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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 13, and 20 recites “receiving, via a client interface, a query to trigger a prompt of a set of prompts configured for a large language model (LLM), wherein the query is indicative of a first set of data from one or more data sources linked to the prompt”, “transmitting, to an augmentation service, a request for a set of grounding data associated with the first set of data from the one or more data sources linked to the prompt”, “receiving, from the augmentation service, the set of grounding data obtained from the one or more data sources of a plurality of data sources associated with the client interface, wherein the set of grounding data comprises hierarchical context data from the one or more data sources”, “querying the LLM via the prompt using the first set of data and the set of grounding data obtained from the one or more data sources”, “receiving, from the LLM, a response to the query”, and “providing, to the client interface, the response for display of the response via the client interface”.
The limitation of receiving a query to trigger a prompt, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more memories” and “one or more processors”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the elements listed above, “receiving” in the context of this claim encompasses receiving a question and triggering further actions, which a human can do in the mind or with a pen and paper. Next, the limitation of transmitting a request for grounding data, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “transmitting” in the context of this claim encompasses sending a request for data, which a human can do in the mind or with a pen and paper. Next, the limitation of receiving grounding data, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “receiving” in the context of this claim encompasses retrieving data from a data source, which a human can do in the mind or with a pen and paper. Next, the limitation of querying via a prompt, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “querying” in the context of this claim encompasses searching data, which a human can do in the mind or with a pen and paper. Next, the limitation of receiving a response, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “receiving”, in the context of this claim encompasses receiving an answer to a question, which a human can do in the mind or with a pen and paper. Lastly, the limitation of providing a response, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “providing” in the context of this claim encompasses displaying an answer which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using a memory and processor to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a memory and processor to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 2-12 and 14-19 are also rejected for the same reasons provided in independent claim 1 and 13 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 3-4, 6-13, 15-16, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bayless et al. US 20250112878 A1 (hereinafter Bayless).
Regarding independent claims 1, 13, and 20, Bayless teaches a method for data processing, comprising / an apparatus for data processing, comprising / a non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to:
one or more memories storing processor-executable code (FIG. 14, 1400); and
one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to (FIG. 14, 1400):
receiving, via a client interface, a query to trigger a prompt of a set of prompts configured for a large language model (LLM), wherein the query is indicative of a first set of data from one or more data sources linked to the prompt (FIG. 1, 128, 132; [0069] “The prompt-engineering component 208 may then provide the most relevant information along with the formal-language query 128 to the LLM 118 in one or more prompts 132”; [0041] “The knowledge-graph system 104 may then iteratively prompt the LLM 118 to provide formal-language answers 134 for the plurality of questions, and build or argument the knowledge graph 122 using the answers 134”);
transmitting, to an augmentation service, a request for a set of grounding data associated with the first set of data from the one or more data sources linked to the prompt ([0024] “The knowledge-graph system may request that the LLM provide a truthful answer to the query”);
receiving, from the augmentation service, the set of grounding data obtained from the one or more data sources of a plurality of data sources associated with the client interface, wherein the set of grounding data comprises hierarchical context data from the one or more data sources (FIG. 2, 218; [0072] “the service provider system 102 may include internal data sources 218 (e.g., service documentation) usable to improve the knowledge graphs 122. The internal data sources 218 may include data provided by users 106, documentation generated by the service provider, and/or other information”);
querying the LLM via the prompt using the first set of data and the set of grounding data obtained from the one or more data sources ([0103] “the knowledge-graph system 104 may, based at least in part on the formal-language answer 134 not being included in the knowledge graph 122, prompt a large language model (LLM) 118 to determine the answer to the formal-language query 128”);
receiving, from the LLM, a response to the query (FIG. 1, 134); and
providing, to the client interface, the response for display of the response via the client interface (FIG. 1, 134, 110, 114; [0120] “The chatbot system 110 may generate a response in the natural language that includes the answer, and at 910, the chatbot system 110 may provide, via the chatbot interface 114 and to the user 106, the response in the natural language”).
Regarding claims 3 and 15, Bayless teaches all of the limitations of claim 1 and 13, upon which claims 3 and 15 depend.
