DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is response to application filed 06/30/2025.
Status of the claims
Claims 1-20 are currently pending for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/11/2025 is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12346366. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of instant application recited similar limitations. Therefore, they are rejected on the ground of nonstatutory double patenting.
Instant application
1. A method, comprising: obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform; selecting, from the plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt; inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM; and generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset.
2. The method of claim 1, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM; responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms; and performing a keyword search on the plurality of documents based on the one or more search terms.
3. The method of claim 2, wherein: each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was opened; and performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document.
4. The method of claim 1, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document, and including the selected document in the subset.
5. The method of claim 1, wherein: each document of the plurality of documents comprises metadata, wherein the metadata indicates whether a user has permission to open a respective document; and selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset.
6. The method of claim 1, wherein: each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was modified; and selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset.
7. The method of claim 1, wherein a citation of the one or more citations comprises a link to a document in the subset.
8. The method of claim 1, wherein a citation of the one or more citations comprises a link to a document portion of a document in the subset.
9. The method of claim 1, further comprising generating, using an embedding model, the first query embedding based on the generative MLM prompt.
10. A system, comprising: a memory; and a processing device, coupled to the memory, to perform operations comprising: obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform; selecting, from the plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt; inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM; and generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset.
11. The system of claim 10, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM; responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms; and performing a keyword search on the plurality of documents based on the one or more search terms, wherein each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was opened, and performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document.
12. The system of claim 10, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document, and including the selected document in the subset, wherein each document of the plurality of documents comprises metadata, wherein the metadata indicates at least one of (i) whether a user has permission to open a respective document or (ii) a timestamp corresponding to a last time a respective document was modified, and wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset.
13. The system of claim 10, wherein a citation of the one or more citations comprises at least one of a link to a document in the subset, or a link to a document portion of a document in the subset.
14. The system of claim 10, the operations further comprising generating, using an embedding model, the first query embedding based on the generative MLM prompt.
15. A non-transitory computer-readable medium comprising instructions, which when executed on a processing device, causing the processing device to perform operations comprising: obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform; selecting, from the plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt; inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM; and generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset.
16. The non-transitory computer-readable medium of claim 15, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM; responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms; and performing a keyword search on the plurality of documents based on the one or more search terms, wherein each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was opened, and performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document.
17. The non-transitory computer-readable medium of claim 15, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document, and including the selected document in the subset, wherein each document of the plurality of documents comprises metadata, wherein the metadata indicates at least one of (i) whether a user has permission to open a respective document or (ii) a timestamp corresponding to a last time a respective document was modified, and wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset.
18. The non-transitory computer-readable medium of claim 15, wherein a citation of the one or more citations comprises a link to a document portion of a document in the subset.
19. The non-transitory computer-readable medium of claim 15, wherein a citation of the one or more citations comprises a link to a document in the subset.
20. The non-transitory computer-readable medium of claim 15, the operations further comprising generating, using an embedding model, the first query embedding based on the generative MLM prompt.
U.S. Patent No. 12346366
1. A method, comprising: obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more documents stored in a cloud-based content management platform; generating, using an embedding model, a first query embedding based on the generative MLM prompt; selecting, from a plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and the first query embedding; inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM; and generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset.
2. The method of claim 1, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM; responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms; and performing a keyword search on the plurality of documents based on the one or more search terms.
3. The method of claim 2, wherein: each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was opened; and performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document.
4. The method of claim 1, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document, and including the selected document in the subset.
5. The method of claim 1, wherein: each document of the plurality of documents comprises metadata, wherein the metadata indicates whether a user has permission to open a respective document; and selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset.
6. The method of claim 1, wherein: each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was modified; and selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset.
7. The method of claim 1, wherein a citation of the one or more citations comprises a link to a document in the subset.
8. The method of claim 1, wherein a citation of the one or more citations comprises a link to a document portion of a document in the subset.
9. A method, comprising: obtaining a first generative machine learning model (MLM) prompt from a user interface, wherein the first generative MLM prompt includes an indication of a user request to generate content based on one or more documents stored on a cloud-based content management platform; inputting the first generative MLM prompt and a plurality of documents into a first generative MLM, wherein: the plurality of documents comprises documents stored on the cloud-based content management platform, and each document of the plurality of documents comprises a plurality of document portions, and each document portion includes an associated first query embedding; generating, using the first generative MLM, a first generative MLM response; generating, via an embedding model, a second query embedding based on the first generative MLM response; selecting a subset of the plurality of document portions, wherein the first query embedding of each document portion in the subset is within a threshold similarity from the second query embedding; and including, in the first generative MLM response, content generated by the first generative MLM, and one or more citations to document portions of the subset of the plurality of document portions.
