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 a mental process without significantly more.
Independent claims 1, 11, and 20 regard a process that, as drafted under its broadest reasonable interpretation (BRI), covers maintaining a conversation history where conversation segments are sorted by topic in an ongoing conversation session and providing a response output message based on a generative language model. For example, under the BRI the method claim relates to:
maintaining a conversation history for an ongoing conversation session, the conversation history containing conversation segments of the ongoing conversation session, each conversation segment being associated with at least one topic, and each conversation segment including one or more previous messages in the ongoing conversation session (a person can maintain a conversation history mentally or via a pen and paper with the above specifications); receiving a new message for the ongoing conversation session (a person can receive a new message in an ongoing conversation out loud or via pen and paper); determining one or more topics associated with the new message (a person can determine the topic associated with a new conversation message mentally or via a pen and paper); filtering the conversation history based on relevance to the one or more topics associated with the new message to obtain a filtered conversation history having at least one relevant conversation segment associated with at least one topic that is relevant to the one or more topics associated with the new message; providing a prompt to a generative language model based on the filtered conversation history and the new message (A person can filter the conversation history mentally or with a pen and paper as specified above); and providing an output message based on output generated by the generative language model in response to the prompt (A person can relay a message based on a generative language model output (ex. ChatGPT output)).
As described above, these limitations can be carried out as a series of mental steps. The judicial exception is not integrated into a practical application because the only additional elements recited are a system comprising of a computer processor and memory, which is general purpose hardware being used as a tool to implement the mental process, and non-transitory computer-readable program code that is conventional components that utilizes the basic functions of a computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as described above, the only additional elements recited are a system comprising of a computer processor and memory, which is general purpose hardware being used as a tool to implement the mental process, and non-transitory computer-readable program code that is conventional components that utilizes the basic functions of a computer.
The remaining dependent claims fail to add patent eligible subject matter to independent claim 1:
Claims 2, 12 simply adds a temporal closeness requirement for better storage of the message which a human can do mentally and/or with a pen and paper.
Claims 3, 13 simply adds another temporal closeness requirement for better storage of the message which a human can do mentally and/or with a pen and paper.
Claims 4, 14 simply adds a check for overlapped segments being associated with different topics which a human can do mentally and/or with a pen and paper.
Claims 5, 15 simply adds a check for where previously stored messages are temporally consecutive messages in temporal order which a human can determine mentally and/or with a pen and paper.
Claims 6, 16 simply adds a filtering step based on topical similarity and the exclusion of irrelevant information, which is a routine mental process of "categorizing and discarding information" that a human can perform with a pen and paper.
Claims 7, 17 simply adds the generation of a summary for excluded information to inform a response, which is a conventional method of "abstracting and condensing data" that a human can perform mentally and/or with a pen and paper.
Claim 8 simply adds the maintenance and querying of a historical database associated with a specific account, which is a basic "record-keeping" task and "information retrieval" process that a human librarian or clerk can perform mentally and/or with a pen and paper.
Claims 9, 18 simply adds the use of a sliding window of recent messages to determine a topic, which is a standard "contextual review" of a limited set of recent notes that a human can perform mentally and/or with a pen and paper.
Claims 10, 19 simply adds the clustering of messages into topical groups and assigning a new message to a specific group, which is a fundamental "sorting and indexing" activity—assigning a new document to a pre-existing folder based on subject matter—that a human can perform mentally and/or with a pen and paper.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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) 1, 5, 6, 7, 8, 10, 11, 15, 16, 17, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (hereinafter Smith) (US 20240289863 A1) in view of Zhang et al. (hereinafter Zhang) (CN 111414462 A).
