DETAILED ACTION
Statement of claims
The present amended application includes:
Claims 1, 17 and 20 are amended
Claims 6-7, 10 and 18-19 are cancelled
Claims 1 , 17 , 20 are pending independent claims and claims 2-5,8-9, 11-16 are pending dependent claims in this application.
Claims 1-5, 8-9, 11-17 and 20 remain pending in the application. Claims 1-5, 8-9, 11-17 and 20 are being considered on the merits.
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 .
Response to Arguments
Claim Rejection(s) under 35 U.S.C. 103
Applicant argues that:
“The rejection is respectfully traversed. With respect to independent claims 1, 17, and 20, each has been amended to recite that the selected prompt to the generative Al service use the response to the first prompt to prompt the generative Al service to construct a query to a third party service and using a response to the selected prompt to query the third party service, receive a response from the third party service, and use the response from the third party service to further execute the server-side program. Support for the amendments is found, without limitation, in the Specification at [0020], [0026], [0030] - [0031], and Figure 2B.".
In response, Applicant’s arguments have been considered but are moot in view of new ground rejection based on Singh et al. (US 2024/0296315) and Brown et al. (US 2019/0042988)
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.
Claim(s) 1-5, 8-9, 11-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (US 2024/0296315, Singh hereinafter) in view of Brown et al. (US 2019/0042988, Brown hereinafter)
As to claim 1, Singh teaches a generative artificial intelligence (AI) system (e.g., “100”, FIG. 1, para [0027] , “ a generative AI platform architecture 100”. Also, see FIG. 11,” Generative AI Model Layer 104”, para 108, “FIG. 11 is a block diagram of architecture 100, shown in FIG. 1”) , comprising:
A network communication interface coupled to receive from a remote system via a network an API call comprising a request (e.g. , see FIG. 11, para 108, “cloud computing delivers the services over a wide area network, such as the internet”, “servers at a remote location”, “access for the user” and “ API 106, prompt response “, “user 594 uses a user device 596 to access those systems” in para 111 ) .
Thus, It is noted that: the “network, such as the internet” coupled with “servers at a remote location”, “access for the user” and “API 106, prompt response” , therefore a network communication interface coupled to receive from a remote system via a network an API call comprising a request); and
a processor coupled to the network communication interface (e.g., “processor(s), FIG. 1, para [0030] , “one or more processors”) and configured to:
execute a server-side prompt program comprising or otherwise associated with one or both of the API call and the request , including by sending two or more prompts to a generative AI service (e.g., e.g., see FIG. 5A/5B, para [0065] the request and makes calls to the target generative AI model, “, “ multiple calls (e.g., chained prompts) can be made to service the generative AI request, and those calls may be executed as chained prompts, or other calls. Making multiple calls to execute the generative AI request is indicated by block 374 in the flow diagram of FIG. 5
para [0030] In the example shown in FIG. 1, generative AI model API 106 includes one or more processors or servers 123, data store 125, interface generator 124, authentication system 126, generative AI request priority system 128, generative AI request processing system 130, prompt/response data collection processor 132,).
However, Singh does not teach receive a final result obtained by sending the two or more prompts to the generative Al service; and return the final result to the remote system in response to the API call; wherein the server-side prompt program includes code to receive a first generative Al response in response to a first prompt included in the two or more prompts; use data comprising the first generative Al response to select from a plurality of candidate next prompts a selected prompt to be sent and use data comprising the first generative Al response to generate the selected prompt , to send to the generative Al service, including by incorporating into the selected prompt at least a selected portion of data comprising the first generative Al response; and
wherein the selected prompt to the generative Al service prompts the generative Al service to construct a query to a third party service and the processor is further configured to receive the query to the third party service, in response to the sending the selected prompt, send the query to the third party service, receive a response from the third party service, and use the response from the third party service to further execute the server-side program.