10. The method of claim 9, further comprising providing, on the user interface, the first generative MLM response, wherein the first generative MLM response further includes a highlighting of text of the first generative MLM response.
11. The method of claim 9, wherein a citation of the one or more citations includes a link to a document that includes a document portion of the subset of the plurality of document portions.
12. The method of claim 11, further comprising, responsive to a user interaction with the link to the document that includes the document portion: opening the document in the user interface; and highlighting the document portion in the document.
13. The method of claim 9, further comprising: obtaining a second generative MLM prompt from the user interface; and responsive to inputting the second generative MLM prompt, the subset of the plurality of documents, and the first generative MLM response into the first generative MLM, generating, using the first generative MLM, a second response.
14. The method of claim 9, wherein a document portion of the plurality of document portions comprises at least one of: a sentence; a paragraph; or a section.
15. The method of claim 9, wherein the generative MLM prompt comprises a generative MLM prompt automatically generated by a subsystem of the cloud-based content management platform.
16. A system, comprising: a memory; and one or more processing devices, coupled to the memory, configured to perform operations comprising: generating, using an embedding model, a first query embedding based on a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more documents stored in a cloud-based content management platform; selecting, from a plurality of documents stored in the cloud-based content management platform, a first subset of the plurality of documents, wherein each document in the first subset includes an associated second query embedding, and the selection of the first subset is based on the first query embedding being within a threshold similarity from the second query embedding associated with a respective document of the plurality of documents; selecting, from the first subset, a second subset of documents, wherein the selection of the second subset is based on an access permission of each document in the first subset; inputting the generative MLM prompt and the second subset of the plurality of documents into a generative MLM; and generating, using the generative MLM, a response, wherein the response comprises content generated by the generative MLM.
17. The system of claim 16, wherein: each document of the second subset comprises a plurality of document portions, and each document portion includes an associated third query embedding; the processing device is further configured to select, from the plurality of document portions of the documents of the second subset, a subset of document portions, wherein the selection of the subset of document portions is based on the first query embedding being within a first threshold similarity from a third query embedding of a plurality of third query embeddings; and inputting the generative MLM prompt and the second subset into the generative MLM comprises inputting the generative MLM prompt and the subset of document portions into the generative MLM.
18. The system of claim 17, wherein the operations further comprise: generating, via the embedding model, a fourth query embedding based on the generative MLM response; determining whether the fourth query embedding is within a second threshold similarity from a third query embedding of plurality of third query embeddings; and responsive to determining that the fourth query embedding is not within the second threshold similarity from any third query embedding of the plurality of third query embeddings, rejecting the generative MLM response.
19. The system of claim 16, wherein the generative MLM comprises a transformer-based large language model (LLM).
20. The system of claim 16, wherein each of the first query embedding and the second query embeddings comprises a vector of floats.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 9-12, 14-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al. (US 20240256582, hereafter Jain).
Regarding claim 1, Jain discloses: A method, comprising: obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform (Jain [0018; 0024] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response);
selecting, from the plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt (Jain [0018] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response);
inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM (Jain [0018] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response); and
generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset (Jain [0017] discloses: The technical benefits of providing a set of search results (e.g., comprising reference documents that have been verified by document owners) with the prompt and/or requesting that the generated response include citations or references to the particular search results of the set of search results used for generating the response is that the integrity of the generated response may be improved.).
Regarding claim 2, Jain discloses: The method of claim 1, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM (Jain [0064] discloses: The prompt generator 278 generates input prompt to be provided to generative AI models);
responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms (Jain [0079] discloses: The natural language phrase may be generated using a generative AI model and a prompt, such as the prompt 327 in FIG. 3C. The generative AI model may comprise one of the machine learning models 282 in FIG. 2D. In step 408, a set of search results is identified using the natural language phrase); and
performing a keyword search on the plurality of documents based on the one or more search terms (Jain [0079] discloses: the set of search results may be generated using a search and knowledge management system or a search engine. The set of search results may include the first search result 324 in FIG. 3C. In step 410, the set of search results is ranked. The set of search results may be ranked based on the relevance of the search results to the search query acquired in step 402).
