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 . This action is in response to the application filled on 12/27/2023. Claims 1-20 are pending and have been examined.
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.
Regarding claim 1:
Step 1: The claimed invention is directed to a method which falls into the statutory category of process.
Step 2A Prong 1: The claimed invention recites multiple mental process: determining, via at least one processor of the computer system, a context of the question and executing, at the computer system, the at least one function, resulting in at least one function result. Given a human being can reasonably determine the context of a question and execute/do some general function/action using a generic computer system.
Step 2A Prong 2: Claim 1 does not integrate the abstract idea into a practical application since
the additional elements of:
one processor of the computer system, is merely an instruction to apply the abstract idea using a generic computer system.
receiving, at a computer system from a terminal, a question, is insignificant extra-solution activity: data gathering/receiving data.
transmitting, from the computer system to a large language model chatbot, the question with the context, is insignificant extra-solution activity: data gathering/transmitting data.
receiving, at the computer system from the large language model chatbot based on the question and the context, at least one function, is insignificant extra-solution activity: data gathering/receiving data.
transmitting, from the computer system to the large language model chatbot, the at least one function result, is insignificant extra-solution activity: data gathering/transmitting data.
receiving, at the computer system from the large language model chatbot, a natural language answer to the question based on the at least one function result, is insignificant extra-solution activity: data gathering/receiving data.
transmitting, from the computer system to the terminal, the natural language answer, is insignificant extra-solution activity: data gathering/transmitting data.
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to
significantly more than a judicial exception. As discussed above, the additional element of
receiving and transmitting data is considered insignificant extra-solution activity because it is well-understood, routine, conventional activity as evidenced by MPEP §2106.05(d)(II)(I).
Regarding claim 2, the rejection of claim 1 is incorporated, further the claim recites retrieving, at the computer system from a graph, the at least one function. This limitation amounts to insignificant extra-solution activity: data gathering/receiving data. Therefore, it is well-understood, routine, conventional activity. As such, the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application nor to significantly more. Claim 2 is not patent eligible.
Regarding claim 3, the rejection of claim 1 is incorporated, further the claim recites further comprising: generating, via the at least one processor, an embedding based on the question; and identifying, via the at least one processor, the context based on similarity of the embedding to at least one topic, wherein the context is a most similar topic within the at least one topic. The limitation amounts to more specifics of the abstract idea of determining a context; further, generating the embedding is a mental process given a human being can assign some value to a question and then identify similar values assigned to different topics. As such, the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application nor to significantly more. Claim 3 is not patent eligible.
Regarding claim 4, the rejection of claim 3 is incorporated, further the claim recites wherein the similarity is determined using a distance measurement of the embedding to the at least one topic. This limitation amounts to more specifics of the abstract idea of determining context. Further, determining similarity using a distance measurement is a mathematical concept given a human being can find a distance between two numerical points and determine similarity this way. As such, the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application nor to significantly more. Claim 4 is not patent eligible.
Regarding claim 5, the rejection of claim 4 is incorporated, further the claim recites wherein the distance measurement is a Cosine distance. This limitation amounts to more specifics of the abstract idea of determining context using a specific distance metric. As such, the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application nor to significantly more. Claim 5 is not patent eligible.
Regarding claim 6, the rejection of claim 1 is incorporated, further the claim recites wherein the transmitting of the question with the context to the large language model chatbot results in a conversation; and wherein the transmitting of the at least one function result to the large language model chatbot appends the at least one function result to the conversation. This limitation amounts to more specifics of the additional element of transmitting, from the computer system to a large language model chatbot, the question with the context and transmitting, from the computer system to the large language model chatbot, the at least one function result. As such the limitation is considered insignificant extra-solution activity: transmitting data. As such, the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application nor to significantly more. Claim 6 is not patent eligible.
Regarding claim 7, the rejection of claim 1 is incorporated, further the claim recites wherein the large language model chatbot is one of CHATGPT, BARD, BING, and GROK. This limitation amounts to merely indicating a technological environment in which to apply the judicial exception. As such, the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application nor to significantly more. Claim 7 is not patent eligible.
Regarding claims 8-14, given the claims are merely variations of claims 1-7 in which a system with multiple components, which falls into the statutory category of machine, is claimed instead of a process, the rejections above are incorporated. Therefore, claims 8-14 are not patent eligible.
