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 .
Claims 1-20 are active in this application.
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 1-5, 7-10, 12, 14-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-8, 13, 15 and 18 of Patent No. 12,373,391. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims recite substantially similar claim limitations as depicted in the table below.
Instant Application
12,373,391
1. A computer-implemented method comprising:
prior to a client device searching a content management system, extracting raw facet data from a plurality of content items stored in the content management system;
determining one or more facet content groups according to the extracted raw facet data;
generating, based on the one or more facet content groups, a facet prompt comprising instructions to a large language model to generate a mapping between content items of the plurality of content items and at least one subset of the one or more facet content groups; and
providing the facet prompt to the large language model prior to the client device searching the content management system to generate a dynamic facet comprising the mapping for the at least one subset of the one or more facet content groups.
2. The computer-implemented method of claim 1, further comprising identifying the at least one subset of the one or more facet content groups based on ranking the one or more facet content groups according to a threshold range of content items associated with each of the one or more facet content groups.
3. The computer-implemented method of claim 2, further comprising generating the facet prompt that includes instructions to the large language model to abstract groupings from the at least one subset of the one or more facet content groups.
4. The computer-implemented method of claim 1, further comprising providing the dynamic facet to a graphical user interface of the client device, wherein the dynamic facet is selectable by the client device.
5. The computer-implemented method of claim 4, further comprising: in response to receiving a selection of the dynamic facet by a client device, generating one or more additional facet content groups by grouping one or more content items of the dynamic facet according to the raw facet data; generate an additional facet prompt from the one or more additional facet content groups; and generate an additional dynamic facet by providing the additional facet prompt to the large language model.
7. The computer-implemented method of claim 1, further comprising: determining permissions of a client device to access one or more content items associated with the dynamic facet; determining that the client device does not have permission to access a content item associated with the dynamic facet; and removing the content item from the dynamic facet.
8. A system comprising: at least one processor; and a non-transitory computer-readable medium storing instructions which, when executed by the at least one processor, cause the system to: extract raw facet data from a plurality of content items stored in a content management system;
determine, based on the raw facet data, one or more facet content groups by grouping the plurality of content items according to topics identified from the raw facet data;
generate, based on the one or more facet content groups, a facet prompt comprising instructions to a large language model to generate a mapping between content items of the plurality of content items and at least one subset of the one or more facet content groups; and
provide the facet prompt to the large language model to generate a dynamic facet comprising the mapping for the at least one subset of the one or more facet content groups.
9. The system of claim 8, further storing instruction which, when executed by the at least one processor, cause the system to generate the dynamic facet as part of pre-processing steps performed prior to a client device searching the content management system.
10. The system of claim 9, further storing instruction which, when executed by the at least one processor, cause the system to: identify the plurality of content items in response to a user of the client device selecting a folder or sub-folder within the content management system; and in response to identifying the plurality of content items, extract the raw facet data from the plurality of content items.
12. The system of claim 8, further storing instructions which, when executed by the at least one processor, cause the system to extract the raw facet data from the plurality of content items by: extracting one or more metadata tags for a content item of the plurality of content items comprising a topic of the content item; extracting access data packets for the content item that indicates one or more client devices that accessed the content item; or extracting a subfolder location for a content item of the plurality of content items.
14. They system of claim 8, further storing instruction which, when executed by the at least one processor, cause the system to: providing, for display on a graphical user interface of a client device, the dynamic facet that corresponds to a first facet content group and a second facet content group; and in response to receiving a selection of the dynamic facet by the client device, providing, for display on the graphical user interface, content items associated with the first facet content group and the second facet content group.
15. A non-transitory computer-readable medium storing executable instructions which, when executed by at least one processor, cause the at least one processor to:
extract raw facet data from a plurality of content items stored in a content management system;
determine, independent of user preferences or instructions, one or more facet content groups by grouping the plurality of content items according to the raw facet data;
generate, based on the one or more facet content groups, a facet prompt comprising instructions to a large language model to generate a mapping between content items of the plurality of content items and at least one subset of the one or more facet content groups; and
provide the facet prompt to the large language model to generate, independent of user preferences or instructions, a dynamic facet comprising the mapping for the at least one subset of the one or more facet content groups.
16. The non-transitory computer-readable medium of claim 15, further storing instructions which, when executed by the at least one processor, cause the at least one processor to extract the raw facet data in response to a client device navigating the content management system.