Regarding claim 3, Jain discloses: The method of claim 2, wherein: each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was opened (Jain [0044] discloses: The metadata for the word processing document may include an identification of the owner of the document, a timestamp associated with when the document was last modified, a file size for the document, and access permissions for the document); and
performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document (Jain [0044] discloses: The ranking path 244 may determine the ranking of the relevant documents based on the number of times that a search query term appears within the content or metadata for a document, whether the search query term matches a key word for a document, and how recently a document was created or last modified).
Regarding claim 4, Jain discloses: The method of claim 1, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document, and including the selected document in the subset (Jain [0018; 0056] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response).
Regarding claim 5, Jain discloses: The method of claim 1, wherein: each document of the plurality of documents comprises metadata, wherein the metadata indicates whether a user has permission to open a respective document (Jain [0044] discloses: The metadata for the word processing document may include an identification of the owner of the document, a timestamp associated with when the document was last modified, a file size for the document, and access permissions for the document); and
selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset (Jain [0056] discloses: a subset of the set of relevant documents may be identified (e.g., the top thirty ranked documents) based on the ranking and summary information or snippets may be acquired from the search index 204 for each document of the subset of the set of relevant documents).
Regarding claim 6, Jain discloses: The method of claim 1, wherein: each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was modified (Jain [0044] discloses: a timestamp associated with when the document was last modified); and
selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset (Jain [0056] discloses: a subset of the set of relevant documents may be identified (e.g., the top thirty ranked documents) based on the ranking and summary information or snippets may be acquired from the search index 204 for each document of the subset of the set of relevant documents).
Regarding claim 9, Jain discloses: The method of claim 1, further comprising generating, using an embedding model, the first query embedding based on the generative MLM prompt (Jain [0064] discloses: The prompt generator 278 generates input prompt to be provided to generative AI models).
Regarding claim 10, Jain discloses: A system, comprising: a memory; and a processing device, coupled to the memory, to perform operations comprising (Jain [0030]): obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform (Jain [0018; 0024] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response);
selecting, from the plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt (Jain [0018] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response);
inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM (Jain [0018] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response); and
generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset (Jain [0017] discloses: The technical benefits of providing a set of search results (e.g., comprising reference documents that have been verified by document owners) with the prompt and/or requesting that the generated response include citations or references to the particular search results of the set of search results used for generating the response is that the integrity of the generated response may be improved.).
Regarding claim 11, Jain discloses: The system of claim 10, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM (Jain [0064] discloses: The prompt generator 278 generates input prompt to be provided to generative AI models);
responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms (Jain [0079] discloses: The natural language phrase may be generated using a generative AI model and a prompt, such as the prompt 327 in FIG. 3C. The generative AI model may comprise one of the machine learning models 282 in FIG. 2D. In step 408, a set of search results is identified using the natural language phrase); and
performing a keyword search on the plurality of documents based on the one or more search terms(Jain [0079] discloses: the set of search results may be generated using a search and knowledge management system or a search engine. The set of search results may include the first search result 324 in FIG. 3C. In step 410, the set of search results is ranked. The set of search results may be ranked based on the relevance of the search results to the search query acquired in step 402).
, wherein each document of the plurality of documents comprises metadata, wherein the metadata includes a timestamp corresponding to a last time a respective document was opened (Jain [0044] discloses: The metadata for the word processing document may include an identification of the owner of the document, a timestamp associated with when the document was last modified, a file size for the document, and access permissions for the document);, and
performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document (Jain [0044] discloses: The ranking path 244 may determine the ranking of the relevant documents based on the number of times that a search query term appears within the content or metadata for a document, whether the search query term matches a key word for a document, and how recently a document was created or last modified).
Regarding claim 12, Jain discloses: The system of claim 10, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document (Jain [0018; 0056] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response), and including the selected document in the subset, wherein each document of the plurality of documents comprises metadata, wherein the metadata indicates at least one of (i) whether a user has permission to open a respective document (Jain [0044] discloses: The metadata for the word processing document may include an identification of the owner of the document, a timestamp associated with when the document was last modified, a file size for the document, and access permissions for the document);or (ii) a timestamp corresponding to a last time a respective document was modified, and wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset (Jain [0056] discloses: a subset of the set of relevant documents may be identified (e.g., the top thirty ranked documents) based on the ranking and summary information or snippets may be acquired from the search index 204 for each document of the subset of the set of relevant documents).
