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 .
DETAILED ACTION
Claims 1-20 are presented for examination.
This is a Non-Final Action.
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 11-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 11 recites A system for interacting with a user via a chatbot, the system comprising:. However, each of the limitation, specifically, the body of the claim does not define any specific hardware (i.e. a processor, memory) to execute the recited receiving, processing the natural language query or for storing/processing input utilizing the LLM. The claim lacks the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101. Therefore, the claimed system is not limited to embodiments which include the hardware necessary to enable any underlying functionality to be realized, instead being software per se.
Claims 11-15 are dependent upon claim 10, respectively, do not add anything to correct the deficiency and therefore are likewise rejected.
Claim Rejections - 35 U.S.C. §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 1-3, 5-20 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to independent claims 1, 11 and 16, specifically claim 1 recites “receiving a natural language query, identifying relevant information including text and images, generating a response based on the query and identified information”, Claim 11 is similar to claim 1, which is mental process because these are observations, evaluations and judgement that can practically be performed in the human mind or with pen and paper.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. At step 2A, prong two, claim(s) 1 and 11 recites the additional elements of “including a chatbot, search engine, LLM, a controller, display” merely apply the abstract idea using generic computer technology to receive information, retrieve information, generate output and display results without improving the function of the computer, search engine, image retrieval or language model itself, hence recites insignificant extra solution activity. For claim 16, “including presenting selectable options, receiving a user selection, using a Large Language Model, and displaying a response via a chatbot”, merely implement the abstract idea using generic computer interaction and processing components, hence is insignificant extra solution activities.. The claim does not recite any specific technological improvement in option generation, user-interface operation or model operation, and therefore does not integrate idea into a practical application.
The claims, 1, 11 and 16 at step 2B do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained with respect to Step 2A Prong Two, the additional elements as recited in step 2A prong 2 recite chatbot, search engine, LLM, and display of textual information and images or links, are recited at high level of generality and perform only their well-understood, routine and convectional functions of receiving input, retrieving information, processing information, generating output, and displaying result. Furthermore, Claim 16 recites additional elements, providing selectable options, receiving a selection, are recited at a high level of generality and perform only generic computer functions associated with user interaction, information presentation, information selection and response generation.. No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself. According, claim 1 is ineligible under 101.
Claims 2, 3, 5-20 are dependent claims and do not recite any additional elements that would amount to significantly more than the abstract idea. Specifically,
Claim 2. With respect to step 2A prong 2 “process the natural language query and submit a corresponding search query to the search engine” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 3. With respect to step 2A prong 2 “extracting textual information from one or more images of one or more documents; and storing an association between the textual information extracted from the one or more images of one or more documents and one of the one or more images or links to one of the one or more images” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 5. With respect to step 2A prong 2 “wherein one or more of the relevant images corresponds to a page of a document that is in an image format” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 6. With respect to step 2A prong 2 “wherein one or more of the relevant images corresponds to a page of a document that is in an image format” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 7. With respect to step 2A prong 2 “wherein one or more of the relevant images corresponds to a video and/or one or more frames of the video” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 8. With respect to step 2A prong 2 “extracting textual information from one or more data sources, wherein the one or more data sources include real-time operational data of a building management system.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 9. With respect to step 2A prong 2 “extracting textual information from one or more data sources, wherein the one or more data sources include a database of prior customer queries and corresponding resolutions.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 10. With respect to step 2A prong 2 “extracting textual information from one or more data sources, wherein the one or more data sources include a database of prior service tickets and corresponding resolutions.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 11 is similar to the combination of claims 1 and 2 hence rejected similarly.
Claims 12 and 13 are similar to claim 1 hence rejected similarly.
Claim 14. With respect to step 2A prong 2 “compare a numerical vector representation of the natural language query to numerical vector representations of textual information and/or images of one or more of documents to identify the relevant information.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 15. With respect to step 2A prong 1 “identified relevant information into two or more clusters” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine algorithms based on search goals.
With respect to step 2A prong 2 “the controller is configured to: provide two or more selectable options via the chatbot user interface that are based at least in part on the two or more clusters; receive a selection of one of the two or more selectable options via the chatbot user interface; submit to the Large Language Model the natural language query and at least some of the identified relevant information that is relevant to the selected one of the two or more selectable options; and the Large Language Model generating the response to the natural language query based at least in part on the natural language query and at least some of the identified relevant information that is relevant to the selected one of the two or more selectable options.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 16 is similar to the combination of claim 1 and 15 hence rejected similarly.
