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
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more. The claims as whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Independent claims 1 and 11 recite “for each of one or more interactions of a chat session between a user and a chatbot, performing a building phase that comprises mapping the interaction into either a graph or an n-dimensional space; performing a verification phase that comprises partitioning the chat session; and using partitions of the chat session obtained during the verification phase to generate a subject-wise summarization of the chat session and/or to generate multiple chat sessions.” The bolded limitations as drafted cover a mental process when a human is in a conversation with another person, and writes the sentences as spoken as interactions on a graph with words connected, each sentence being its own graph, circling graphs that are about similar topics, and writing next to each one of the circles a summary of what the sentences entail.
This judicial exception is not integrated into a practical application. In particular claim 11 recites additional element of processor, which is a form of generic computer equipment. In the as-filed Specifications ¶ [00103] The embodiments disclosed herein may include the use of a special purpose or general-purpose computer including various computer hardware or software modules, as discussed in greater detail below. A computer may include a processor and computer storage media carrying instructions that, when executed by the processor and/or caused to be executed by the processor, perform any one or more of the methods disclosed herein, or any part(s) of any method disclosed. . Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do 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 element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2 and 12 recite wherein the mapping comprises mapping the interactions into respective node representations This amounts to the human doing writing each interaction starting at a node. No additional limitations are present.
Claims 3 and 13 recite wherein mapping the interactions into node representations comprises: computing a vector embedding for each of the interactions; comparing a respective distance of each of the vector embeddings with respective distances of existing nodes in a set of interactions, where each of the distances represents a respective similarity of one of the interactions with another of the interactions This amounts to the human representing each interaction as a number which represents the words in the interaction, and in a matrix comparing number for each new interaction with number with other interactions on the graph and adding the new interaction to the graph that was within 5 count, and starting a new node if the count was larger than 5. No additional limitations are present.
Claims 4 and 14 recite wherein when one of the distances between two of the interactions is below a threshold, the nodes representing the two interactions are merged into a single node. This amounts to the human representing each interaction as a number which represents the words in the interaction, and in a matrix comparing number for each new interaction with number with other interactions on the graph and adding the new interaction to the graph that was within 5 count, and starting a new node if the count was larger than 5. No additional limitations are present.
Claims 5 and 15 recite wherein when the interactions are mapped into a graph, the verification phase triggers use of a community detection algorithm that partitions the graph into mutually similar vertices which each correspond to a different respective subject of the chat session. This amounts to the human circling all graphs that are similar. No additional limitations are present.
Claims 6 and 16 recite wherein the mapping comprises using a clustering algorithm to map the interactions into respective data points of the n-dimensional space This amounts to the human circling all interactions that are somewhat similar. No additional limitations are present.
Claims 7, and 17 recite wherein the subject-wise summarization comprises multiple summaries, each corresponding to a different respective subject of the chat session This amounts to the human doing writing summary of all the circled graphs. No additional limitations are present.
Claims 8 and 18 recite wherein the building phase and the verification phase are performed for each interaction of the chat session This amounts to the human doing writing summary of all the circled graphs and writing all the interactions one after the other. No additional limitations are present.
Claims 9 and 19 recite wherein each interaction comprises a respective query submitted by the user, and an answer to the query, and the answer is generated by an LLM (large language model) of the chatbot This amounts to the human answering a question from another person. The additional element of LLM is mapped to generic computer hardware because of a lack of specificity. No additional limitations are present.
Claims 10 and 20 recite wherein the building phase and the verification phase are performed while maintaining adherence to a token budget of an LLM (large language model) of the chatbot. This amounts to the human recording the interactions on a graph such that only the first 10 words of each conversation are recorded. The additional element of LLM is mapped to generic computer hardware because of a lack of specificity. No additional limitations are present.
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.
Claim(s) 1, 7, 8, 11, 17 and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Breedwelt ( US 20250355883 A1).
Regarding claims 1, and 11 Breedwelt teaches (claim 1) A method, comprising:
(claim 11 A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising (Breedwelt ¶[0019] A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions [instruction] and/or data for performing computer operations specified in a given CPP claim... A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory [non-transitory] signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media).
