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 is a first Office Action for application 18/740,060, filed on 06/11/2024. Claims 1-20 are pending and examined below.
Allowable Subject Matter
Claim 6 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claim 6 limitation
in response to an output of the at least one dynamic computer file of the first category being invalid: executing at least one dynamic computer file of a second file category based on the second relatedness scores is not taught by previous reference art when considered as a whole with claim 1, 5 and other claim 6 limitations. There are reference arts that disclose executing files of a different category after a failed/invalid output like reference art Heidel et al. (US Pub. 2010/0138826), but not in a way that is obvious to combine and teaches all the limitations of claims 1 and 5. Therefore, claim 6 is allowable. Claims 16 is worded differently, but allowable under the same rationale.
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, 5, 7-8, 10-11, 15, 17-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vira et al. (US Pub. 2020/0073895) in view of Crouse et al. (US Pat. 11,676,231).
Regarding claim(s) 1, Vira teaches
A computer-implemented method for assisting a user on a computer-implemented platform, the method comprising: receiving a user input from a user; (Fig. 5; Par. [0088] user input is received (#510))
determining an input category of the user input among a plurality of pre-determined input categories; (Fig. 5; Par. [0089] a group of categories associated with the user input is determined (i.e. pre-determined) by a machine learning model (#520) and then selected by the user (i.e. category is determined; #530-540))
in response to the input category being a first input category: accessing a first database configured to host a plurality of static computer files, each static computer file being associated with a document embedding mapping a content of the associated static computer file; (Par. [0035] in some embodiments the data being accessed by the natural language query (i.e. with document embeddings) is static data (examiner notes that the “computer files” language is explicitly taught by secondary art Crouse as shown below, but is left in here for referential clarity))
determining a first relatedness score for each static computer file based on a comparison of an input embedding associated with the user input with the document embeddings of the plurality of static computer files; and (Par. [0020-1, 36] the characteristics of the user query are determined based on a natural language query (i.e. relatedness score comparing input with document embeddings))
returning a first indication of at least one of the static computer files based on the first relatedness scores to the user; and (Par. [0035] in some embodiments the data being accessed (i.e. returned) is static data)
in response to the input category being a second input category, the second input category being distinct from the first input category: accessing a second database configured to host a plurality of executable dynamic computer files, each dynamic computer file being associated with a service embedding mapping a function of the associated dynamic computer file; (Par. [0035-6] in some embodiments the data being accessed is dynamic data via natural language query)
determining a second relatedness score for each dynamic computer file based on a comparison of the input embedding with the service embeddings of the plurality of dynamic computer files; (Par. [0020-1, 36] the characteristics of the user query are determined based on a natural language query (i.e. relatedness score comparing input embedding with service embeddings))
executing at least one dynamic computer file based on the second relatedness scores; and (Par. [0034, 40] the information platform can call a second or third module (i.e. execute a dynamic computer file) that can run other modules to process the user's query)
returning a second indication of an output of the execution of the at least one dynamic computer file to the user. (Par. [0036] in some embodiments the data being accessed (i.e. returned) is dynamic data)
Vira does not explicitly teach host a plurality of static computer files
However, from the same field Crouse teaches
host a plurality of static computer files (Fig. 5; Col. 15 [Line 65] - Col. 16 [Line 6] at step #502, documents are received from the data repository)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the computer files of Crouse into the virtual assistant platform of Vira. The motivation for this combination would have been to improve processing speed and costs as explained in Crouse (Col. 1 [Lines 28-46]).
