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 .
Applicant’s filing dated 05/29/2024 has been received and made of record.
Application 18/677,723 claims priority to Provisional Application 63/472,290, filed 06/10/2023.
Claims 1-20 are currently pending in Application 18/677,723.
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. Claim 15 is drawn to a "machine-storage medium". The specification (Paragraph [0267]) describes the term in an open-ended fashion. Thus, applying the broadest reasonable interpretation in light of the specification and taking into account the meaning of the words in their ordinary usage as they would be understood by one of ordinary skill in the art (MPEP §2111), the claim as a whole covers both transitory and non-transitory media, as well as both tangible and non-tangible media. A transitory (or non-tangible) medium does not fall into any of the four statutory categories of invention (process, machine, manufacture, or composition of matter). Applicant is advised to amend the claim language to explicitly recite a “non-transitory machine-readable storage medium” instead. Claims 16-20 depend from claim 15 and are therefore subject to the same rejection.
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)(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) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Smith Lewis (US 2024/0289863 A1).
Regarding claims 1, 8, and 15, Smith Lewis discloses A method/machine/machine-storage medium… (Smith Lewis: Claims 1, 8, and 15), comprising:
one or more processors (Smith Lewis: Claim 8, “a computer having a processor and a memory”);
and one or more memories storing instructions that, when executed by the one or more processors, cause the machine to perform operations (Smith Lewis: Claim 8, “a computer having a processor and a memory; and one or more code sets stored in the memory and executed by the processor”) comprising:
receiving, by one or more processors, a user prompt from a user (Smith Lewis: Paragraph [0052], “ this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”);
determining, by the one or more processors, a conversation history of one or more conversations between the user and a chatbot (Smith Lewis: Paragraph [0052], “ this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”);
generating, by the one or more processors, an augmented prompt using the user prompt and the conversation history (Smith Lewis: Paragraph [0052], “ this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”);
communicating, by the one or more processors, the augmented prompt to a generative AI model (Smith Lewis: Paragraph [0052], “ this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”);
receiving, by the one or more processors, a response from the generative AI model to the augmented prompt (Smith Lewis: Paragraph [0052], “ this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”);
and providing, by the one or more processors, the response to the user (Smith Lewis: Paragraph [0117], “the processor is configured to personalize one or more responses of a conversational agent interacting with the first user based at least in part on the first user profile. This may include, for example, responses based on dedicated and/or curated memories, information, etc., as described herein. In some embodiments, the processor may interact with the user via a user interface”, and Figure 4 response in element 400).
Smith Lewis teaches 2/9/16. The method of claim 1/machine of claim 8/medium of claim 15, wherein determining the conversation history comprises:
retrieving, from a conversation history datastore, a conversation history of one or more conversations between a user and a chatbot (Smith Lewis: Paragraph [0045], “the conversation history between the system and the user, or a subset of this history identified as important either by the user or by a machine learning model, or a set of higher level summaries or other abstractions of the user's conversation history, or similar text, audio or visual representations of the user's history with the system, may be embedded as indexed vectors in a vector database or similar storage structure for later reference by the system”).
Smith Lewis teaches 3/10/17. The method of claim 1/machine of claim 8/medium of claim 15, further comprising:
generating one or more summarized memories using the conversation history (Smith Lewis: Paragraph [0045], “the conversation history between the system and the user, or a subset of this history identified as important either by the user or by a machine learning model, or a set of higher level summaries or other abstractions of the user's conversation history, or similar text, audio or visual representations of the user's history with the system, may be embedded as indexed vectors in a vector database or similar storage structure for later reference by the system”).
Smith Lewis teaches 4/11/18. The method of claim 3/machine of claim 10/medium of claim 17, further comprising:
generating one or more moderated memories using the summarized memories (Smith Lewis: Paragraph [0045], “the conversation history between the system and the user, or a subset of this history identified as important either by the user or by a machine learning model, or a set of higher level summaries or other abstractions of the user's conversation history, or similar text, audio or visual representations of the user's history with the system, may be embedded as indexed vectors in a vector database or similar storage structure for later reference by the system”; the higher level abstractions and/or embedded indexed vectors can be considered “moderated memories”);
and storing the one or more moderated memories into a memories datastore (Smith Lewis: Paragraph [0045], “the conversation history between the system and the user, or a subset of this history identified as important either by the user or by a machine learning model, or a set of higher level summaries or other abstractions of the user's conversation history, or similar text, audio or visual representations of the user's history with the system, may be embedded as indexed vectors in a vector database or similar storage structure for later reference by the system”).