Regarding claim 14, Jain discloses: The system of claim 10, the operations further comprising generating, using an embedding model, the first query embedding based on the generative MLM prompt (Jain [0064] discloses: The prompt generator 278 generates input prompt to be provided to generative AI models).
Regarding claim 15, Jain discloses: A non-transitory computer-readable medium comprising instructions, which when executed on a processing device, causing the processing device to perform operations comprising (Jain [0030]): obtaining a generative machine learning model (MLM) prompt, wherein the generative MLM prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform (Jain [0018; 0024] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response);
selecting, from the plurality of documents stored in the cloud-based content management platform, a subset of the plurality of documents, wherein the selection of the subset is based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt (Jain [0018] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response);
inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM (Jain [0018] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response); and
generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset (Jain [0017] discloses: The technical benefits of providing a set of search results (e.g., comprising reference documents that have been verified by document owners) with the prompt and/or requesting that the generated response include citations or references to the particular search results of the set of search results used for generating the response is that the integrity of the generated response may be improved.).
Regarding claim 16, Jain discloses: The non-transitory computer-readable medium of claim 15, wherein selecting the subset of the plurality of documents comprises: inputting the generative MLM prompt into a second generative MLM (Jain [0064] discloses: The prompt generator 278 generates input prompt to be provided to generative AI models);
responsive to inputting the generative MLM prompt into the second generative MLM, generating, via the second generative MLM, one or more search terms (Jain [0079] discloses: The natural language phrase may be generated using a generative AI model and a prompt, such as the prompt 327 in FIG. 3C. The generative AI model may comprise one of the machine learning models 282 in FIG. 2D. In step 408, a set of search results is identified using the natural language phrase); and
performing a keyword search on the plurality of documents based on the one or more search terms, wherein each document of the plurality of documents comprises metadata(Jain [0079] discloses: the set of search results may be generated using a search and knowledge management system or a search engine. The set of search results may include the first search result 324 in FIG. 3C. In step 410, the set of search results is ranked. The set of search results may be ranked based on the relevance of the search results to the search query acquired in step 402).
wherein the metadata includes a timestamp corresponding to a last time a respective document was opened (Jain [0044] discloses: The metadata for the word processing document may include an identification of the owner of the document, a timestamp associated with when the document was last modified, a file size for the document, and access permissions for the document); and
performing the keyword search on the plurality of documents comprises ranking a document in the plurality of documents higher based on the timestamp of the respective document (Jain [0044] discloses: The ranking path 244 may determine the ranking of the relevant documents based on the number of times that a search query term appears within the content or metadata for a document, whether the search query term matches a key word for a document, and how recently a document was created or last modified).
Regarding claim 17, Jain discloses: The non-transitory computer-readable medium of claim 15, wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the first query embedding being within a threshold similarity from a second query embedding associated with the document (Jain [0018; 0056] discloses: a search and knowledge management system may be used to identify the top ten documents for a particular search query and the inputted prompt to a machine learning model (e.g., a generative AI model) may include the top ten documents along with a text directive to reference a subset of the top ten documents that were used to generate the response), and including the selected document in the subset, wherein each document of the plurality of documents comprises metadata, wherein the metadata indicates at least one of (i) whether a user has permission to open a respective document (Jain [0044] discloses: The metadata for the word processing document may include an identification of the owner of the document, a timestamp associated with when the document was last modified, a file size for the document, and access permissions for the document); or (ii) a timestamp corresponding to a last time a respective document was modified, and wherein selecting the subset of the plurality of documents comprises selecting a document of the plurality of documents based on the metadata, and including the selected document in the subset (Jain [0056] discloses: a subset of the set of relevant documents may be identified (e.g., the top thirty ranked documents) based on the ranking and summary information or snippets may be acquired from the search index 204 for each document of the subset of the set of relevant documents).
Regarding claim 20, Jain discloses: The non-transitory computer-readable medium of claim 15, the operations further comprising generating, using an embedding model, the first query embedding based on the generative MLM prompt (Jain [0064] discloses: The prompt generator 278 generates input prompt to be provided to generative AI models).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7, 8, 13, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (US 20240256582, hereafter Jain) in view of Garner (US 20240296279).
Regarding claim 7, Jain didn’t disclose, but Gardner discloses: The method of claim 1, wherein a citation of the one or more citations comprises a link to a document in the subset (Gardner [0085] discloses: each citation among the one or more citations as presented in the generated response or structured object 365 within the communication session may include one of a text-only citation or a text citation together with a navigation link. The navigation link may be a link (e.g., a hyperlink) to a cited portion of the data item or a cited portion of the portion of the data item from which each corresponding requested information or each corresponding portion of the requested information was extracted).