Claim 17. With respect to step 2A prong 2 “clustering the identified relevant information into two or more clusters; and wherein each of the two or more selectable options correspond to a corresponding one of the two or more clusters.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 18. With respect to step 2A prong 2 “wherein the two or more selectable options correspond to the two or more clusters that the search engine identifies as having a highest correlation with the search query..” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 19. With respect to step 2A prong 2 “where each of two or more of the selectable options includes a stated refinement to the natural language query.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 20. With respect to step 2A prong 2 “where one of the two or more selectable options correspond to a request for further refinement of the natural language query by the user via the chatbot.” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
In view of compact prosecution, for independent claim 1, 11 and 16, if claims 4 and interlink claims were incorporated into claim 1 and functional claim language from claims 3 and 4 were incorporated into the claim 11 and 16 would overcome the abstract idea 101.
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, 2, and 11-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Socher et al. (US 2024/0020538).
1. Socher teaches, A method for interacting with a user via a chatbot (Abstract, Paragraph 3 – teaches utilizing ChatGPT (Chat interface), Socher), the method comprising:
receiving a natural language query via the chatbot (Paragraph 3 and Fig 7: 702 – teaches receiving a NLQ via chat interface, Socher);
processing the natural language query and submitting a corresponding search query to a search engine (Fig 7:704 – teaches processing the NLQ and generating a corresponding search query, Socher), wherein the search engine identifies relevant information for use in formulating a response to the natural language query (Fig 8:808 – teaches obtaining first results (relevant information) , 8:810 – teaches incorporating, in the NLM, the set of research results – thus disclosing that search results are identified and then used in formulating the response, Socher) including identifying relevant textual information (Paragraph 34 – teaches extracting information from the search results, Socher) and one or more relevant images or links to one or more relevant images (Paragraph 33 – teaches both links and images retrieved from a search-results, Socher);
submit the identified relevant information along with the natural language query to a Large Language Model (Fig 8: 802 – teaches a input query and 8:810 – teaches incorporate, in the natural language model, the set of search results – thus disclosing input query and the identified relevant information/search results supplied to the model; Paragraphs 47-48 – discloses LLMs communicating with the NPL models, Socher);
the Large Language Model generating the response to the natural language query based at least in part on the natural language query and the identified relevant information that was submitted to the Large Language Model (Fig 8:810 – teaches incorporating the natural lanage model the set of search results; 8:812 – teaches generate, by the natural lineage mode, a response to the input query; and Paragraph 34 – teaches generate text based on the search results and nay parameters specified in input 122, generate a natural language response – thus disclosing model generating the response based on the input query and the identified relevant information (search results), Socher); and
displaying the response via the chatbot (Paragraph 34 and 36 – teaches generate a natural language response and return as a NL output for display at the user device in a form of conversation, Socher), wherein the response includes textual information and one or more relevant images or links to one or more relevant images (Paragraph 43 – teaches insert one or more images retrieved from a webpage following a search result link in the NL output 125; Paragraphs 24 and 45 – teaches the NL output 125 may comprise citations and/or references, Socher).
2. Socher teaches, The method of claim 1, wherein an orchestrator is configured to process the natural language query and submit a corresponding search query to the search engine (Fig 7:704, 706 & Paragraph 28 – teaches a server-side component that processes the natural language input to generate a search query and provides that search query to one or more search engines. The server/text generation server corresponds to the claimed orchestrator, Socher).
Claim 11 is similar to the combination of claims 1 and 2 hence rejected similarly.
Claims 12 and 13 are similar to claim 1 hence rejected similarly.
Claim 14 is similar to claim 4 hence rejected similarly.
Claim 16 is similar to claims 1 and 15 hence rejected similarly.
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.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538) further in view of Vincent et al. (US 2013/0039570)
All the limitations of claim 2 are taught above.
3. Socher does not explicitly teach,
extracting textual information from one or more images of one or more documents; and
storing an association between the textual information extracted from the one or more images of one or more documents and one of the one or more images or links to one of the one or more images.