17. The non-transitory computer-readable medium of claim 16, wherein navigating the content management system comprises a user of the client device selecting a folder or sub-folder within the content management system.
18. The non-transitory computer-readable medium of claim 17, further storing instruction which, when executed by the at least one processor, cause the at least one processor to generate the dynamic facet as part of pre-processing steps performed prior to the client device searching the content management system.
19. The non-transitory computer-readable medium of claim 15, further storing instruction which, when executed by the at least one processor, cause the at least one processor to provide the dynamic facet to a graphical user interface of a client device, wherein the dynamic facet is selectable by the client device.
1. A system comprising: at least one processor; and a non-transitory computer-readable medium storing instructions which, when executed by the at least one processor, cause the system to:
prior to a client device searching a content management system and in response to the client device navigating the content management system; extract raw facet data from a plurality of content items stored in the content management system, wherein navigating the content management system comprises a user of the client device selecting a folder or sub-folder within the content management system;
determine a plurality of facet content groups by grouping the plurality of content items according to the raw facet data;
identify a subset of the plurality of facet content groups based on ranking the plurality of facet content groups according to a threshold range of content items associated with each of the plurality of facet content groups;
generate a facet prompt from the subset of the plurality of facet content groups, wherein the facet prompt includes instructions to a large language model that are created in response to the client device navigating the content management system and prior to the client device performing the search of the content management system;
generate a dynamic facet by providing the facet prompt to the large language model, wherein the facet prompt comprises the subset of the plurality of facet content groups; and provide the dynamic facet to a graphical user interface of the client device, wherein the dynamic facet is selectable by the client device.
4. The system of claim 1, further storing instructions, which when executed by the at least one processor cause the system to: generate the dynamic facet by generating the facet prompt that includes instructions to the large language model to abstract groupings from the subset of the plurality of facet content groups, wherein the dynamic facet is generated as part of pre-processing steps performed prior to the client device searching the content management system.
6. The system of claim 1, further storing instructions, which when executed by the at least one processor cause the system to: in response to receiving a selection of the dynamic facet by a client device, generate one or more additional facet content groups by grouping one or more content items of the dynamic facet according to the raw facet data; generate an additional facet prompt from the one or more additional facet content groups; and generate an additional dynamic facet by providing the additional facet prompt to the large language model.
5. The system of claim 1, further storing instructions, which when executed by the at least one processor cause the system to: determine permissions of a client device to access one or more content items associated with the dynamic facet; determine that the client device does not have permission to access a content item associated with the dynamic facet; and remove the content item from the dynamic facet.
7. A computer-implemented method comprising: prior to a client device searching a content management system and in response to a client device navigating a content management system, extracting raw facet data from a plurality of content items stored in the content management system;
determining one or more facet content groups by grouping the plurality of content items according to the raw facet data;
generating a facet prompt comprising the one or more facet content groups, wherein the facet prompt includes instructions to a large language model that are created in response to the client device navigating the content management system and prior to the client device performing the search of the content management system; and
generating a dynamic facet by providing the facet prompt to the large language model, wherein the facet prompt comprises a subset of the one or more facet content groups.
13. The computer-implemented method of claim 7, further comprising: selecting a subset of the one or more facet content groups based on a ranking of the one or more facet content groups; and generating a facet prompt for the subset of the one or more facet content groups, the facet prompt comprising instructions for the large language model to determine the dynamic facet that includes content items corresponding to the subset of the one or more facet content groups, wherein the facet prompt is generated as a part of pre-processing steps performed prior to the client device searching the content management system.
8. The computer-implemented method of claim 7, wherein extracting the raw facet data from the plurality of content items comprises at least one of: extracting one or more metadata tags for a content item of the plurality of content items comprising a topic of the content item; extracting access data packets for the content item that indicates one or more client devices that accessed the content item; or extracting operation data packets for the content item that indicates one or more operations performed on the content item.
15. The computer-implemented method of claim 7, wherein generating the dynamic facet by providing the facet prompt to the large language model further comprises: generating the dynamic facet comprising a first facet content group and a second facet content group, wherein the first facet content group relates to a first topic and the second facet content group relates to a second topic; providing, for display on a graphical user interface of a client device the dynamic facet comprising the first topic and the second topic, wherein the first topic differs from the second topic; and in response to receiving a selection of the dynamic facet by the client device, causing the graphical user interface to update and display content items associated with the first facet content group and the second facet content group.