Regarding claim 1, Smith teaches:
A computer-implemented method comprising (Smith, P[0052]: "In some embodiments, 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", reads on a computer-implemented method for data processing.):
maintaining a conversation history for an ongoing conversation session, the conversation history containing conversation segments of the ongoing conversation session(Smith, P[0074]: "In some embodiments, conversational history may be searched at runtime, and relevant information inserted into the system prompt at runtime, as described herein" and P[0076]: "In some embodiments, 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", reads on maintaining history for an active, ongoing session. The use of "runtime" and "time frame such as the past hour" identifies that the system is managing an active, live interaction session.)
filtering the conversation history based on relevance to the one or more topics associated with the new message to obtain a filtered conversation history (Smith, P[0074]: "In some embodiments, conversational history may be searched at runtime, and relevant information inserted into the system prompt at runtime, as described herein" and P[0075]: "For example, a customer asking a question of a customer service conversational agent may prompt a query across past conversations with that user to find related issues", reads on filtering history in real-time to identify segments that match the topical "issue" or semantic relevance of the new message.) having at least one relevant conversation segment associated with at least one topic that is relevant to the one or more topics associated with the new message(Smith, P[0076]: "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 messages", reads on obtaining a complete conversation segment or message burst that is topically relevant to the determined topic of the new query.)
providing a prompt to a generative language model based on the filtered conversation history and the new message (Smith, P[0052]: "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", reads on constructing a prompt for a generative model using both the retrieved/filtered history and the current user message.)
providing an output message based on output generated by the generative language model in response to the prompt (Smith, P[0052]: "For example, in some embodiments, 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", reads on providing a final output message generated by the model in response to the contextually enriched prompt.)
Smith does not explicitly teach:
each conversation segment being associated with at least one topic, and each conversation segment including one or more previous messages in the ongoing conversation session;
receiving a new message for the ongoing conversation session;
However, Zhang teaches:
each conversation segment being associated with at least one topic, and each conversation segment including one or more previous messages in the ongoing conversation session (Zhang, Page 18: "when determining the first current dialogue topic, and considering that the acceptation similarity with the history dialogue topic, and acceptation similarity of dialogue with the target topic, determining the topic is more proper and improves the accuracy of first current dialogue topic determination and improves the current dialogue topic by first determining the accuracy of the first dialogue sentence. further considering the accuracy target user interest degree of each candidate dialogue topic, the first topic is determined from the current dialogue can better meet the demand of the user and improves the first current dialogue topic in the process for determining the first current dialogue topic.", reads on analyzing the content and similarity of the "history dialogue topic" and the "current dialogue" to determine a primary topic for that segment.)
receiving a new message for the ongoing conversation session (Zhang, Page 18: "can use the similarity history dialogue topic and target dialogue topic to determine whether further to continue to provide the first current dialogue topic. under the condition that the similarity is not high can continuously provide first current dialogue topic to the user, while the very high similarity, the can ends discussed above for the current target dialogue topic, and sends the corresponding information to the user, such a message pushing mode can improve the influence of the push message. and the user knows the message-related information to be pushed, is reduced because the user suddenly receiving the objectionable degree caused by the message to be pushed in the process of continuously alternating.", reads on a system that operates within a continuous, "continuously alternating" dialogue process where the system is constantly receiving and processing new user inputs and making decisions in real-time.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith in view of Zhang. Doing so would have provided the high-level architecture for maintaining chatbot conversation history of Smith (Smith, Abstract) with the real time topic determination techniques of Zhang (Zhang, Abstract) thus improving the efficient handling of new conversation segments being grouped into topics in a real-time conversation with an AI chatbot.
Regarding claim 5, Smith in view of Zhang teaches the method according to claim 1.
Smith further teaches:
wherein the one or more previous messages stored in each conversation segment are temporally consecutive messages stored in temporal order (Smith, P[0074]: "Past conversations may be stored as message histories, for example as ordered lists of messages and responses between the user and an agent", reads on storing previous messages in temporal order as ordered lists. Smith, P[0076]: "In some embodiments, 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", reads on segments consisting of temporally consecutive messages within a specific time frame.)
Regarding claim 6, Smith in view of Zhang teaches the method according to claim 1.