Brown teaches receive a final result obtained by sending the two or more prompts to the generative Al service (e.g., para [0079] –[0080]“ the AI agent system 10 may send/receive query results to/from the enterprise data services 32 of the enterprise system 14.”, “The AI agent system 10 may use structured data as one or more hints, prompts”) ; and return the final result to the remote system in response to the API call (e.g., para [0077] “The AI agent system 10 may use this interaction to obtain answers to queries, obtain information on business projects and then notify client systems 12 on getting things done for the business projects, build the world model 16 and add to the world model 16, etc.”) ; wherein a server-side prompt program includes code to receive a first generative Al response in response to a first prompt included in the two or more prompts use data comprising the first generative Al response to generate the selected prompt to send to the generative Al service ( e.g., para [0236] In embodiments producing an answer of a specific type to a query about, for example a product or service of an enterprise may include prompting the user asking the question to clarify the question.” , “process may, based on the model graph portion and the clarifying response, generatively producing at least a portion of an answer to the question regarding the specific type of information of the enterprise from information known to the world model 16 and retrieving a portion of the answer from one or more information sources in an enterprise information system by posing queries to the enterprise information system, such as based on the intent of the question”.
Thus, it is noted that” the “posing queries” include two or more prompts, the “service of an enterprise” include a server-side prompt program, the “model graph portion” include the code) , including by incorporating into the selected prompt at least a selected portion of data comprising the first generative Al response (e.g., para 236, “ process may, based on the model graph portion and the clarifying response, generatively producing at least a portion of an answer to the question regarding the specific type of information of the enterprise from information known to the world model 16 and retrieving a portion of the answer from one or more information sources in an enterprise information system by posing queries to the enterprise information system, such as based on the intent of the question.”) ; and wherein the selected prompt to the generative Al service prompts the generative Al service to construct a query to a third party service and the processor is further configured to receive the query to the third party service ( e.g., see FIG. 7, para [0085] FIG. 7 illustrates the initiation of system-to-system communication between the AI agent system 10 and third-party application program interfaces (APIs) 100 (e.g., goip services, weather services, push notification services, etc.), third party applications 102 (e.g., any system external that wants to query/command an API of the agent, such as a chatbot, website, custom interface, etc.), “the third-party applications 102 (e.g., third party bots) may initiate (i.e., application initiates) the agent API 104”.
Thus, it is to be noted that : the “wants to query/command an API” include a query to a third party service ) in response to the sending the selected prompt, send the query to the third party service, receive a response from the third party service, and use the response from the third party service to further execute the server-side program (e.g., see FIG. 9Apara 90, “ The linked data catalog may represent knowledge topically and how to get the knowledge e.g., such as the AI agent system 10 knowing that pricing data is located at a specific location in enterprise system 14 and where the linked data catalog may contain information used to instruct the integration engine 30 of the AI agent system 10 what to retrieve and where to retrieve it. This information may comprise data input sets to be passed to the integration engine 30 to complete a query with respect to the linked data catalog. Linked data 124 such as linked data catalogs help with enterprise integration. This allows for linking of data between dispersed sources.”
[0098] There are several results of distributed data query and re-unification. For example, taking one or more queries (e.g., looking for piece of information), breaking a query into subqueries (e.g., based on a linked-data catalog and information stored in the integration engine itself), determining which query sources need to be hit, processing queries in parallel, recombining results, and producing semantically-consistent responses back. The AI agent system 10 may be able to take response data and produce natural language responses and/or instructions to the client system 12 on actions to take/content to show.)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Singh with those of Brown because both references are directed to related systems addressing similar technical problems within the same field and seek to improve system performance, reliability, and efficiency.
Singh et al. disclose A generative artificial intelligence (AI) system, comprising: a network communication interface coupled to receive from a remote system via a network an API call comprising a request; and a processor coupled to the network communication interface and configured to: execute a server-side prompt program comprising or otherwise associated with one or both of the API call and the request, including by sending two or more prompts to a generative Al service while Brown et al. teaches receive a final result obtained by sending the two or more prompts to the generative Al service; and return the final result to the remote system in response to the API call; wherein the server-side prompt program includes code to receive a first generative Al response in response to a first prompt included in the two or more prompts; use data comprising the first generative Al response to select from a plurality of candidate next prompts a selected prompt to be sent; and use data comprising the first generative Al response to generate the selected prompt to send to the generative Al service, including by incorporating into the selected prompt at least a selected portion of data comprising the first generative Al response; and wherein the selected prompt to the generative Al service prompts the generative Al service to construct a query to a third party service and the processor is further configured to receive the query to the third party service, in response to the sending the selected prompt, send the query to the third party service, receive a response from the third party service, and use the response from the third party service to further execute the server-side program.
