query index in association with each database table used in the database query ) using at least one criterion (database table used in the database query) based on the analyzing of the plurality of historical requests and the plurality of responses; storing, using the storage device, the at least one cluster(stores the database query in the database query index in association with each database table used in the database query); analyzing, using the processing device, the at least one request and the at least one cluster; and identifying, using the processing device, at least one of the at least one cluster based on the analyzing of the at least one request and the clustering, wherein the generating of the at least one response is further based on at least one of the at least one cluster for the at least one request (See rejection of claim 1 and [0050] The system builds the database query index by accessing a store of historical data comprising past queries that were executed by users. The historical data may be extracted from a system such as Snowflake™ or from a source code repository or a version control system such as GitHub™. The system analyzes each database query to identify the table or tables used in the database query and stores the database query in the database query index in association with each database table used in the database query. The database query index may store database queries that process data in a single database query as well as complex database queries that join multiple database tables. [0051] According to one or more embodiments, the online system 140 receives a natural language question requesting information stored in the databases of the online system. The online system 140 generates a vector index query for extracting relevant database tables based on the natural language question. The vector index query is configured to identify a set of database tables that are relevant to answering the natural language question. The vector index 280 executes the vector index query by generating a vector representation of the natural language question and identifying one or more database tables that have vector representations that are within a threshold vector distance of the vector representation of the natural language question. The vector index 280 may execute the vector index query by generating a vector representation of the natural language question, sorting database tables stored in the vector index 280 based on their vector distances from the vector representation of the natural language question, and selecting the top few database tables that have the closest vector distance from the vector representation of the natural language question. The prompt generation module 260 extracts a set of database tables relevant to the natural language question based on the vector index 280 and includes the database tables in the prompt generated for sending to the machine learned language model for generating a database query representing the natural language question. [0058] The system sends 330 the prompt to the machine learned language model for execution. If the machine learned language model is stored on the system, the system may execute the machine learned language model by providing the prompt as the input. [0062] The system receives 340 the response obtained as a result of execution of the machine learned language model based on the prompt. The response includes the database query generated by the machine learned language model based on the prompt for processing the natural language question.).
Regarding Claim 11, Dilipkumar teach: A system for facilitating conversational interaction with users to help the users, the system comprising: a communication device (Fig.1, Network 130) configured for: transmitting a conversational interaction interface for conversationally interacting with at least one user to at least one user device (Fig.1, client device 100 or 110) associated with the at least one user; receiving at least one request (a natural language question) of the at least one user through the conversational interaction interface (API or chat interface or Fig.1, interface system 160 ) from the at least one user device; and transmitting the at least one response through the conversational interaction interface for conversationally interacting with the at least one user to the at least one user device; a processing device (Fig.1, online system 140) communicatively coupled with the communication device, wherein the processing device is configured for: identifying at least one information based on the at least one request; generating at least one input (a prompt) for at least one machine learning model (a prompt for the machine leaned language model) based on the at least one request and the at least one information(information describing relevant database tables), wherein the at least one input comprises the at least one request and the at least one information; processing the at least one input using the at least one machine learning model, wherein the at least one machine learning model is configured for generating at least one output based on the at least one input; and generating the at least one response for the at least one request based on the processing of the at least one input; and a storage device communicatively coupled with the processing device, wherein the storage device is configured for storing the at least one machine learning model, the at least one request, and the at least one response (See rejection of Claim 1).
Regarding Claim 12, Dilipkumar teach: The system of claim 11, wherein the processing device is further configured for: analyzing the at least one output; and modifying the at least one output by incorporating at least one modification in the at least one output based on the analyzing of the at least one output, wherein the generating of the at least one response is further based on the modifying of the at least one output(See rejection of Claim 2).
Regarding Claim 13, Dilipkumar teach: The system of claim 11, wherein the processing device is further configured for: analyzing the at least one request; identifying at least one instruction for the at least one request based on the analyzing of the at least one request; and appending the at least one instruction to the at least one request, wherein the generating of the at least one input is further based on the appending of the at least one instruction(See rejection of Claim 3).
Regarding Claim 14, Dilipkumar teach: The system of claim 11, wherein the processing device is configured for analyzing the at least one request using the at least one machine learning model, wherein the at least one machine learning model is configured for generating at least one initial output based on the at least one request, wherein the identifying of the at least one information is further based on the at least one initial output, wherein the generating of the at least one output is further based on the at least one initial output(See rejection of Claim 4).
Regarding Claim 15, Dilipkumar teach: The system of claim 11, wherein the at least one machine learning model comprises at least one large language model (LLM) comprising a generative pre-trained transformer (GPT) architecture(See rejection of Claim 5).
Regarding Claim 16, Dilipkumar teach: The system of claim 11, wherein the at least one machine learning model is trained using a plurality of training samples, wherein the storage device is further configured for retrieving at least one data specific to at least one domain, wherein the processing device is further configured for: analyzing the at least one data; generating at least one additional training sample for the at least one machine learning model; and tuning the at least one machine learning model based the at least one additional training sample using at least one training technique, wherein the at least one machine learning model is configured for performing at least one operation for the generating of the at least one output based on the tuning(See rejection of Claim 6).
Regarding Claim 17, Dilipkumar teach: The system of claim 11, wherein the at least one machine learning model is associated with at least one persona, wherein the at least one machine learning model is configured for modifying the at least one output based on the at least one persona, wherein the generating of the at least one response is further based on the modifying of the at least one output based on the at least one persona(See rejection of Claim 7).
Regarding Claim 18, Dilipkumar teach: The system of claim 11, wherein the processing device is further configured for: analyzing at least one interaction with the at least one user through the conversational interaction interface, wherein the at least one interaction comprises the at least one request and the at least one response; and determining at least one task for the at least one user based on the analyzing of the at least one interaction, wherein the communication device is further configured for transmitting the at least one task to the at least one user device through the conversational interaction interface(See rejection of Claim 8).
Regarding Claim 19, Dilipkumar teach: The system of claim 11, wherein the processing device is further configured for: analyzing the at least one request; and determining at least one context associated with the helping of the at least one user based on the analyzing of the at least one request, wherein the generating of the at least one output is further based on the at least one context(See rejection of Claim 9).
Regarding Claim 20, Dilipkumar teach: The system of claim 11, wherein the storage device is further configured for: retrieving a plurality of historical requests and a plurality of responses associated with the plurality of requests; and storing at least one cluster, wherein the processing device is further configured for: analyzing the plurality of historical requests and the plurality of responses; and clustering the plurality of historical requests and the plurality of responses in the at least one cluster using at least one criterion based on the analyzing of the plurality of historical requests and the plurality of responses; analyzing the at least one request and the at least one cluster; and identifying at least one of the at least one cluster based on the analyzing of the at least one request and the clustering, wherein the generating of the at least one response is further based on at least one of the at least one cluster for the at least one request(See rejection of Claim 10).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art of record Kshirsagar et al.(US 2025/0272279 A1) teach: Systems And Methods For Generative Language Model Database System Action Integration.
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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2653
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