Smith further teaches:
wherein filtering the conversation history comprises:
identifying the at least one relevant topic based on a measure of similarity between the at least one relevant topic and the one or more topics associated with the new message; and
excluding at least some conversation segments in the conversation history that are associated with topics other than the at least one relevant topic (Smith, P[0050]: "In some embodiments, chunks may then be retrieved in response to a natural language query, for example, by treating that query in the same or a similar way as described herein (e.g., preprocessing the query and then passing it through the same embedding model used to embed the original documents), and comparing the resulting vector to the vectors stored in the database, e.g., using a similarity measure such as cosine distance", reads on identifying relevance based on a measure of similarity. Smith, P[0052]: "In some embodiments, additional filtering steps, for example against the metadata for each chunk, may be performed prior to vector comparison, so that manual or automated tags and other metadata may be taken into account alongside the meaning and content of the text", reads on filtering and excluding segments that do not match the metadata or topical tags.).
Regarding claim 7, the combination of Smith and Zhang teaches the method according to claim 6.
Zhang further teaches:
further comprising:
generating a summary of at least one of the excluded conversation segments (Zhang, Pages 4, 18: "If the dialogue is higher than predetermined value abnormal degree, then recording the dialogue field target object not of interest according to the history dialogue" and "while the very high similarity, the can ends discussed above for the current target dialogue topic", reads on identifying segments to be excluded based on redundancy or abnormality. The "recording" of a "dialogue field" as "not of interest" functions as a generated summary or abstraction of the excluded segments.);
wherein the prompt provided to the generative language model is further based on the generated summary (Zhang, Page 18: "respectively to obtain corresponding candidate sentence by dialogue sentence library and sentence generation model two ways" and "namely can through changing the current dialogue topic to change determination policy of dialogue sentence", reads on the prompt or input to the generative sentence model being based on the generated topic summary. By using the recorded interest field and history topic to set the determination policy, the system ensures the generative output is based on the context of the excluded segments.).
Regarding claim 8, Smith in view of Zhang teaches the method according to claim 1.
Smith further teaches: wherein the ongoing conversation session is associated with an account, the method further comprising:
maintaining a historical database containing historical messages from one or more historical conversation sessions associated with the account, the historical database containing historical conversation segments that each belong to a respective historical conversation session, each historical conversation segment being associated with at least one topic, and each historical conversation segment including one or more historical messages of the respective historical conversation session; and
identifying at least one historical conversation segment associated with the at least one relevant topic that is relevant to the one or more topics associated with the new message;
wherein the prompt provided to the generative language model is further based on the identified at least one historical conversation segment (Smith, P: "In some embodiments, user interactions may be further personalized by reference to a long-term personal conversation memory, specific to each user. Past conversations may be stored as message histories, for example as ordered lists of messages and responses between the user and an agent. These conversation histories may be embedded in the same vector space" and Smith, P: "For example, a customer asking a question of a customer service conversational agent may prompt a query across past conversations with that user to find related issues, such as a series of steps the customer has already attempted in the past to resolve the issue", reads on maintaining a historical database of past sessions for a specific user account and identifying relevant historical segments to include in the generative prompt.)
Regarding claim 10, Smith in view of Zhang teaches the method according to claim 1.
Zhang further teaches: wherein:
previous messages in the ongoing conversation session are clustered, each cluster corresponding to a conversation segment associated with at least one topic (Zhang, P: "perform topic segmentation of the dialogue through clustering and quantify the key information in each utterance, thereby capturing the dialogue topics more effectively", reads on previous messages being clustered into segments where each cluster represents a conversation segment associated with a topic.);
determining the one or more topics associated with the new message comprises:
using a clustering algorithm to cluster the new message with a particular cluster (Zhang, P: "propose an iterative clustering algorithm that facilitates cluster-level refinement and the continuous discovery of high-quality intent clusters", reads on using a clustering algorithm to group a new message with a specific cluster.); and
determining the one or more topics associated with the new message based on the at least one topic associated with the conversation segment corresponding to the particular cluster (Zhang, P: "assigning intent labels to each dialog turn (intent clustering). DASH-DTS provides interpretable reasoning and confidence scores for each segment", reads on determining the topic of a new message based on the existing label of the cluster or segment it joins.); and
filtering the conversation history comprises:
selecting the conversation segment corresponding to the particular cluster as the filtered conversation history (Zhang, P: "topic segmentation approach improves the focus on relevant topics, ensuring that the generated summaries align more closely with user needs", reads on selecting the specific relevant cluster or segment to serve as the filtered context for the response.).