Jain and Gardner are analogous art because they are in the same field of endeavor, or utilizing generative artificial intelligence (AI) techniques to automatically generate and display summaries of search results. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jain, to include the teaching of Gardner, in order for implementing confidence enhancement for responses by document-based large language models. The suggestion to combine is to generate NL responses that are in a useful form while handling the backend processing in both an efficient and high-performance manner that produces highly accurate and customized results that are specific to the user.
Regarding claim 8, Jain as modified discloses: The method of claim 1, wherein a citation of the one or more citations comprises a link to a document portion of a document in the subset (Gardner [0085] discloses: each citation among the one or more citations as presented in the generated response or structured object 365 within the communication session may include one of a text-only citation or a text citation together with a navigation link. The navigation link may be a link (e.g., a hyperlink) to a cited portion of the data item or a cited portion of the portion of the data item from which each corresponding requested information or each corresponding portion of the requested information was extracted).
Jain and Gardner are analogous art because they are in the same field of endeavor, or utilizing generative artificial intelligence (AI) techniques to automatically generate and display summaries of search results. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jain, to include the teaching of Gardner, in order for implementing confidence enhancement for responses by document-based large language models. The suggestion to combine is to generate NL responses that are in a useful form while handling the backend processing in both an efficient and high-performance manner that produces highly accurate and customized results that are specific to the user.
Regarding claim 13, Jain as modified discloses: The system of claim 10, wherein a citation of the one or more citations comprises at least one of a link to a document in the subset, or a link to a document portion of a document in the subset (Gardner [0085] discloses: each citation among the one or more citations as presented in the generated response or structured object 365 within the communication session may include one of a text-only citation or a text citation together with a navigation link. The navigation link may be a link (e.g., a hyperlink) to a cited portion of the data item or a cited portion of the portion of the data item from which each corresponding requested information or each corresponding portion of the requested information was extracted).
Jain and Gardner are analogous art because they are in the same field of endeavor, or utilizing generative artificial intelligence (AI) techniques to automatically generate and display summaries of search results. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jain, to include the teaching of Gardner, in order for implementing confidence enhancement for responses by document-based large language models. The suggestion to combine is to generate NL responses that are in a useful form while handling the backend processing in both an efficient and high-performance manner that produces highly accurate and customized results that are specific to the user.
Regarding claim 18, Jain as modified discloses: The non-transitory computer-readable medium of claim 15, wherein a citation of the one or more citations comprises a link to a document portion of a document in the subset (Gardner [0085] discloses: each citation among the one or more citations as presented in the generated response or structured object 365 within the communication session may include one of a text-only citation or a text citation together with a navigation link. The navigation link may be a link (e.g., a hyperlink) to a cited portion of the data item or a cited portion of the portion of the data item from which each corresponding requested information or each corresponding portion of the requested information was extracted).
Jain and Gardner are analogous art because they are in the same field of endeavor, or utilizing generative artificial intelligence (AI) techniques to automatically generate and display summaries of search results. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jain, to include the teaching of Gardner, in order for implementing confidence enhancement for responses by document-based large language models. The suggestion to combine is to generate NL responses that are in a useful form while handling the backend processing in both an efficient and high-performance manner that produces highly accurate and customized results that are specific to the user.
Regarding claim 19, Jain as modified discloses: The non-transitory computer-readable medium of claim 15, wherein a citation of the one or more citations comprises a link to a document in the subset (Gardner [0085] discloses: each citation among the one or more citations as presented in the generated response or structured object 365 within the communication session may include one of a text-only citation or a text citation together with a navigation link. The navigation link may be a link (e.g., a hyperlink) to a cited portion of the data item or a cited portion of the portion of the data item from which each corresponding requested information or each corresponding portion of the requested information was extracted).
Jain and Gardner are analogous art because they are in the same field of endeavor, or utilizing generative artificial intelligence (AI) techniques to automatically generate and display summaries of search results. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jain, to include the teaching of Gardner, in order for implementing confidence enhancement for responses by document-based large language models. The suggestion to combine is to generate NL responses that are in a useful form while handling the backend processing in both an efficient and high-performance manner that produces highly accurate and customized results that are specific to the user.
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/CINDY NGUYEN/Examiner, Art Unit 2156
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