Smith Lewis teaches 5. The method of claim 4, further comprising, receiving a request from the user to delete the conversation history;
and in response to receiving the request, deleting the conversation history and one or more memories stored in the memories datastore associated with the conversation history (Smith Lewis: Paragraph [0093], “a user may be able to view the memories that the system holds about them and delete, correct, or add to them”, and Paragraph [0122], “users are provided with transparency into memory retention policies across coordinating agents. Explicit visibility may be given regarding data usage purposes, sharing protocols, retention duration commitments, and options to permanently delete memories on-demand through administrator dashboards.”).
Smith Lewis teaches 6/13/19. The method of claim 4/machine of claim 11/medium of claim 18, wherein generating the augmented prompt comprises:
generating a current conversation context from a current conversation between the user and the chatbot (Smith Lewis: Paragraph [0076], “conversation history may be stored hierarchically, e.g., by summarizing a full conversation (series of messages within some time frame such as the past hour, or about some related set of topics), and embedding this either in place of or in addition to the messages comprising that conversation. In this way longer histories may be efficiently searched by reference to the conversation summaries, or full relevant conversations may be retrieved and inserted into the system prompt rather than only snippets and individual message”);
retrieving, from the memories datastore, one or more stored memories using the current conversation context (Smith Lewis: Paragraph [0045], “the conversation history between the system and the user, or a subset of this history identified as important either by the user or by a machine learning model, or a set of higher level summaries or other abstractions of the user's conversation history, or similar text, audio or visual representations of the user's history with the system, may be embedded as indexed vectors in a vector database or similar storage structure for later reference by the system”, and Paragraph [0052], “, this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”);
and generating the augmented prompt using the one or more stored memories (Smith Lewis: Paragraph [0076], “conversation history may be stored hierarchically, e.g., by summarizing a full conversation (series of messages within some time frame such as the past hour, or about some related set of topics), and embedding this either in place of or in addition to the messages comprising that conversation. In this way longer histories may be efficiently searched by reference to the conversation summaries, or full relevant conversations may be retrieved and inserted into the system prompt rather than only snippets and individual message”).
Smith Lewis teaches 7/14/20. The method of claim 6/machine of claim 13/medium of claim 19, wherein the one or more memories stored in the memories datastore are isolated for a first conversation group, and wherein the one or more memories are not used to generate the augmented prompt for a second conversation group (Smith Lewis: Paragraph [0045], “the conversation history between the system and the user, or a subset of this history identified as important either by the user or by a machine learning model, or a set of higher level summaries or other abstractions of the user's conversation history, or similar text, audio or visual representations of the user's history with the system, may be embedded as indexed vectors in a vector database or similar storage structure for later reference by the system”, and Paragraph [0052], “, this embedding-retrieval pipeline may be applied to Retrieval Augmented Generation (RAG) whereby a targeted search across the embedded vector database is performed in response to a user query, e.g., in order to produce context for a conversational agent to then generate a response, for example by inserting the retrieved text chunks into a system prompt or message used to generate the agent's response to the user”; the memories/embeddings can be user specific, so a first user’s memories are used for the first user’s conversations but not for a second user’s conversations).
Claim(s) 1-4 and 6-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Taylert (US 11960514 B1).