16. A non-transitory computer-readable medium storing executable instructions which, when executed by at least one processor, cause the at least one processor to: prior to a client device searching a content management system and in response to a client device navigating a content management system, extract raw facet data from a plurality of content items stored in the content management system;
determine one or more facet content groups by grouping the plurality of content items according to the raw facet data;
generate a facet prompt from the one or more facet content groups, wherein the facet prompt includes instructions to a large language model that are created in response to the client device navigating the content management system and prior to the client device performing the search of the content management system;
generate a dynamic facet by providing the facet prompt to the large language model, wherein the facet prompt comprises a subset of the one or more facet content groups; and provide, for display on a graphical user interface of the client device the dynamic facet, wherein the dynamic facet is selectable by the client device.
Examiner's Note
The Examiner respectfully requests of the Applicants in preparing responses, to fully consider the entirety of the references as potentially teaching all or part of the claimed invention.
It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. "The use of patents as references is not Limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the Literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including non-preferred embodiments (see MPEP 2123).
The Examiner has cited particular locations in the reference(s) as applied to the claims below for the convenience of the Applicants. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claims, typically other passages and figures will apply as well.
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sankaranarayanan (US 2025/0165543 ).
Regarding claim 1, Sankaranarayanan discloses a computer-implemented method comprising:
prior to a client device searching a content management system, extracting raw facet data from a plurality of content items stored in the content management system ([0032], “The techniques leverage the capability of large language models to distill knowledge representations and identify facets from any kind of data, including unstructured text and other types of UGC. Since trained LLMs can perform extraction of such facets from any data, the described techniques can automatically generate facets from any set of information, e.g., UGC such as entity reviews (place reviews, or other reviews), blogs or other text writing, audio such as podcasts, video” and [0057]);
determining one or more facet content groups according to the extracted raw facet data ([0032], [0059]-[0060], “the prompt may include further commands for the LLM, e.g., “generate questions to which this content item is an answer; then, compress the questions to short phrases of less than twenty characters.” In some implementations, generated facets may be associated with categories. For example, for a restaurant, the facets “accessible restroom,” “accessible stairway,” “child seat,” etc. may all be associated with the category “accessibility” while the facets “great drinks,” “rock music,” “large screen TV,” may be associated with “party-friendly.”);
generating, based on the one or more facet content groups, a facet prompt comprising instructions to a large language model ([0045], “[0045] Machine learning (ML) model 358 may provide various functions. For example, ML model 358 may include a large language model (LLM) that can receive prompts as input (e.g., text prompts including commands to generate facets and other information such as context and parameters for the command, or characteristics of output responses provided by the LLM). For example, such functions may include automatically generating facets from user-generated content in response to a received prompt”) to generate a mapping between content items of the plurality of content items and at least one subset of the one or more facet content groups ([0056]-[0060] and [0063]); and
providing the facet prompt to the large language model prior to the client device searching the content management system to generate a dynamic facet comprising the mapping for the at least one subset of the one or more facet content groups ([0060]-[0063] and [0072]).
Regarding claim 2, Sankaranarayanan discloses identifying the at least one subset of the one or more facet content groups based on ranking the one or more facet content groups according to a threshold range of content items associated with each of the one or more facet content groups ([0059]-[0063] and [0066]).
Regarding claim 3, Sankaranarayanan discloses generating the facet prompt that includes instructions to the large language model to abstract groupings from the at least one subset of the one or more facet content groups ([0059]-[0063]).
Regarding claim 4, Sankaranarayanan discloses providing the dynamic facet to a graphical user interface of the client device, wherein the dynamic facet is selectable by the client device ([0070]-[0072]).
Regarding claim 5, Sankaranarayanan discloses in response to receiving a selection of the dynamic facet by a client device, generating one or more additional facet content groups by grouping one or more content items of the dynamic facet according to the raw facet data; generate an additional facet prompt from the one or more additional facet content groups; and generate an additional dynamic facet by providing the additional facet prompt to the large language model. Please see [0070]-[0075].
Regarding claim 6, Sankaranarayanan discloses in response to receiving a search of the content management system from the client device, updating the dynamic facet by filtering the content items included in the mapping for the at least one subset of the one or more facet content groups according to one or more terms of the search ([0061]-[0067] and [0070]-[0072]).