However, Vincent teaches,
extracting textual information from one or more images of one or more documents (Abstract, Fig 3:312, Paragraphs 7 and 13 – teaches extracting text information from images, Vincent); and
storing an association between the textual information extracted from the one or more images of one or more documents and one of the one or more images or links to one of the one or more images (Paragraph 18, 87 and 95 – teaches storing the extracted text as associated with the image, Vincent).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to combine Vincent with Socher because Socher teaches a conventional system that generates search queries from natural language input, obtains search results, and incorporates retrieved information, including images and links, into a response, while Vincent teaches extracting text from images and storing the extracted text as associated with the image so that the image may be identified and retrieved using the text. A POSITA would have ben motivated to apply Vincent’s image-text extraction and association techniques in Socher to improve retrieval of relevant image based information such as document images containing textual content, thereby enabling Socher’s system to use text extracted from images as additional searchable information for generating more relevant chatbot responses. The combination would have involved only the predictable use of known techniques to improve a similar search and retrieval function.
Claims 4 and 14 is rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538) in view of Vincent et al. (US 2013/0039570) further in view of Mandal et al. (US 2025/0219969)
All the limitation of claim 3 are taught above.
4. The combination of Socher and Vincent teach,
…and the one or more relevant images or links to one or more relevant image (Paragraph 33 – teaches query results include links to webpages, Socher; Paragraph 43 – teaches images retrieved from a webpage following a search result link, Vincent)
The combination of Socher and Vincent do not teach,
converting textual information in one or more of the documents into numerical vector representations, including the textual information extracted from the one or more images of one or more documents;
converting the natural language query into a numerical vector representation; and
the search engine processing the numerical vector representation of the natural language query and the numerical vector representations of the textual information in the one or more of the documents to identify the relevant information and the one or more relevant images or links to one or more relevant image.
However, Mandal teaches,
converting textual information in one or more of the documents into numerical vector representations, including the textual information extracted from the one or more images of one or more documents (Fig 2:204, Paragraph 19 – teaches converting textual information into numerical vector representations by embedding textual content into vector space , Mandal);
converting the natural language query into a numerical vector representation (Fig 2:206, Paragraph 19 – teaches converting the user/natural language query into a vector representation, Mandal); and
the search engine processing the numerical vector representation of the natural language query and the numerical vector representations of the textual information in the one or more of the documents to identify the relevant information (Fig 2:207, Paragraph 25 – teaches processing the vectorized query and vectorized textual content using vector search to identify relevant information, Mandal).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify Socher/Vincent combination with Mandal because Socher already uses search-driven retrieval for generating responses, Vincent adds text extracted from images and associated with the image, and Mandal teaches embedding textual content and user queries into vector space and executing vector search to improve retrieval of relevant content, including images. A POSITA would have been motivated to apply Mandal’s vector-search techniques to the text extracted from mages in Vincent and the search-driven response framework of Socher in order to improve identification of relevant textual information and corresponding images for use in generating chatbot responses, which would have been predictable improvement to retrieval accuracy and relevance.
Claim 14 is similar to claim 4 hence rejected similarly.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538) in view of Bersagel et al. (US 2024/0346257)
All the limitations of claim 1 are taught above.
5. Socher does not explicitly teach, wherein one or more of the relevant images corresponds to a page of a document that is in an image format.
However, Bersagel teaches, wherein one or more of the relevant images corresponds to a page of a document that is in an image format (Abstract, Paragraph 20 – teaches a document that is image-based and further teaches that the document has pages. Thereby teaching relevant image may correspond to a page of a document in image format).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify Socher with Bersagel because Socher teaches a search driven chatbot system that retrieves relevant information, including links and images, for use in generating a response to a NLQ, while Bersagel teaches processing an image based document, including OCR based extraction of words from the document and analysis of document pages within the image based document. A POSITA would have been motivated to incorporate Bersagel’s image based processing into Socher in order to improve Socher’s ability to identify and retrieve relevant chatbot responses. Doing so would have been a predictable use of known document-image/OCR techniques to improve the retrieval of relevant image based document content in Socher’s existing search and response framework.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538) in view of Takashima (US 20240193217)
All limitations of claim 1 are taught above.