Regarding claim 11, claim 11 recites the computer system corresponding to the method presented in claim 1 and is rejected under the same grounds stated above.
Additionally, the combination further discloses or makes obvious:
A computer system comprising (Smith, P[0149] - P[0151]):
at least one processor (Smith, P[0151]); and
a computer readable medium storing instructions that, when executed by the at least one processor, cause the computer system to (Smith, P[0151]):
Regarding claim 15, claim 15 recites the system corresponding to the method presented in claim 5 and is rejected under the same grounds as above.
Regarding claim 16, claim 16 recites the system corresponding to the method presented in claim 6 and is rejected under the same grounds as above.
Regarding claim 17, claim 17 recites the system corresponding to the method presented in claim 7 and is rejected under the same grounds as above.
Regarding claim 18, claim 18 recites the system corresponding to the method presented in claim 9 and is rejected under the same grounds as above.
Regarding claim 19, claim 19 recites the system corresponding to the method presented in claim 10 and is rejected under the same grounds as above.
Regarding claim 20, claim 20 recites the non-transitory computer-readable medium storing instructions that when executed correspond to the method presented in claim 1 and is rejected under the same grounds stated above.
Additionally, the combination further discloses or makes obvious:
A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computer system, cause the computer system to (Smith, P[0019], P[0151]):
Claim(s) 2, 3, 4, 12, 13, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (hereinafter Smith) (US 20240289863 A1) in view of Zhang et al. (hereinafter Zhang) (CN 111414462 A) in further view of Bern et al. (hereinafter Bern) (US 20170272388 A1)
Regarding claim 2, Smith in view of Zhang teaches the method according to claim 1.
Smith in view of Zhang does not teach:
further comprising:
determining, based on the one or more topics associated with the new message, that a particular conversation segment in the conversation history that is temporally closest to the new message is associated with at least one topic that is similar to or same as at least one of the one or more topics associated with the new message; and
storing the new message to the particular conversation segment in the conversation history.
Bern teaches:
further comprising:
determining, based on the one or more topics associated with the new message, that a particular conversation segment in the conversation history that is temporally closest to the new message is associated with at least one topic that is similar to or same as at least one of the one or more topics associated with the new message (Bern, P[0020]: "In Phase I, emails that are part of the same exchange and are associated with one mailbox are linked together to create a local virtual conversation of the one mailbox" and "In some embodiments, filters may be employed to filter email contents based on participants, subject, attachments, and the like", reads on determining similarity between a new message and a particular conversation segment based on topic or subject similarity. Bern, P[0020]: "UEP system is a communication and collaboration platform that creates a virtual single-threaded conversation that allows interactions between various communication clients, whether UEP clients or existing/different platform clients, as a real-time, ongoing stream of communications, in which people may enter an ongoing conversation in the computing cloud", reads on identifying a particular conversation segment that is the current ongoing stream and thus temporally closest to the new message.);; and
storing the new message to the particular conversation segment in the conversation history (Bern, P[0020]: "In various embodiments, the cloud-based conversation may be split or nested allowing side conversations to separate from the main session", reads on storing a new message to a specific particular conversation segment by nesting it within the identified main session.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith in view of Zhang and in further view of Bern. Doing so would have provided the “temporal” sorting of Bern (Bern, P[0020]) with the high-level architecture for maintaining chatbot conversation history of Smith (Smith, Abstract) with the real time topic determination techniques of Zhang (Zhang, Abstract) thus improving the efficient handling of new conversation segments being grouped into topics in a real-time conversation with an AI chatbot as well as the contextual understanding of the AI chatbot for better responses.
Regarding claim 3, Smith in view of Zhang teaches the method according to claim 1.