Additionally, Bayless teaches transforming, via the augmentation service, the set of grounding data obtained from the one or more data sources into a first data format, the set of grounding data being transformed into the first data format via a first data transformer of a plurality of data transformers associated with the augmentation service, wherein a respective data transformer of the plurality of data transformers is associated with a respective data format (FIG. 2, 204, 118; [0067] “The query engine 204 may integrate the retrieved information from the knowledge graphs 122/202 to provide context or augment the generative model's responses. The integration can be done in various ways, such as concatenating the retrieved information with the input prompt, using it as a context window, or employing attention mechanisms to focus on specific parts of the retrieved content”).
Regarding claims 4 and 16, Bayless teaches all of the limitations of claim 3 and 15, upon which claims 4 and 16 depend.
Additionally, Bayless teaches adjusting, via a first data refiner of a plurality of data refiners associated with the augmentation service, the set of grounding data within the first data format to be used for querying the LLM, wherein querying the LLM via the prompt and the set of grounding data is based at least in part on adjustments to the set of grounding data ([0067] “The query engine 204 may then generate output using combined input (original prompt+retrieved information) that is passed to the generative model, and the model then generates a response based on this augmented input”).
Regarding claims 6 and 18, Bayless teaches all of the limitations of claim 1 and 13, upon which claims 6 and 18 depend.
Additionally, Bayless teaches wherein receiving the set of grounding data obtained from the one or more data sources comprises: querying, via the augmentation service, the one or more data sources for the set of grounding data based at least in part on reception of the first set of data via the query, wherein the set of grounding data is associated with the first set of data (FIG. 2, 204, 202, 122, 216, 218;).
Regarding claim 7, Bayless teaches all of the limitations of claim 1, upon which claim 7 depends.
Additionally, Bayless teaches wherein respective prompts of the set of prompts configured for the LLM are configured to perform respective tasks via the LLM (FIG. 1, 132; [0045] “the LLMs 118 can be fine-tuned for specific tasks or prompts, such as summarizing content, answering questions, and text completion”).
Regarding claims 8 and 19, Bayless teaches all of the limitations of claim 1 and 13, upon which claims 8 and 19 depend.
Additionally, Bayless teaches wherein the plurality of data sources comprises internal databases, external databases, cloud-based platforms, application programming interfaces associated with respective services, customer relationship management systems, or any combination thereof ([0054] “the service provider system 102 may provide cloud-based, scalable, and network accessible compute power services, storage services, database services, and/or other services”; [0062]).
Regarding claim 9, Bayless teaches all of the limitations of claim 1, upon which claim 9 depends.
Additionally, Bayless teaches wherein the hierarchical context data of the set of grounding data is based at least in part on metadata from the one or more data sources ([0023] “a chatbot may answer user queries by working in conjunction with the knowledge-graph system that may generate, build, or augment a knowledge graph at least partly using answers from an LLM”).
Regarding claim 10, Bayless teaches all of the limitations of claim 1, upon which claim 10 depends.
Additionally, Bayless teaches wherein the plurality of data sources comprises unstructured data sources, structured data sources, or both (FIG. 1, 102, 120, 122; [0020] “a knowledge graph is a data structure used to organize information and represent knowledge in a structured format”).
Regarding claim 11, Bayless teaches all of the limitations of claim 1, upon which claim 11 depends.
Additionally, Bayless teaches wherein the client interface is a graphical user interface, an application programming interface, or a combination thereof (FIG. 1, 108, 124;).
Regarding claim 12, Bayless teaches all of the limitations of claim 1, upon which claim 12 depends.
Additionally, Bayless teaches wherein the augmentation service is configured to format data obtained from the one or more data sources for respective prompts of the set of prompts irrespective of a respective data source type associated with the one or more data sources ([0067] “The query engine 204 may integrate the retrieved information from the knowledge graphs 122/202 to provide context or augment the generative model's responses. The integration can be done in various ways, such as concatenating the retrieved information with the input prompt, using it as a context window, or employing attention mechanisms to focus on specific parts of the retrieved content”).