6. Socher does not explicitly teach, wherein one or more of the relevant images corresponds to particular image on a page of a document.
However, Takeshima teaches, wherein one or more of the relevant images corresponds to particular image on a page of a document (Fig 5:S501, S502, Fig 7: (No.1), Fig 8:S802, S804 - teaches a document page containing a particular image block on that page and processing that particular image, Takashima).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify Socher with Takashima because Socher teaches a search driven chatbot system that retrieves relevant information, including images and links, for use in generating a response to a natural language query, while Takashima teaches processing document image data, determining block on a document page, and distinguishing a particular IMAGE block from TEXT and other content on the page. A POSITA would have bene motivated to incorporate Takashima’s document page image block identification into Socher in order to improve Socher’s ability to identify and retrieve not only whole document-page images, but also a particular image on a page of a document, for use in formulating chatbot responses. Doing so would have been a predictable use of known document-image segmentation and image recognition techniques to improve the granularity and relevance of image retrieval within Socher’s existing search and response framework.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538) in view of Dela Rosa et al. (US 2025/0078540)
All the limitations of claim 1 are taught above.
7. Socher does not explicitly teach, wherein one or more of the relevant images corresponds to a video and/or one or more frames of the video.
However, Dela Rosa teaches, wherein one or more of the relevant images corresponds to a video and/or one or more frames of the video (Abstract, Fig 2, Fig 5:502 – teaches using image frames (video/camera stream) as the basis for identifying relevant results, Dela Rosa).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify Socher with Dela Rosa because Socher teaches a search-driven chatbot system that retrieves relevant information, including images and links, for use in generating a response to a natural lanage query, while Dela Rosa teaches using image frames from a computing device as the source material for identifying relevant results. A POSITA would have been motivated to incorporate Dela Rosa’s video frame retrieval approach into Socher in order to improve Socher’s ability to identify and retrieve relevant visual content not only from static images, but also from videos, for use in formulating chatbot responses. Doing so would have been predictable use of Known frame-based image analysis techniques to expand the type of relevant image content retrievable within Socher’s existing search and response framework.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538)
All the limitations of claim 1 are taught above.
Claims 8-10. Socher teaches, extracting information from one or more data sources for use in generating a response to a natural language query, including generating search queries from natural language input, obtaining search results, and extracting information from webpages or cloud files linked by the search results for inclusion in the generated responses (Fig 7:702-706, Fig 8:808-812; Paragraphs 28, 33 and 79).
Socher does not explicitly recite, extracting textual information from one or more data sources, wherein the one or more data sources include: real-time operational data of a building management system (claim 8), a database of prior customer queries and corresponding resolutions (claim 9), a database of prior service tickets and corresponding resolutions (claim 10).
However, It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to use any such known information repository as the data source in Socher, because each merely constitutes a known source of textual information that can be queries and extracted to provide relevant information responsive to a user query, and the selection of kown source type over another would have been a predictable design choice that does not change the fundamental operation of Socher’s system.
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Socher et al. (US 2024/0020538) in view of Haahr et al. (US 8,645,407) and further in view of Soubbotin (US 12,038,958)
All limitations of claim 11 are taught above.
15. Socher does not explicitly teach,
wherein the search engine is configured to cluster the identified relevant information into two or more clusters, and the controller is configured to:
provide two or more selectable options via the chatbot user interface that are based at least in part on the two or more clusters;
receive a selection of one of the two or more selectable options via the chatbot user interface;
submit to the Large Language Model the natural language query and at least some of the identified relevant information that is relevant to the selected one of the two or more selectable options; and the Large Language Model generating the response to the natural language query based at least in part on the natural language query and at least some of the identified relevant information that is relevant to the selected one of the two or more selectable options.
However, Haahr teaches,
wherein the search engine is configured to cluster the identified relevant information into two or more clusters (Fig 10:137; Col 10: lines 46-47 – teaches clustering identified relevant information into clusters, Haahr), and the controller is configured to:
provide two or more selectable options via the chatbot user interface that are based at least in part on the two or more clusters (Fig 10: 143, 149 – teaches deriving a refinement option from each cluster and presenting refinements, Haahr).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Socher with Haahr because Socher teaches a search driven chatbot system that receives a natural language query, generates a search query, retrieves relevant information and uses a language model to generate a response, while Haahr teaches clustering retrieved query related information, ranking the clusters, selecting the highest ranking clusters as refinement clusters, naming the clusters with representative queries and presenting the refinement. A POSITA would have been motivated to incorporate Haahr’s clustering and refinement techniques into Socher in order to organize Socher’s retrieved information into grouped refinement options and present the most relevant grouping to the user before final response generation. Dosing so would have been predictable use of known search refinement and clustering techniques to improve the relevance and usability of Socher’s search and response workflow.