for each of one or more interactions of a chat session between a user and a chatbot, performing a building phase that comprises mapping the interaction into either a graph or an n-dimensional space (Breedwelt ¶[0036] Initially, AI conversation [interactions] code 200 identifies and tracks data topics during a human-artificial intelligence (AI) conversation or other communication session (e.g., chat, conversation, thread, etc.). AI conversation code 200 generates an interactive, natural language, conversation or other communication session 215 with a user 205. User 205 may provide user input 210 to the conversation (e.g., an inquiry, further details and/or inquiries, responses, etc.). AI conversation code 200 may include any conventional or other chatbot, [chatbots] ¶[0040] Concept extractor 230 extracts the entities and business intelligence concepts from user input 210... ¶[0041] Topic coordinator 220 converts the extracted entities to textual , and searches the existing topics stored in embedding database 245);
performing a verification phase that comprises partitioning the chat session (Breedwelt ¶[0047] Once the topic segments [partitioning] are generated, they may be used for various future analysis at operation 315 (e.g., generating summaries [subject-wise summarization] for topics, eliciting further inquiries, etc.). Topics within plural different conversations may be monitored and tracked in substantially the same manner described above. For example, a same topic may be within different conversations, and a plurality of segments from the different conversations may be assigned to the same topic. Thus, a search for the topic may provide the segments from the different conversations corresponding to the topic); and
using partitions of the chat session obtained during the verification phase to generate a subject-wise summarization of the chat session and/or to generate multiple chat sessions (Breedwelt ¶[0047] Once the topic segments [partitioning] are generated, they may be used for various future analysis at operation 315 (e.g., generating summaries [subject-wise summarization] for topics, eliciting further inquiries, etc.). Topics within plural different conversations may be monitored and tracked in substantially the same manner described above. For example, a same topic may be within different conversations, and a plurality of segments from the different conversations may be assigned to the same topic. Thus, a search for the topic may provide the segments from the different conversations corresponding to the topic)
Regarding claims 7, and 17 Breedwelt teaches wherein the subject-wise summarization comprises multiple summaries, each corresponding to a different respective subject of the chat session (Breedwelt ¶[0047] Once the topic segments [partitioning] are generated, they may be used for various future analysis at operation 315 (e.g., generating summaries [subject-wise summarization] for topics, eliciting further inquiries, etc.). Topics within plural different conversations may be monitored and tracked in substantially the same manner described above. For example, a same topic may be within different conversations, and a plurality of segments from the different conversations may be assigned to the same topic. Thus, a search for the topic may provide the segments from the different conversations corresponding to the topic).
Regarding claims 8, and 18 Breedwelt teaches wherein the building phase and the verification phase are performed for each interaction of the chat session (Breedwelt ¶[0036] Initially, AI conversation [interactions] code 200 identifies and tracks data topics during a human-artificial intelligence (AI) conversation or other communication session (e.g., chat, conversation, thread, etc.). AI conversation code 200 generates an interactive, natural language, conversation or other communication session 215 with a user 205. User 205 may provide user input 210 to the conversation (e.g., an inquiry, further details and/or inquiries, responses, etc.). AI conversation code 200 may include any conventional or other chatbot, [chatbots] ¶[0040] Concept extractor 230 extracts the entities and business intelligence concepts from user input 210... ¶[0041] Topic coordinator 220 converts the extracted entities to textual , and searches the existing topics stored in embedding database 245, ¶[0047] Once the topic segments [partitioning] are generated, they may be used for various future analysis at operation 315 (e.g., generating summaries [subject-wise summarization] for topics, eliciting further inquiries, etc.). Topics within plural different conversations may be monitored and tracked in substantially the same manner described above. For example, a same topic may be within different conversations, and a plurality of segments from the different conversations may be assigned to the same topic. Thus, a search for the topic may provide the segments from the different conversations corresponding to the topic).
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 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.
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 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.
Claim(s) 2, 3, 4, 12, 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Breedwelt in view of Yuan ( US 20230325707 A1).
With respect to claims 2 and 12 Breedwelt does not explicitly disclose however Yuan teaches wherein the mapping comprises mapping the interactions into respective node representations (Yuan ¶[0023] However, in some implementations, there is no node [node] with an embedding space representation within the threshold distance to the embedding space representation of the attribute. FIG. 2C illustrates an embedding space representation of agricultural attribute [interaction] C 240, the space representation of an eighth node 242 );
Yuan is considered to be analogous to the claimed invention because it is in the same field of endeavor. 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 Breedwelt further in view of Yuan to allow for mapping the interactions into respective node representations. . Motivation to do so would allow for improving context awareness and enhance accuracy.
