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
This responds to Applicant’s Arguments/Remarks filed 12/01/2025. Claims 1-7, 12-13, 15-20 have been amended. Claim 14 have been cancelled. Claim 21 have been newly added. Claims 1-13, 15-21 are now pending in this Application
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-13, 15-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-9, 11-13, 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable Shek et al (U.S. Pub No. 2021/0165967), and in view of Colon et al (U.S. Patent No. 12,411,945).
As per claim 1, Shek discloses a method of personalizing conversational interactions with a database system, the method comprising:
obtaining, by a chatbot service at the database system, a conversational user input associated with a user of a client device coupled to the database system over a network (par [0013] assign relevancy scores to the common entities and relationships in conversation);
identifying, at the database system, one or more data records of a plurality of data records associated with the user in a database of the database system based on a relationship between a numerical representation of the conversational user input and respective numerical representations of the one or more data records (Par [0014]);
generating, by the chatbot service at the database system, an augmented conversational user input comprising the conversational user input using content of the one or more data records from the database; and providing, by the chatbot service at the database system, a conversational response to the conversational user input to the client device based at least in part on the personalized conversational (Par [0026-0034]).
Shek discloses chatbot but silence about providing, by the chatbot service at the database system, the augmented conversational user input to a large language model-based (LLM-based) chatbot service; receiving, by the chatbot service at the database system from the LLM-based chatbot service, a personalized conversational response to the augmented conversational user input generated by the LLM-based chatbot service; response generated by the LLM-based chatbot service.
However, Colon discloses providing, by the chatbot service at the database system, the augmented conversational user input to a large language model-based (LLM-based) chatbot service; receiving, by the chatbot service at the database system from the LLM-based chatbot service, a personalized conversational response to the augmented conversational user input generated by the LLM-based chatbot service; response generated by the LLM-based chatbot service (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Colon into the teaching of Shek in order to improve the client experience (Col 17 lines 54-55).
As per claim 2, Shek discloses the method of claim 1, the content comprising a relevant subset of data from the one or more data records associated with the user, wherein the augmented conversational user input is configured to influence a response generated by the chatbot service to the conversational user input in a manner that reflects the relevant subset of data associated with the user, resulting in the personalized conversational response (Par [0014, 0038-0040] and fig 3C).
Shek discloses chatbot but silence about LLM-based. However, Colon discloses LLM-based (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Colon into the teaching of Shek in order to improve the client experience (Col 17 lines 54-55).
As per claim 3, Colon disclose the method of claim 2, wherein the LLM-based chatbot service comprises a service at an external system coupled to the database system over the network, wherein the service is configured to generate the response using at least one of a large language model (LLM) or a generative pre-trained transformer (GPT) model (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56)
As per claim 4, Shek discloses the method of claim 1, further comprising generating, at the database system, a personal model associated with the user based at least in part on user data associated with the user, the user data comprising the plurality of data records associated with the user, wherein identifying the one or more data records comprises identifying the one or more data records using the personal model (par [0036-0037]).
As per claim 5, Shek discloses the method of claim 4, wherein the personal model comprises numerical representations of a plurality of data records associated with the user in the database, (Par [0014, 0027-0028, 0036-0037]).
As per claim 6, Shek discloses the method of claim 5, wherein generating the augmented conversational user input comprises: obtaining textual content of the one or more data records from the database; and combining the textual content of the one or more data records with input textual content of the conversational user input into an input prompt (Par [0026-0034]).
As per claim 7, Shek discloses the method of claim 6, wherein the input prompt is structured or formatted to ground the LLM-based chatbot service responding to the input textual content using the textual content of the one or more data records at the database system (par [0019]).
Shek discloses chatbot but silence about LLM-based. However, Colon discloses LLM-based (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Colon into the teaching of Shek in order to improve the client experience (Col 17 lines 54-55).
As per claim 8, Shek discloses the method of claim 4, further comprising providing, by an application platform of the database system to the client device, a virtual application comprising a user interface for receiving user configuration information indicative of the plurality of data records associated with the user to be utilized for generating the personal model (Par [0021, 0079] user interface).
As per claim 9, Shek discloses the method of claim 4, further comprising tokenizing the user data prior to generating the personal model, wherein the personal model numerically or mathematically represents the user data based on the tokenized user data (Par [0014, 0027-0028, 0036-0037]).
As per claim 11, Shek discloses the method of claim 1, wherein obtaining the conversational user input comprises receiving, over the network, the conversational user input via a graphical user interface (GUI) associated with an instance of a virtual application provided by an application platform at the database system within a client application at the client device (Par [0021, 0079] user interface).
As per claim 12, Shek discloses the method of claim 1, further comprising converting the conversational user input to a numerical representation using a word embedding algorithm or encoder model, wherein identifying the relevant subset of data comprises identifying the relevant subset of data based on a difference between the numerical representation of the conversational user input and respective numerical representations of the data associated with the user (Par [0014, 0027-0028, 0036-0037]).
As per claim 13, Shek discloses the method of claim 12, wherein identifying the one or more data records comprises identifying a data record associated with the user in the database closest to the conversational user input based the difference between the numerical representation of input textual content of the conversational user input and the respective numerical representation of stored textual content of the data record data associated with the user (Par [0014, 0019, 0027-0028]).
As per claim 15, Shek discloses the method of claim 1, wherein the LLM-based chatbot service comprises an application programming interface (API) associated with an external system distinct from the database system (Par [0021]).