Regarding claim 7, Sankaranarayanan discloses determining permissions of a client device to access one or more content items associated with the dynamic facet ([0063], [0066] and [0077]-[0078]); determining that the client device does not have permission to access a content item associated with the dynamic facet ([0063], [0066] and [0077]-[0078]); and removing the content item from the dynamic facet ([0061], and [0066]-[0067]).
Regarding claim 8, Sankaranarayanan discloses a system (Figure 8) comprising:
at least one processor (Figure 8); and
a non-transitory computer-readable medium storing instructions (Figure 8) which, when executed by the at least one processor, cause the system to:
extract raw facet data from a plurality of content items stored in a content management system ([0032], “The techniques leverage the capability of large language models to distill knowledge representations and identify facets from any kind of data, including unstructured text and other types of UGC. Since trained LLMs can perform extraction of such facets from any data, the described techniques can automatically generate facets from any set of information, e.g., UGC such as entity reviews (place reviews, or other reviews), blogs or other text writing, audio such as podcasts, video” and [0057]);
determine, based on the raw facet data, one or more facet content groups by grouping the plurality of content items according to topics identified from the raw facet data ([0032], [0059]-[0060], “the prompt may include further commands for the LLM, e.g., “generate questions to which this content item is an answer; then, compress the questions to short phrases of less than twenty characters.” In some implementations, generated facets may be associated with categories. For example, for a restaurant, the facets “accessible restroom,” “accessible stairway,” “child seat,” etc. may all be associated with the category “accessibility” while the facets “great drinks,” “rock music,” “large screen TV,” may be associated with “party-friendly.”);
generate, based on the one or more facet content groups, a facet prompt comprising instructions to a large language model ([0045], “[0045] Machine learning (ML) model 358 may provide various functions. For example, ML model 358 may include a large language model (LLM) that can receive prompts as input (e.g., text prompts including commands to generate facets and other information such as context and parameters for the command, or characteristics of output responses provided by the LLM). For example, such functions may include automatically generating facets from user-generated content in response to a received prompt”) to generate a mapping between content items of the plurality of content items and at least one subset of the one or more facet content groups ([0056]-[0060] and [0063]); and
provide the facet prompt to the large language model to generate a dynamic facet comprising the mapping for the at least one subset of the one or more facet content groups ([0060]-[0063] and [0072]).
Regarding claim 9, Sankaranarayanan discloses storing instruction which, when executed by the at least one processor, cause the system to generate the dynamic facet as part of pre-processing steps performed prior to a client device searching the content management system ([0061]-[0067] and [0070]-[0072]).
Regarding claim 10, Sankaranarayanan discloses The system of claim 9, further storing instruction which, when executed by the at least one processor, cause the system to: identify the plurality of content items in response to a user of the client device selecting a folder or sub-folder within the content management system ([0092]-[0095]); and in response to identifying the plurality of content items, extract the raw facet data from the plurality of content items ([0032], [0057]).
Regarding claim 11, Sankaranarayanan discloses storing instruction which, when executed by the at least one processor, cause the system to: determine the at least one subset of the one or more facet content groups based on ranking the one or more facet content groups according to a threshold range of content items associated with each of the one or more facet content groups ([0059]-[0063] and [0066]); and generate the dynamic facet by generating the facet prompt that includes instructions to the large language model to abstract groupings from the at least one subset of the one or more facet content groups ([0059]-[0063] and [0066]).
Regarding claim 12, Sankaranarayanan discloses storing instructions which, when executed by the at least one processor, cause the system to extract the raw facet data from the plurality of content items by: extracting one or more metadata tags for a content item of the plurality of content items comprising a topic of the content item ([0032], [0059]-[0060] and [0102]); extracting access data packets for the content item that indicates one or more client devices that accessed the content item ([0032], [0059]-[0060] and [0102]); or extracting a subfolder location for a content item of the plurality of content items ([0092]-[0095]).
Regarding claim 13, Sankaranarayanan discloses storing instructions which, when executed by the at least one processor, cause the system to extract the raw facet data from the plurality of content items by: extracting one or more word combinations from a file name for a content item of the plurality of content items ([0060], [0064]); extracting a file type for the content item of the plurality of content items ([0060], [0064] and [0080]); or extracting operation data packets for the content item that indicates one or more operations performed on the content item ([0060]-[0063]).