With respect to claims 3 and 13 Yuan further teaches wherein mapping the interactions into node representations comprises: computing a vector embedding for each of the interactions (Yuan ¶[0023] However, in some implementations, there is no node [node] with an embedding space representation within the threshold distance to the embedding space representation of the attribute. FIG. 2C illustrates an embedding space representation of agricultural attribute [interaction] C 240, the embedding space representation of an eighth node 242);
comparing a respective distance of each of the vector embeddings with respective distances of existing nodes in a set of interactions, where each of the distances represents a respective similarity of one of the interactions with another of the interactions (Yuan ¶[0023] However, in some implementations, there is no node with an embedding space representation within [comparing] the threshold distance [similarity, distance] to the embedding space representation of the attribute. FIG. 2C illustrates an embedding space representation of agricultural attribute C 240, the embedding space representation of an eighth node 242, the embedding space representation of a ninth node 244, and the embedding space representation of a tenth node 246. None of the embedding space representations 242, 244, or 246 are within the threshold distance 204 of the embedding space representation of attribute C 240. In some implementations, the system can determine to add [create a node] a new node to the knowledge graph based on the embedding space representation of the attribute in response to determining there is no embedding space representation of a node within the threshold distance. FIG. 2D illustrates also illustrates the embedding space representation of agricultural attribute C 240, the embedding space representation of an eighth node 242);
based on the comparing, and for each of the interactions, creating a respective node in a graph to represent the interaction; and creating, in the graph, edges from each of the nodes to respective neighbors of the nodes (Yuan ¶[0023] However, in some implementations, there is no node with an embedding space representation within [comparing] the threshold distance [similarity, distance] to the embedding space representation of the attribute. FIG. 2C illustrates an embedding space representation of agricultural attribute C 240, the embedding space representation of an eighth node 242, the embedding space representation of a ninth node 244, and the embedding space representation of a tenth node 246. None of the embedding space representations 242, 244, or 246 are within the threshold distance 204 of the embedding space representation of attribute C 240. In some implementations, the system can determine to add [create a node] a new node to the knowledge graph based on the embedding space representation of the attribute in response to determining there is no embedding space representation of a node within the threshold distance. FIG. 2D illustrates also illustrates the embedding space representation of agricultural attribute C 240, the embedding space representation of an eighth node 242).
With respect to claims 4 and 14 Yuan further teaches wherein when one of the distances between two of the interactions is below a threshold, the nodes representing the two interactions are merged into a single node (Yuan ¶[0021] For example, the system can determine whether the embedding space representation of the nodes is within a threshold [threshold] distance of the embedding space representation of attribute A. FIG. 2A additionally includes an indication of a threshold distance 204 from the embedding space representation of agricultural attribute A 202. In the illustrated example, the embedding space representation of the first node 206 is within the threshold distance 204 of the embedding space representation of attribute A 202, while the embedding space representation of the second node 208 and the embedding space representation of the third node 210 are not within the threshold distance 204 of the embedding space representation of attribute A 202. In some implementations, the system can determine the first node corresponds [merged into a single node] with attribute A based on determining the embedding space representation of the first node 206 is within the threshold distance 204 of the embedding space representation of attribute A. However, the threshold distance 204 illustrated in FIG. 2A is merely illustrative. In some implementations, one or more additional or alternative threshold values can be utilized.)
.
Claim(s) 5, 6, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Breedwelt in view of Mendelson ( US 20250181891A1).
With respect to claims 5 and 15 Breedwelt does not explicitly disclose however Mendelson teaches wherein when the interactions are mapped into a graph, the verification phase triggers use of a community detection algorithm that partitions the graph into mutually similar vertices which each correspond to a different respective subject of the chat session (Mendelson ¶[0053] In some embodiments, the graph generation engine 204 may also assign weights to the edges 412, 414, 416, and 418, such that a larger weight is assigned to the edge when a larger number of transactions are associated with the two fuzzy attributes and a smaller weight is assigned to the edge when a smaller number of transactions are associated with the two fuzzy attributes. For example, the graph generation engine 204 may assign a weight of “2” to the edge 412 that connects the attribute vertices [vertices] 322 and 324 based on the two transaction vertices 302 and 304 that are connected to the attribute vertices 322 and 324 in the graph 300. The graph generation engine 204 may assign a weight of “1” to each of the edges 414, 416, and 418, since only one transaction vertex is connected to the respective pairs of attribute vertices in the graph 300. Such a modified graph enables the community detection [community detection algorithm] engine 206 to determine which fuzzy attributes are related [mutually similar] to each other, and how strong are the relationships among the fuzzy attributes. For example, when edges within a community have larger weights, the community detection engine 206 may determine that the relationships among the fuzzy attributes are stronger than edges within a community having smaller weights. The community detection engine 206 may partition the modified graph 400 into multiple communities based on the connections among the attribute vertices. For example, the community detection engine 206 may group the attribute vertices 322, 324, 328, and 326 into a community 402 based on the attribute vertices 322, 324, 326, and 328 being connected with each other, directly or indirectly. The community detection engine 206 may also group the attribute vertices 330 and 332 into another community 404. The community detection engine 206 separates the two communities 402 and 404 because there is no connection between the vertices in the community 402 and the vertices in the community 404.)