Regarding claims 15-20, given the claims are merely variations of claims 1-6 in which a
computer program product, which falls into the statutory category of manufacture, is claimed instead of
a process, the rejections above are incorporated. Therefore, claims 15-20 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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) 1-4, 6, 8-11, 13, 15-18, 20 are rejected under 35 U.S.C. 102 a(2) as being anticipated by Khosla (US20250005051A1).
Regarding claim 1, Khosla teaches receiving, at a computer system from a terminal, a question (Par 0020, The customer computing devices 122 can send natural language questions (e.g., input from a user via a user interface (UI) of the customer computing devices 122) to the natural language question answering service); determining, via at least one processor of the computer system, a context of the question (Par 0012, The aggregator may analyze the natural language question and determine what search systems to retrieve passages and QA pairs from, to answer that question. The aggregator may retrieve those passages (and related QA pairs) and use them, along with the question, to formulate a prompt); transmitting, from the computer system to a large language model chatbot, the question with the context (Abs, A large language model (LLM) of the natural language question answering service may receive the prompt and provide an answer); receiving, at the computer system from the large language model chatbot based on the question and the context, at least one function (Par 0064, Moreover, the LLM component 106 may additionally be trained to provide and/or run application programming interface (API) commands in response to a natural language question or prompt); executing, at the computer system, the at least one function, resulting in at least one function result; transmitting, from the computer system to the large language model chatbot, the at least one function result (Par 0064, The LLM component 106 may then generate an answer which provides the API command to execute the function of the network-based on-demand code execution service (e.g., the LLM component 106 may also run the API command for the customer……… The customer may respond to the LLM component 106 (e.g., or alternatively the natural language question answering service 102) and indicate to the LLM component 106 that the API command should be executed), the customer’s response is transmitted from the computer system to the chatbot); receiving, at the computer system from the large language model chatbot, a natural language answer to the question based on the at least one function result; and transmitting, from the computer system to the terminal, the natural language answer (Par 0064, The LLM component 106 may then generate an answer which provides the API command (e.g., to perform the API command on the network-based AI service), the API command or whether or not to perform the command is the natural language answer).
Regarding claim 2, Khosla teaches retrieving, at the computer system from a graph, the at least one function (Par 0044, As another example, a search system may be a network-based system (or associated with a network-based system) that contains knowledge graphs of customers for a network-based service (e.g., what kind of services they have, their usage activity, questions the customers have previously asked, types of questions customers have asked and their occurrence, their preferences regarding answers, etc.). The aggregator component 104 may utilize the knowledge graphs to create QA pairs where information about a customer may be an answer in a QA pair, Par 0064, The LLM component 106 may gain access to the user's credentials (e.g., which services they are subscribed to, usage history, knowledge graphs of the customer, etc.) to generate an answer which can include API commands as an answer, the users knowledge graph is used to retrieve the answer/API command).
Regarding claim 3, Khosla teaches generating, via the at least one processor, an embedding based on the question; and identifying, via the at least one processor, the context based on similarity of the embedding to at least one topic, wherein the context is a most similar topic within the at least one topic (Par 0022, For example, the aggregator component 104 may analyze the natural language question by using string matching techniques (e.g., partial string matching, dense passage retrieval, etc.) to determine the meaning of the natural language question. After determining the meaning of the natural language question, the aggregator component 104 may then determine which of the search systems 124 the aggregator component 104 may retrieve passages from (e.g., retrieve from a network-based storage service QA pair system but not a network-based AI service QA pair system) based on the natural language question. Based on the retrieved passages (e.g., documents, text of the documents, pictures of the documents, or video of the documents, etc.) from the search systems 124, the aggregator component 104 may create a prompt, Par 0045, The aggregator component 104 may also use a similarity score to determine which passages retrieved are relevant (e.g., not out of scope). The retrieved passages may be sent through a dense encoder and to get their dense embeddings. Scores may be generated by the aggregator component 104 for each passage in relativity to the natural language question (e.g., how well the passage is related to the question)).
Regarding claim 4, Khosla teaches wherein the similarity is determined using a distance measurement of the embedding to the at least one topic (Par 0035, To perform retrieval, the dense retriever module 219 may transform the natural language question into a similar dense embedding and find the K nearest neighbors from an index matrix. As such, the dense retriever module 219 may be used to identify dense passages of text in network-based services, Par 0046, Given a question or query, the aggregator component 104 may use DPR techniques to retrieve relevant passages from an index based on the similarity between their representations and the representation of a query or question, k nearest neighbors uses distance).