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.
Claims 2, 5, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bayless in view of Thomas et al. US 20240386058 A1 (hereinafter Thomas).
Regarding claims 2 and 14, Bayless teaches all of the limitations of claims 1 and 13, upon which claims 2 and 14 depend.
Bayless fails to teach receiving, prior to receiving the query, a configuration of the client interface, the configuration comprising a selection of one or more query configurations, a selection of the set of prompts that is based at least in part on the selection of the one or more query configurations, an indication of one or more labels for the client interface, or any combination thereof, wherein reception of the query is based at least in part on reception of the configuration for the client interface.
However, Thomas teaches receiving, prior to receiving the query, a configuration of the client interface, the configuration comprising a selection of one or more query configurations, a selection of the set of prompts that is based at least in part on the selection of the one or more query configurations, an indication of one or more labels for the client interface, or any combination thereof, wherein reception of the query is based at least in part on reception of the configuration for the client interface ([0063] “an interaction with LLM 330 including inputs received from a user, prompts generated based on the inputs, and responses based on replies to the inputs from LLM 330. In FIG. 5B, the user enters the natural language input about chart 503 in task pane 504. User interface 307 transmits the user input to prompt engine 305 which generates a prompt based on the input. Prompt engine 305 selects a prompt template based on a type of inquiry and configures a prompt according to the template”, examiner interprets the template to be the configuration).
Bayless in view of Thomas are considered to be analogous to the claimed invention because both are the same field of information retrieval. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users of Bayless with the technique of selecting of prompts based on query configurations taught by Thomas in order to improve the integration of productivity applications and large language models (see Thomas [0001]).
Regarding claims 5 and 17, Bayless teaches all of the limitations of claims 1 and 13, upon which claims 5 and 17 depend.
Bayless fails to teach providing, to the client interface, a display for selection of the prompt of the set of prompts; and receiving, from the client interface, the selection of the prompt of the set of prompts, wherein reception of the query to trigger the prompt is based at least in part on reception of the selection.
However, Thomas teaches providing, to the client interface, a display for selection of the prompt of the set of prompts ([0019] “The application displays the clarifying question in the user interface and generates a follow-up prompt based on the user's response”); and receiving, from the client interface, the selection of the prompt of the set of prompts, wherein reception of the query to trigger the prompt is based at least in part on reception of the selection ([0066] “Prompt engine 305 configures a prompt according to the selected template and which includes the updated property tree for chart 503 for context.”)
Bayless in view of Thomas are considered to be analogous to the claimed invention because both are the same field of information retrieval. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users of Bayless with the technique of selecting of prompts based on query configurations taught by Thomas in order to improve the integration of productivity applications and large language models (see Thomas [0001]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 20260050616 A1) teaches systems and methods for a fine-tuning system for large language models trained for open-ended domain-specific tasks. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include chatbots, information retrieval systems, question-and-answer systems, and the like. To provide better LLM training and fine-tuning, which may improve LLM performance in answering users' questions in an automated manner, the service provider may implement a fine-tuning system that may utilize automated annotations of training data, such as query and response pairs. An LLM may be prompted to determine an annotation to such pairs, and the annotations may be used to label the training data. A fine-tuning system and operations may then be implemented to fine-tune the LLMs using different processes including question-answering, retrieval augmented generation, or a continuous fine-tuning based on a size of the training data.
Nabel (US 20250342182 A1) teaches various embodiments of the technology described programmatically access a user query intended for a Large Language Model (LLM), analyze the user query, and determine prompt-enriching information that is combined with the user query to generate an enriched user query that is ultimately communicated to the LLM. In this manner, additional prompt-enriching information or context is added to the user query before being communicated to the LLM so that the additional prompt-enriching information, along with the user query, can be tokenized to better guide the LLM to a more accurate answer without modifying weights, parameters, or training of the LLM. Certain embodiments have the technical effect of improved accuracy relative to existing approaches by enriching user queries with prompt-enriching information to generate an enriched user query that is passed to the LLM. Based on the enriched user query, certain embodiments reduce the likelihood of hallucinations present in the LLM response.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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