Mendelson is considered to be analogous to the claimed invention because it is in the same field of endeavor. 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 Breedwelt further in view of Mendelson to allow for community detection. Motivation to do so would allow for determining attributes that are related to each other (Mendelson, [0020])
With respect to claims 6 and 16 Mendelson teaches wherein the mapping comprises using a clustering algorithm to map the interactions into respective data points of the n-dimensional space (Mendelson ¶[0053] In some embodiments, the graph generation engine 204 may also assign weights to the edges 412, 414, 416, and 418, such that a larger weight is assigned to the edge when a larger number of transactions are associated with the two fuzzy attributes and a smaller weight is assigned to the edge when a smaller number of transactions are associated with the two fuzzy attributes. For example, the graph generation engine 204 may assign a weight of “2” to the edge 412 that connects the attribute vertices 322 and 324 based on the two transaction vertices 302 and 304 that are connected to the attribute vertices 322 and 324 in the graph 300. The graph generation engine 204 may assign a weight of “1” to each of the edges 414, 416, and 418, since only one transaction vertex is connected to the respective pairs of attribute vertices in the graph 300. Such a modified graph enables the community detection engine 206 to determine which fuzzy attributes are related to each other, and how strong are the relationships among the fuzzy attributes. For example, when edges within a community have larger weights, the community detection engine 206 may determine that the relationships among the fuzzy attributes are stronger than edges within a community having smaller weights. The community detection engine 206 may partition the modified graph 400 into multiple communities based on the connections among the attribute vertices. For example, the community detection engine 206 may group [clustering] the attribute vertices 322, 324, 328, and 326 into a community 402 based on the attribute vertices 322, 324, 326, and 328 being connected with each other, directly or indirectly. The community detection engine 206 may also group the attribute vertices 330 and 332 into another community 404. The community detection engine 206 separates the two communities 402 and 404 because there is no connection between the vertices in the community 402 and the vertices in the community 404.)
Mendelson is considered to be analogous to the claimed invention because it is in the same field of endeavor. 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 Breedwelt further in view of Mendelson to allow for community detection. Motivation to do so would allow for determining attributes that are related to each other (Mendelson, [0020])
Claim(s) 9, 10, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Breedwelt in view of Bastian ( US 20250077728 A1)
With respect to claims 9 and 19 Breedwelt does not explicitly disclose however Bastian teaches wherein each interaction comprises a respective query submitted by the user, and an answer to the query, and the answer is generated by an LLM (large language model) of the chatbot (Bastian ¶[0089] The vectorstore tool can be useful when the agent needs to defer to a compiled, curated set of knowledge in a particular area. For example, the system saves a set of knowledge and context in a vector database. The user's query [query] is passed to the tool, which then utilizes the vector database to determine most relevant knowledge sources from the vector database. The knowledge from the database and the user's query is then sent to the LLM [LLM] in order for the LLM to summarize the information and give an answer [answer] to the question which was given to the tool. By combining a curated vectorstore with the power of the LLM for summary, the system can provide accurate knowledge to users where the LLM itself may not be specifically trained. Knowledge in a particular area can include complex manufacturing details, specifics, etc., which are important to be correct.)
Mendelson is considered to be analogous to the claimed invention because it is in the same field of endeavor. 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 Breedwelt further in view of Bastian to allow for LLM. Motivation to do so would allow the system to provide accurate knowledge to users where the LLM itself may not be specifically trained (Bastian, [0089])
With respect to claims 10 and 20 Breedwelt does not explicitly disclose however Bastian teaches wherein the building phase and the verification phase are performed while maintaining adherence to a token budget of an LLM (large language model) of the chatbot (Bastian ¶[0052] As the conversational history increases, the back-and-forth of the conversation would continue to be extended so that, up to a limit, the LLM [LLM] is aware of the conversational history which has occurred. Additional chat [chat] history generally improves performance, but increases the number of tokens used per API call... For example, the system can manage chat history using a variety of techniques, including just passing the n most recent messages, dynamically summarizing the chat history (via another, separate LLM API call), or saving chat history to a queryable vectorstore. These techniques can be used to allocate a token budget [token budget] for chat history to ensure the largest number of tokens can be used for user queries.)
Mendelson is considered to be analogous to the claimed invention because it is in the same field of endeavor. 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 Breedwelt further in view of Bastian to allow for LLM. Motivation to do so would allow the system to provide accurate knowledge to users where the LLM itself may not be specifically trained (Bastian, [0089])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATHAR N PASHA whose telephone number is (408)918-7675. The examiner can normally be reached Monday-Thursday Alternate Fridays, 7:30-4:30 PT.
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/ATHAR N PASHA/ Primary Examiner, Art Unit 2657
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