Regarding claim 14, Sankaranarayanan discloses storing instruction which, when executed by the at least one processor, cause the system to: providing, for display on a graphical user interface of a client device, the dynamic facet that corresponds to a first facet content group and a second facet content group (Figure 7 and corresponding text); and in response to receiving a selection of the dynamic facet by the client device, providing, for display on the graphical user interface, content items associated with the first facet content group and the second facet content group (Figure 7 and [0078]-[0082]).
Regarding claim 15, Sankaranarayanan discloses non-transitory computer-readable medium storing executable instructions (Figure 8) which, when executed by at least one processor, cause the at least one processor to:
extract raw facet data from a plurality of content items stored in a content management system ([0032], “The techniques leverage the capability of large language models to distill knowledge representations and identify facets from any kind of data, including unstructured text and other types of UGC. Since trained LLMs can perform extraction of such facets from any data, the described techniques can automatically generate facets from any set of information, e.g., UGC such as entity reviews (place reviews, or other reviews), blogs or other text writing, audio such as podcasts, video” and [0057]);
determine, independent of user preferences or instructions, one or more facet content groups by grouping the plurality of content items according to the raw facet data ([0032], [0059]-[0060], “the prompt may include further commands for the LLM, e.g., “generate questions to which this content item is an answer; then, compress the questions to short phrases of less than twenty characters.” In some implementations, generated facets may be associated with categories. For example, for a restaurant, the facets “accessible restroom,” “accessible stairway,” “child seat,” etc. may all be associated with the category “accessibility” while the facets “great drinks,” “rock music,” “large screen TV,” may be associated with “party-friendly.”);
generate, based on the one or more facet content groups, a facet prompt comprising instructions to a large language model ([0045], “[0045] Machine learning (ML) model 358 may provide various functions. For example, ML model 358 may include a large language model (LLM) that can receive prompts as input (e.g., text prompts including commands to generate facets and other information such as context and parameters for the command, or characteristics of output responses provided by the LLM). For example, such functions may include automatically generating facets from user-generated content in response to a received prompt”) to generate a mapping between content items of the plurality of content items and at least one subset of the one or more facet content groups ([0056]-[0060] and [0063]); and
provide the facet prompt to the large language model to generate, independent of user preferences or instructions, a dynamic facet comprising the mapping for the at least one subset of the one or more facet content groups ([0060]-[0063] and [0072]).
Regarding claim 16, Sankaranarayanan discloses storing instructions which, when executed by the at least one processor, cause the at least one processor to extract the raw facet data in response to a client device navigating the content management system ([0032], “The techniques leverage the capability of large language models to distill knowledge representations and identify facets from any kind of data, including unstructured text and other types of UGC. Since trained LLMs can perform extraction of such facets from any data, the described techniques can automatically generate facets from any set of information, e.g., UGC such as entity reviews (place reviews, or other reviews), blogs or other text writing, audio such as podcasts, video” and [0057]).
Regarding claim 17, Sankaranarayanan discloses wherein navigating the content management system comprises a user of the client device selecting a folder or sub-folder within the content management system ([0078], [0092]-[0095]).
Regarding claim 18, Sankaranarayanan discloses The non-transitory computer-readable medium of claim 17, further storing instruction which, when executed by the at least one processor, cause the at least one processor to generate the dynamic facet as part of pre-processing steps performed prior to the client device searching the content management system ([0060]-[0063] and [0072]).
Regarding claim 19, Sankaranarayanan discloses storing instruction which, when executed by the at least one processor, cause the at least one processor to provide the dynamic facet to a graphical user interface of a client device, wherein the dynamic facet is selectable by the client device ([0060]-[0063] and [0072]).
Regarding claim 20, Sankaranarayanan discloses The non-transitory computer-readable medium of claim 19, further storing instruction which, when executed by the at least one processor, cause the at least one processor to update the dynamic facet ([0061]-[0067] and [0070]-[0072]) in response to one or more of: receiving a selection of the dynamic facet via the graphical user interface of the client device ([0061]-[0067] and [0070]-[0072]); receiving a selection of a content item associated with the dynamic facet via the graphical user interface of the client device ([0060]-[0067] and [0070]-[0072]); or receiving a search of the content management system by the client device ([0078]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sateli (US 2025/0238347) discloses method and apparatus of monitoring and managing a generative AI system.
Burton (US 2025/0165717) discloses multilevel data analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERILYN P NGUYEN whose telephone number is 571-272-4026. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571) 272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERILYN P NGUYEN/ Primary Examiner, Art Unit 2153
April 30, 2026
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