Regarding claim(s) 5, Vira and Crouse teach claim 1 as shown above, and Vira further teaches
The method of claim 1, wherein each dynamic computer file is associated with a dynamic computer file category among a plurality of dynamic computer file categories comprising: a machine learning model category, (Par. [0019, 35-6] in some embodiments the data being accessed is dynamic data (including dynamic content) via natural language query, including any number of categories)
a contextual file category, a given contextual file including dynamic content of the computer-implemented user platform, and (Par. [0019, 35-6] in some embodiments the data being accessed is dynamic data (including dynamic content) via natural language query, including any number of categories)
a network communication service category. (Par. [0019, 35-6] in some embodiments the data being accessed is dynamic data (including dynamic content) via natural language query, including any number of categories)
Regarding claim(s) 7, Vira and Crouse teach claim 5 as shown above, and Vira further teaches
The method of claim 5, wherein executing at least one dynamic computer file of a first file category based on the second relatedness scores comprises: inputting a contextual file and the user input into a natural language processing (NLP) service, the contextual file being selected based on the input of the user; and (Par. [0020, 35-6] in some embodiments the data being accessed is dynamic data (including dynamic content) via natural language query (i.e. based on user input) run through a processing service)
returning an output of the NLP service to the user. (Par. [0020] any number of external natural language machine learning services can be used to process (i.e. return) a query)
Regarding claim(s) 8, Vira and Crouse teach claim 1 as shown above, and Vira further teaches
The method of claim 1, further comprising, in response to the input category being a third input category, the third input category being distinct from the first and second input categories: accessing a third database configured to host a plurality of operation template computer files, each operation template computer file being associated with a transaction embedding mapping a function of an associated operation; (Par. [0034, 40] the information platform can return instructions that call (i.e. executable instructions based on template file) a second or third module to process the user's query)
determining a fourth relatedness score for each operation template computer file based on a comparison of the input embedding with the transaction embeddings of the plurality of operation template computer files; (Par. [0020, 35] a number of confidence scores are used for determining relatedness of the user query and the computer files)
selecting at least one operation template computer file based on the fourth relatedness scores; and (Par. [0034, 40] the information platform can return instructions that call a second or third module to process (i.e. select and return results of) the user's query)
returning a set of executable operation instructions based on an application of the input to the at least one operation template computer file to the user. (Par. [0034, 40] the information platform can return instructions that call (i.e. executable instructions based on template file) a second or third module to process (i.e. select and return results of) the user's query)
Regarding claim(s) 10, Vira and Crouse teach claim 1 as shown above, and Vira further teaches
The method of claim 1, wherein determining an input category comprises: determining a confidence score for each input category for the given input, the confidence score being indicative of a probability that the input belong to the input category; and (Par. [0020, 35] a number of confidence scores are used for determining relatedness of the first user query and the query category )
if the confidence score is below a pre-determined threshold: accessing a temporal series of historical input entered by the user; (Par. [0039] the first (i.e. historical input) and second user queries (i.e. second input) are compared to determine if they are related)
identifying a first historical input directly preceding the input; and (Par. [0039] the first (i.e. historical) and second user queries are compared to determine if they are related)
Vira further teaches
determining a second input of the first historical input, and (Par. [0039] the first and second user queries (i.e. second input) are compared to determine if they are related)
Vira further teaches
wherein determining an input category of the input is further based on the first historical input and the second input. (Par. [0039] the first and second user queries are compared to determine if they are related, including based on the categories of the queries)
Regarding claim(s) 11, while worded slightly differently is rejected under the same rationale as claim 1. Vira further teaches
a processor (Fig. 3 #320; Par. [0003] processors are used)
Regarding claim(s) 15, while worded slightly differently is rejected under the same rationale as claim 5.
Regarding claim(s) 17, while worded slightly differently is rejected under the same rationale as claim 7.
Regarding claim(s) 18, while worded slightly differently is rejected under the same rationale as claim 8.
Regarding claim(s) 20, while worded slightly differently is rejected under the same rationale as claim 10.
Claim(s) 2-4 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vira et al. (US Pub. 2020/0073895) in view of Crouse et al. (US Pat. 11,676,231), and further in view of Chang et al. (US Pub. 2006/0123045).