Regarding claims 1, 8, and 15, Taylert discloses A method/machine/machine-storage medium… (Taylert: Column 22 Lines 10-30, “method or process, the subject matter also relates to apparatus for performing the operations herein. This apparatus may be a particular machine that is specially constructed for the required purposes, or it may comprise a computer otherwise selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium… A machine implementing the techniques herein comprises a hardware processor, and non-transitory computer memory holding computer program instructions that are executed by the processor to perform the above-described methods”), comprising:
one or more processors (Taylert: Column 22 Lines 10-30, “method or process, the subject matter also relates to apparatus for performing the operations herein. This apparatus may be a particular machine that is specially constructed for the required purposes, or it may comprise a computer otherwise selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium… A machine implementing the techniques herein comprises a hardware processor, and non-transitory computer memory holding computer program instructions that are executed by the processor to perform the above-described methods”);
and one or more memories storing instructions that, when executed by the one or more processors, cause the machine to perform operations (Taylert: Column 22 Lines 10-30, “method or process, the subject matter also relates to apparatus for performing the operations herein. This apparatus may be a particular machine that is specially constructed for the required purposes, or it may comprise a computer otherwise selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium… A machine implementing the techniques herein comprises a hardware processor, and non-transitory computer memory holding computer program instructions that are executed by the processor to perform the above-described methods”) comprising:
receiving, by one or more processors, a user prompt from a user (Taylert: Column 15 Lines 36-63, “assume that the generative chat application is running on an endpoint (identified at the URL 900), and the user has asked the following prompt: “how can Drift help me create revenue?””);
determining, by the one or more processors, a conversation history of one or more conversations between the user and a chatbot (Taylert: Column 18 Lines 30-56, “the incoming utterance is passed through the embeddings service 1512, which identifies an embedding and sends it to the vector database 1514 to retrieve the context… the vector database 1514 stores all context and conversation history created and uploaded by the data pipelines 1518. The data pipelines update the vector database with new data. In this operating embodiment, the data pipelines comprise several data sources. The conversation history job 1522 collects chat logs that are saved for customers of the platform. These logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs”);
generating, by the one or more processors, an augmented prompt using the user prompt and the conversation history (Taylert: Column 15 Lines 36-63, “When the post-request is sent to the endpoint 900, the endpoint passes the prompt value through the semantic-search API to find a relevant context. As noted above, the semantic search and retrieval techniques may be used for this purpose, although this is not a limitation. The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3”);
communicating, by the one or more processors, the augmented prompt to a generative AI model (Taylert: Column 15 Lines 50-63, “The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3. This particular language model is not intended to be limiting, as other large language models may be used for this purpose. Upon receiving a response from the generative AI text completion endpoint, the suggested replies API returns it, e.g., to a front-end application tool that is managing the conversation (namely, the interaction between the user and the live-agent”);
receiving, by the one or more processors, a response from the generative AI model to the augmented prompt (Taylert: Column 15 Lines 50-63, “The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3. This particular language model is not intended to be limiting, as other large language models may be used for this purpose. Upon receiving a response from the generative AI text completion endpoint, the suggested replies API returns it, e.g., to a front-end application tool that is managing the conversation (namely, the interaction between the user and the live-agent”);
and providing, by the one or more processors, the response to the user (Taylert: Column 15 Lines 50-63, “The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3. This particular language model is not intended to be limiting, as other large language models may be used for this purpose. Upon receiving a response from the generative AI text completion endpoint, the suggested replies API returns it, e.g., to a front-end application tool that is managing the conversation (namely, the interaction between the user and the live-agent”, and Figure 4).
Taylert teaches 2. The method of claim 1, wherein determining the conversation history comprises:
retrieving, from a conversation history datastore, a conversation history of one or more conversations between a user and a chatbot (Taylert: Column 16 Line 53-Column 17 Line 8, “the suggested replies tool provides for conversation history caching. In particular, and referring back to FIG. 8, the system may cache conversation turns (e.g., in database or other data store) 818 and append the history into the prompt sent to the generative AI. In the example embodiment, this history is retrieved by the generative chat API issuing a request 820 to the database. By affording this history, the generative AI potentially provides more accurate replies. More generally, the goal of this aspect of the tool is to improve the accuracy of suggested-replies by caching conversation history and using it to generate more contextually relevant responses. A conversational history cache stores in a database the text of previous user inputs and replies from either the generative model or the live-agent. Each conversation is associated with a unique conversation ID, which is then used to retrieve the relevant history when generating a response. When user input is received, the generative-chat-application first checks the conversation history cache to see if there is any relevant conversation history. If so, the conversation history is passed to the response generation module along with the current user input and the context found from semantic-search to generate a contextually-relevant response”).
Taylert teaches 3. The method of claim 1, further comprising:
generating one or more summarized memories using the conversation history (Taylert: Column 18 Lines 30-56, “the incoming utterance is passed through the embeddings service 1512, which identifies an embedding and sends it to the vector database 1514 to retrieve the context… the vector database 1514 stores all context and conversation history created and uploaded by the data pipelines 1518. The data pipelines update the vector database with new data. In this operating embodiment, the data pipelines comprise several data sources. The conversation history job 1522 collects chat logs that are saved for customers of the platform. These logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs”).