Soubbotin teaches,
receive a selection of one of the two or more selectable options via the chatbot user interface (Fig 15:1565, 1590 –teaches those refinement as selectable user options, Soubbotin; In combination with Haahr’s teaching of deriving a refinement option from each cluster and presenting refinements; Together, they teach providing two or more selectable option based on clusters).
submit to the Large Language Model the natural language query and at least some of the identified relevant information that is relevant to the selected one of the two or more selectable options (Fig 15: 1540 – teaches the AI language model uses the query, relevant information and user selected feedback/refinement, Soubbotin; in combination with Fig 1B:L116a-116n – teaches LLMs and Fig 8:810 – teaches explicit LLM framework, Socher); and
the Large Language Model generating the response to the natural language query based at least in part on the natural language query and at least some of the identified relevant information that is relevant to the selected one of the two or more selectable options (Fig 15: 1560 – teaches AI language model response generation based on the refined/selected information flow, Soubbotin; in combination with Fig 8:812 – teaching LLM response generation, Socher).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Socher/Haahr combination with Soubbotin because although Socher and Haahr together teach retrieving relevant information and clustering/ranking refinement clusters, Soubbotin teaches presenting multiple selectable refinements to the user and using the user’s selection in subsequent search, summarization and AI language model processing. A POSITA would have been motivated to incorporate Soubbotin’s user selection refinement workflow into the Socher-Haahr system so that cluster based refinements generated using Haahr could be presented as selectable options and a user’s selected option could guide the subset of information used for subsequent AI response generation. This would have been a predictable combination of known techniques to improve user navigation, disambiguation and refinement of retrieved information before final chatbot response generation.
Claim 16 is similar to claims 1 and 15 hence rejected similarly.
17. The combination of Socher, Haahr and Soubbotin teach, The method of claim 16, further comprising:
clustering the identified relevant information into two or more clusters (Fig 10:137; Col 10: lines 46-47 – teaches clustering identified relevant information into two or more clusters, Haahr); and
wherein each of the two or more selectable options correspond to a corresponding one of the two or more clusters (Fig 10: 143, 149; Col 11: lines 15-17 – teaches that each cluster is represented by a corresponding query/refinement, i.e. each selectable option corresponds to a corresponding cluster, Haahr).
18. The combination of Socher, Haahr and Soubbotin teach, The method of claim 17, wherein the two or more selectable options correspond to the two or more clusters that the search engine identifies as having a highest correlation with the search query (Fig 10: 138, 139 – teaches identifying clusters by ranking/scoring them relative to the query context and selecting the highest ranking cluster as refinement clusters, Haahr).
19. The combination of Socher, Haahr and Soubbotin teach, The method of claim 16, where each of two or more of the selectable options includes a stated refinement to the natural language query ((Fig 15:1565, 1590 –teaches those refinement as selectable user options, Soubbotin; In combination with Fig 10: 143, 149 - Haahr’s teaching of deriving a refinement option from each cluster and presenting refinements; Together, they teach providing two or more selectable option based on cluster ).
20. The combination of Socher, Haahr and Soubbotin teach, The method of claim 19,
the user may refine the query by reforming it, adding information to it or selecting suggested questions, suggested websites or cloud tags (Fig 15:1590, 1565, Col 47: lines 12-57, Soubbotin). However, Soubbotin does not expressly disclose that one of the selectable options is itself a request for further refinement of the natural language query.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to include, among selectable options presented in Soubbotin’s refinement interface, an explicit option requesting further refinement from the user, because such an option would have been a predictable design choice for prompting the user to provide additional narrowing information when the original query remained ambiguous, thereby improving the relevance of subsequent search and AI generated response.
Conclusion
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/AMRESH SINGH/Primary Examiner, Art Unit 2159
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