Smith in view of Zhang does not teach:
determining, based on the one or more topics associated with the new message, that all of the at least one topic associated with a particular conversation segment in the conversation history that is temporally closest to the new message are dissimilar to the one or more topics associated with the new message;
creating a new conversation segment in the conversation history associated with the one or more topics associated with the new message; and
storing the new message to the new conversation segment;
Bern teaches:
determining, based on the one or more topics associated with the new message, that all of the at least one topic associated with a particular conversation segment in the conversation history that is temporally closest to the new message are dissimilar to the one or more topics associated with the new message (Bern, P[0020]: "In some embodiments, filters may be employed to filter email contents based on participants, subject, attachments, and the like", reads on determining that a new message does not belong to the current thread because the filtered topic/subject is not a match.);
creating a new conversation segment in the conversation history associated with the one or more topics associated with the new message (Bern, P[0020]: "In various embodiments, the cloud-based conversation may be split or nested allowing side conversations to separate from the main session", reads on creating a new, separate conversation segment when the filter determines the content should be split from the main session.); and
storing the new message to the new conversation segment (Bern, P[0020]: "allowing side conversations to separate from the main session", reads on storing the new message into the newly created separate side conversation segment.);
Regarding claim 4, Smith in view of Zhang teaches the method according to claim 1.
Smith in view of Zhang does not teach:
wherein at least two conversation segments in the conversation history that are associated with at least two respective different topics have at least one overlapping message in common, the at least one overlapping message being associated with both of the at least two respective different topics.
Bern teaches:
wherein at least two conversation segments in the conversation history that are associated with at least two respective different topics have at least one overlapping message in common, the at least one overlapping message being associated with both of the at least two respective different topics (Bern, P[0020]: "In various embodiments, the cloud-based conversation may be split or nested allowing side conversations to separate from the main session", reads on a message structure where a "nested" side conversation remains logically linked to the main session, creating an overlapping relationship where a parent message can be common to both the main session segment and the nested/split segment.).
Regarding claim 12, claim 12 recites the system corresponding to the method presented in claim 2 and is rejected under the same grounds as above.
Regarding claim 13, claim 13 recites the system corresponding to the method presented in claim 3 and is rejected under the same grounds as above.
Regarding claim 14, claim 14 recites the system corresponding to the method presented in claim 4 and is rejected under the same grounds as above.
Claim(s) 9, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (hereinafter Smith) (US 20240289863 A1) in view of Zhang et al. (hereinafter Zhang) (CN 111414462 A) in further view of Singh et al. (hereinafter Singh) (US 11144730 B2).
Regarding claim 9, Smith in view of Zhang teaches the method according to claim 1.
Smith in view of Zhang does not teach:
wherein determining the one or more topics associated with the new message comprises:
using a sliding window to define a defined number of one or more most recent messages; providing the new message together with the one or more most recent messages to a trained model; and
receiving the one or more topics as output from the trained model.
Singh teaches:wherein determining the one or more topics associated with the new message comprises:
using a sliding window to define a defined number of one or more most recent messages (Singh, [0063]: "a sliding window is used to identify a prior group of one-three questions and one-three answers that precede the current user question in a conversation session between the user and the automated assistant," reads on using a sliding window to define a defined number of one or more most recent messages.); providing the new message together with the one or more most recent messages to a trained model (Singh, [0063]: "the user question and the prior group of one-three questions and one-three answers are converted to a conversation vector by an answer prediction model," reads on providing the new message together with the one or more most recent messages to a trained model.); and
receiving the one or more topics as output from the trained model (Singh, [0063]: "to identify the reason why the user is asking the user question based on the context of the conversation session," reads on receiving the one or more topics, such as the reason or intent, as output from the trained model.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith in view of Zhang and in further view of Singh. Doing so would have provided the sliding window mechanism of Singh (Singh, P[0063]) with the high-level architecture for maintaining chatbot conversation history of Smith (Smith, Abstract) with the real time topic determination techniques of Zhang (Zhang, Abstract) thus improving the identification of user intent and providing efficient handling of new conversation segments being grouped into topics in a real-time conversation with an AI chatbot.
Regarding claim 18, claim 18 recites the system corresponding to the method presented in claim 9 and is rejected under the same grounds as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHASHIDHAR S MANOHARAN whose telephone number is (571)272-6772. The examiner can normally be reached M-F 8:00-4:00.
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/SHASHIDHAR SHANKAR MANOHARAN/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655