Regarding claim(s) 2, Vira and Crouse teach claim 1 as shown above, and Vira further teaches
The method of claim 1, wherein returning the first indication of at least one of the static computer files based on the first relatedness scores comprises: comparing the first relatedness scores associated with the static computer files; (Par. [0020, 35] a number of confidence scores are used for determining relatedness of the user query and the static computer files)
accessing a content of the set of static computer files, the content of each static computer file comprising sections associated with a section embedding; (Par. [0020, 35] the called modules can access universal knowledge bases (i.e. containing section embeddings) including FAQ and employee manual databases)
determining a third relatedness score for each section of static computer file based on a comparison of the input embedding with the corresponding section embedding; and (Par. [0020, 35] a number of confidence scores are used for determining relatedness of the user query and the static computer files)
returning one or more sections of the set of static computer files based on the third relatedness scores. (Par. [0035] in some embodiments the data being accessed (i.e. returned) is static data)
The combination of Vira and Crouse do not explicitly teach
identifying a set of a pre-determined number of static computer files based on the comparison;
However, from the same field, Chang teaches
identifying a set of a pre-determined number of static computer files based on the comparison; (Fig. 5; Par. [0045] after a count of the number of results (#112), a determination is made of the available space (i.e. a pre-determined number; #114) and compared to the count before eliminating results until the result count is below the available space count (#116-#122))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the pre-determined number of results of Chang into the natural language query of Vira. The motivation for this combination would have been to improve the efficiency of searches as explained in Chang (Par. [0071]).
Regarding claim(s) 3, Vira, Crouse and Chang teach claim 1 as shown above, and Vira further teaches
The method of claim 2, wherein returning one or more sections of the set of static computer files comprises: inputting the one or more sections and the user input into a natural language processing (NLP) service configured to receive sections of static computer files as an input; and (Par. [0020] any number of external natural language machine learning services can be used to process the first query)
returning an output of the NLP service to the user. (Par. [0020] any number of external natural language machine learning services can be used to process (i.e. return) the first query)
Regarding claim(s) 4, Vira, Crouse and Chang teach claim 1 as shown above, and Vira further teaches
The method of claim 3, wherein the NLP service is further configured to: receive a current content of the computer-implemented user platform as a second input, the output of the NLP service being at least in part based on the current content. (Par. [0020] any number of external natural language machine learning services can be used to process the first query)
Regarding claim(s) 12, while worded slightly differently is rejected under the same rationale as claim 2.
Regarding claim(s) 13, while worded slightly differently is rejected under the same rationale as claim 3.
Regarding claim(s) 14, while worded slightly differently is rejected under the same rationale as claim 4.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vira et al. (US Pub. 2020/0073895) in view of Crouse et al. (US Pat. 11,676,231), and further in view of Piskala (US Pub.2025/0095641).
Regarding claim(s) 9, Vira and Crouse teach claim 1 as shown above, and Vira further teaches
inputting the prompt to a natural language processing (NLP) service; and (Par. [0020] any number of external natural language machine learning services can be used to process the first query)
returning an output of the NLP service to the user. (Par. [0020] any number of external natural language machine learning services can be used to process (i.e. return) a query)
The combination of Vira and Crouse do not explicitly teach
The method of claim 1, further comprising, in response to the input category being a fourth input category, the fourth input category being distinct from the first, second and third input categories: inputting a dynamic computer file in a machine learning algorithm (MLA), the MLA being configured to receive a dynamic computer file as an input and output a formatted data structure wrapped in a prompt generated based on the user input;
However, from the same field, Piskala teaches
The method of claim 1, further comprising, in response to the input category being a fourth input category, the fourth input category being distinct from the first, second and third input categories: inputting a dynamic computer file in a machine learning algorithm (MLA), the MLA being configured to receive a dynamic computer file as an input and output a formatted data structure wrapped in a prompt generated based on the user input;( Fig. 4; Par. [0031] at operation #403, the system receives the user input (#401) and tool (#424A) is requested by the LLM (#422) after the LLM reformats (i.e. formatted data structure) the user input into the target format of the tool)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the user input reformation of Piskala into the virtual assistant platform of Vira. The motivation for this combination would have been to improve performance of downstream systems as explained in Piskala (Par. [0014]).
Regarding claim(s) 19, while worded slightly differently is rejected under the same rationale as claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dhotre et al. (US Pub. 2023/0229865) teaches an extensible digital assistant.
Robert Jose et al. (US Pub. 2023/0196033) teaches a system for responding to natural language queries.
Abu Asba et al. (US Pub. 2020/0302936) teaches integrating third party natural language understanding systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to J MITCHELL CURRAN whose telephone number is (469)295-9081. The examiner can normally be reached M-F 8:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at (571) 272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J MITCHELL CURRAN/Examiner, Art Unit 2169
/YU ZHAO/Primary Examiner, Art Unit 2169