Taylert teaches 4. The method of claim 3, further comprising:
generating one or more moderated memories using the summarized memories (Taylert: Figure 8 and Column 18 Lines 30-56, “the incoming utterance is passed through the embeddings service 1512, which identifies an embedding and sends it to the vector database 1514 to retrieve the context… the vector database 1514 stores all context and conversation history created and uploaded by the data pipelines 1518. The data pipelines update the vector database with new data. In this operating embodiment, the data pipelines comprise several data sources. The conversation history job 1522 collects chat logs that are saved for customers of the platform. These logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs”);
and storing the one or more moderated memories into a memories datastore (Taylert: Column 18 Lines 30-56, “the incoming utterance is passed through the embeddings service 1512, which identifies an embedding and sends it to the vector database 1514 to retrieve the context… the vector database 1514 stores all context and conversation history created and uploaded by the data pipelines 1518. The data pipelines update the vector database with new data. In this operating embodiment, the data pipelines comprise several data sources. The conversation history job 1522 collects chat logs that are saved for customers of the platform. These logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs”).
Taylert teaches 6. The method of claim 4, wherein generating the augmented prompt comprises:
generating a current conversation context from a current conversation between the user and the chatbot (Taylert: Column 18 Lines 30-56, “the incoming utterance is passed through the embeddings service 1512, which identifies an embedding and sends it to the vector database 1514 to retrieve the context… the vector database 1514 stores all context and conversation history created and uploaded by the data pipelines 1518. The data pipelines update the vector database with new data. In this operating embodiment, the data pipelines comprise several data sources. The conversation history job 1522 collects chat logs that are saved for customers of the platform. These logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs”);
retrieving, from the memories datastore, one or more stored memories using the current conversation context (Taylert: Column 18 Lines 30-56, “the incoming utterance is passed through the embeddings service 1512, which identifies an embedding and sends it to the vector database 1514 to retrieve the context… the vector database 1514 stores all context and conversation history created and uploaded by the data pipelines 1518. The data pipelines update the vector database with new data. In this operating embodiment, the data pipelines comprise several data sources. The conversation history job 1522 collects chat logs that are saved for customers of the platform. These logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs”);
and generating the augmented prompt using the one or more stored memories (Taylert: Column 15 Lines 36-63, “When the post-request is sent to the endpoint 900, the endpoint passes the prompt value through the semantic-search API to find a relevant context. As noted above, the semantic search and retrieval techniques may be used for this purpose, although this is not a limitation. The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3”).
Taylert teaches 7. The method of claim 6, wherein the one or more memories stored in the memories datastore are isolated for a first conversation group, and wherein the one or more memories are not used to generate the augmented prompt for a second conversation group (Taylert: Column 18 Lines 45-56, “these logs are queried periodically (e.g., daily), embedded, and then stored in the vector database by a historical conversation query service 1524, which service also is responsible for selectively pulling chat-logs associated with an organization identifier (org-ID)”; the memories/embeddings are organization specific, so a first organization’s memories are used for the first organization’s conversations but not for a second organization’s conversations).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Taylert (US 11960514 B1) as applied to claim 4 above, and further in view of Burgess (“How to Delete Your Data From ChatGPT”).
Taylert teaches 5. The method of claim 4.
Taylert does not explicitly disclose receiving a request from the user to delete the conversation history, and in response to receiving the request, deleting the conversation history and one or more memories stored in the memories datastore associated with the conversation history.
However, Burgess discloses this feature (Burgess: Page 6, turning off chat history deletes all conversation history and memories; ChatGPT also allows for more fine-grained deletion of conversations/memories).
Taylert and Burgess are analogous art in the same field of endeavor as the instant invention as all are drawn to generative AI LLM systems. 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; that is, it would have been obvious to incorporate Brugess’ deletion mechanism into the system of Taylert to allow for greater privacy and security.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Grimshaw (US 11947902 B1) describes a system for generating messages for a user using a generative AI prompted with a predefined phrase, a shortened summary of previous conversations, and a user text selection.
Hawes (US 2024/0403194 A1) describes a system for improving LLM prompting using conversation context and history, stored in whole, in part, or in summary within a database.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMAD HUSSAIN whose telephone number is (571)270-3628. The examiner can normally be reached Monday-Friday 0900-1700 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamal Divecha can be reached at (571) 272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IMAD HUSSAIN/Primary Examiner, Art Unit 2453
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