Shek discloses chatbot but silence about LLM-based. However, Colon discloses LLM-based (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Colon into the teaching of Shek in order to improve the client experience (Col 17 lines 54-55).
As per claim 16, Shek discloses at least one non-transitory machine-readable storage medium that provides instructions that, when executed by at least one processor, are configurable to cause the at least one processor to perform operations comprising:
obtaining, by a chatbot service at the database system, a conversational user input associated with a user of a client device coupled to the database system over a network (par [0013] assign relevancy scores to the common entities and relationships in conversation);
identifying, at the database system, one or more data records of a plurality of data records associated with the user in a database of the database system based on a relationship between a numerical representation of the conversational user input and respective numerical representations of the one or more data records (Par [0014]);
generating, by the chatbot service at the database system, an augmented conversational user input comprising the conversational user input using content of the one or more data records from the database; and providing, by the chatbot service at the database system, a conversational response to the conversational user input to the client device based at least in part on the personalized conversational (Par [0026-0034]).
Shek discloses chatbot but silence about providing, by the chatbot service at the database system, the augmented conversational user input to a large language model-based (LLM-based) chatbot service; receiving, by the chatbot service at the database system from the LLM-based chatbot service, a personalized conversational response to the augmented conversational user input generated by the LLM-based chatbot service; response generated by the LLM-based chatbot service.
However, Colon discloses providing, by the chatbot service at the database system, the augmented conversational user input to a large language model-based (LLM-based) chatbot service; receiving, by the chatbot service at the database system from the LLM-based chatbot service, a personalized conversational response to the augmented conversational user input generated by the LLM-based chatbot service; response generated by the LLM-based chatbot service (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Colon into the teaching of Shek in order to improve the client experience (Col 17 lines 54-55).
As per claim 17, Colon discloses the at least one non-transitory machine-readable storage medium of claim 16, wherein the chatbot service comprises a service at an external system coupled to the database system over the network, wherein the service is configured to generate the response using at least one of a large language model (LLM) or a generative pre-trained transformer (GPT) model (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
As per claim 18, Shek discloses the at least one non-transitory machine-readable storage medium of claim 16, wherein the instructions are configurable to cause the at least one processor to generate a personal model associated with the user based at least in part on user data associated with the user, wherein identifying the one or more data records comprises identifying the one or more data records using the personal model (Par [0014, 0038-0040] and fig 3C).
As per claim 19, Shek discloses the at least one non-transitory machine-readable storage medium of claim 18, wherein: the personal model comprises numerical representations of a plurality of data records associated with the user in the database (Par [0014, 0027-0028, 0036-0037]); and
generating the augmented conversational user input comprises: obtaining textual content of the one or more data records from the database; and combining the textual content of the one or more data records with input textual content of the conversational user input using the relevant subset of data into an input prompt (Par [0026, 0034]).
As per claim 20, Shek discloses a computing system comprising:
at least one non-transitory machine-readable storage medium that stores software; and at least one processor, coupled to the at least one non-transitory machine-readable storage medium, to execute the software that implements a contextual personalization service and that is configurable to perform operations comprising:
obtaining, by a chatbot service at the database system, a conversational user input associated with a user of a client device coupled to the database system over a network (par [0013] assign relevancy scores to the common entities and relationships in conversation);
identifying, at the database system, one or more data records of a plurality of data records associated with the user in a database of the database system based on a relationship between a numerical representation of the conversational user input and respective numerical representations of the one or more data records (Par [0014]);
generating, by the chatbot service at the database system, an augmented conversational user input comprising the conversational user input using content of the one or more data records from the database; and providing, by the chatbot service at the database system, a conversational response to the conversational user input to the client device based at least in part on the personalized conversational (Par [0026-0034]).
Shek discloses chatbot but silence about providing, by the chatbot service at the database system, the augmented conversational user input to a large language model-based (LLM-based) chatbot service; receiving, by the chatbot service at the database system from the LLM-based chatbot service, a personalized conversational response to the augmented conversational user input generated by the LLM-based chatbot service; response generated by the LLM-based chatbot service.
However, Colon discloses providing, by the chatbot service at the database system, the augmented conversational user input to a large language model-based (LLM-based) chatbot service; receiving, by the chatbot service at the database system from the LLM-based chatbot service, a personalized conversational response to the augmented conversational user input generated by the LLM-based chatbot service; response generated by the LLM-based chatbot service (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Colon into the teaching of Shek in order to improve the client experience (Col 17 lines 54-55).
As per claim 21, Colon discloses the method of claim 1,wherein: the content comprises supplemental textual content from the one or more data records; generating the augmented conversational user input comprises combining the supplemental textual content with textual content of the conversational user input into an augmented conversational user input prompt in a manner that delineates the supplemental textual content from the conversational user input; and the LLM-based chatbot service generates the personalized conversational response to the conversational user input that reflects the supplemental textual content in response to the augmented conversational user input prompt (Col 5 lines 12-43 and col 7 lines 47-67 through col 8 lines 1-56).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shek et al (U.S. Pub No. 2021/0165967), and Colon et al (U.S. Patent No. 12,411,945), and further in view of Castelli et al (U.S. Pub No. 2020/0090659 A1).
As per claim 10, Shek discloses natural language processing but silence about wherein the personal model comprises a bag-of-words model associated with the user.
However, Castelli discloses a bag-of-word (Par [0082]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Castelli into the teachings of Shek in order improve the relevancy of the user (Par [0023]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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March 7, 2026
/THU N NGUYEN/Examiner, Art Unit 2154