Regarding claim 6, Khosla teaches wherein the transmitting of the question with the context to the large language model chatbot results in a conversation; and wherein the transmitting of the at least one function result to the large language model chatbot appends the at least one function result to the conversation (Par 0063, The LLM component 106 may store the multiple previous questions as conversational context. The LLM component 106 may utilize this evidence pool, current natural language question, and conversional context, to answer the current question. The LLM component 106 may update the evidence pool in different ways. In one example, the LLM component 106 may update the evidence pool with any questions and/or answers generated by the LLM component, the answers and questions are added to the conversation history through the evidence pool).
Regarding claims 8-11 and claim 13, the inventive concept is essentially the same as claims 1-4 and claim 6 except for the system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations. Khosla teaches a system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations (Par 0078, The computing system may include one or more computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device, Figure 2A). Therefore, Khosla teaches claims 8-11 and claim 13 as shown in the rejections above for claims 1-4 and claim 6.
Regarding claims 15-18 and claim 20, the inventive concept is essentially the same as claims 1-4 and claim 6 except for the non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations. Khosla teaches a non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations (Par 0078, The computing system may include one or more computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device, Figure 2A). Therefore, Khosla teaches claims 15-18 and claim 20 as shown in the rejections above for claims 1-4 and claim 6.
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) 5, 12, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Khosla as applied to claims 1, 8, 15 above, and further in view of Karpukhin (Dense Passage Retrieval for Open-Domain Question Answering).
Regarding claim 5, Khosla teaches the method of claim 1 but fails to teach wherein the distance measurement is a Cosine distance. Karpukhin teaches wherein the distance measurement is a Cosine distance (Pg. 6774, Besides dot product, cosine and Euclidean L2 distance are also commonly used as decomposable similarity functions).
Khosla and Karpukhin both discuss dense passage retrieval to determine similarity between embeddings. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have used cosine distance to measure the similarity between embedded queries and passages given cosine distance can be used with dense passage retrieval instead of other distance metrics.
Regarding claim 12, Khosla teaches the system of claim 8 but fails to teach wherein the distance measurement is a Cosine distance. Karpukhin teaches wherein the distance measurement is a Cosine distance (Pg. 6774, Besides dot product, cosine and Euclidean L2 distance are also commonly used as decomposable similarity functions).
Khosla and Karpukhin both discuss dense passage retrieval to determine similarity between embeddings. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have used cosine distance to measure the similarity between embedded queries and passages given cosine distance can be used with dense passage retrieval instead of other distance metrics.
Regarding claim 19, Khosla teaches the non-transitory computer-readable storage medium of claim 15 but fails to teach wherein the distance measurement is a Cosine distance. Karpukhin teaches wherein the distance measurement is a Cosine distance (Pg. 6774, Besides dot product, cosine and Euclidean L2 distance are also commonly used as decomposable similarity functions).
Khosla and Karpukhin both discuss dense passage retrieval to determine similarity between embeddings. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have used cosine distance to measure the similarity between embedded queries and passages given cosine distance can be used with dense passage retrieval instead of other distance metrics.
Claim(s) 7, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Khosla as applied to claims 1 and 8 above, and further in view of Schick (Toolformer: Language Models Can Teach Themselves to Use Tools).
Regarding claim 7, Khosla teaches the method of claim 1 but fails to teach wherein the large language model chatbot is one of CHATGPT, BARD, BING, and GROK. Schick teaches wherein the large language model chatbot is one of CHATGPT, BARD, BING, and GROK (Table 3, GPT-3).
Khosla and Schick are analogous to the claimed invention because they use language models to call commands such as API commands while taking in questions/queries. Therefore, it would have been obvious to one of ordinary skill in the art to have used GPT-3 as the language model in Khosla because GPT-3 can call API commands as shown in Schick.
Regarding claim 14, Khosla teaches the system of claim 8 but fails to teach wherein the large language model chatbot is one of CHATGPT, BARD, BING, and GROK. Schick teaches wherein the large language model chatbot is one of CHATGPT, BARD, BING, and GROK (Table 3, GPT-3).
Khosla and Schick are analogous to the claimed invention because they use language models to call commands such as API commands while taking in questions/queries. Therefore, it would have been obvious to one of ordinary skill in the art to have used GPT-3 as the language model in Khosla because GPT-3 can call API commands as shown in Schick.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATNAEL A ASEGDEW whose telephone number is (571)270-0407. The examiner can normally be reached 7:30-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATNAEL A ASEGDEW/Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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