Incorporating the teachings of Brown et al. into the system of Singh et al. would have been a predictable and logical modification, yielding improved operational robustness and efficiency without requiring undue experimentation.
Such a combination would merely involve the substitution or integration of known elements performing their established functions, as taught by Brown et al., into the system of Singh et al., consistent with design incentives and market demands for improved performance and scalability. Moreover, Brown et al. explicitly recognize benefits to “ensuring that users are delivered information at a level of detail that is consistent with a level of know-how determined from the context of a query may result in more rapid learning by the user and faster resolution of a problem for which the user has posed the query.” (see Brown, para 106) . —that would naturally be desirable in the system of Singh et al.
Accordingly, to one of ordinary skill in the art would have had a reasonable expectation of success in combining Singh et al. with Brown et al., and the combination represents no more than the predictable use of prior art elements according to their known functions.
As to claim 2, Singh teaches wherein the API call is received at an API endpoint associated with the communication interface (e.g., e.g., see FIG. 11, para 108) [0108], “Cloud computing infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user”, “servers at a remote location” . Thus, it is noted that : the “Cloud computing infrastructures” coupled with “servers at a remote location” include the API endpoint).
As to claim 3, Singh teaches wherein the remote system comprises an application server and the API call is generated and sent by an application running on the application server (e.g., see FIG. 11, para [0108] FIG. 11 is a block diagram of architecture 100, shown in FIG. 1, except that its elements are disposed in a cloud computing architecture 590. Cloud computing provides computation, software”, “Software or components of architecture 100 as well as the corresponding data, can be stored on servers at a remote location”).
As to claim 4, Singh teaches wherein the prompt program is written in a scripting or other interpreted language (e.g., para [0047] data loading scripts 283”, [0041] “ data extraction scripts”. Also, see FIG. 10C and “ scripts that are provided along with the prompt “ in para 42).
As to claim 5, Singh teaches wherein the prompt program is executed in one or more of a runtime , a virtual machine, and a container (e.g., see FIG. 11, para 108, wherein “The computing resources in a cloud computing environment can be consolidated at a remote data center location or they can be dispersed. Cloud computing infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user’ and “The generative AI models in layer 104 can be run in AI model execution layer 107 which can include a production pool 108”, “clusters (running the requested AI model type) are to determine which AI models or clusters, of the desired type, may have capacity to serve the generative AI request. It will be noted that cluster capacity scaling system 136 can scale the capacity, as needed, as indicated by block 470, and evaluation of the capacity can be done in other ways as well, as indicated by block 472” in para 27 and 78.
Thus, it is noted that: “a production pool 108”, “clusters (running the requested AI model type) “ include one or more of a runtime , a virtual machine, and a container ) .
As to claim 8, Singh teaches wherein the prompt program includes code to use data comprising a first response to a first prompt to send a query to the remote system (e.g., see FIG. 5A para [0067] “. Response processor 189 returns the response to the calling client/user/tenant (e.g., calling client application 102 or development platform 114) “ and “In processing the requests, system 130 identifies the type of generative AI model being requested, and processes the prompt to route the request to a target generative AI model 142-144. Generative AI request processing system 130 also returns the responses from the target generative AI model back to the requesting client apps 102 (e.g., through the interface generated by interface generator 124 or in other ways)” in para 32.).
As to claim 9, Singh and Harris do not explicitly teach wherein the prompt program includes code to use data comprising a first response to a first prompt to send a query to a third-party system. However, Brown teaches wherein the prompt program includes code to use data comprising a first response to a first prompt to send a query to a third-party system ( see FIGs. 2 and 7, para [0085] FIG. 7 illustrates the initiation of system-to-system communication between the AI agent system 10 and third-party application program interfaces (APIs) 100 (e.g., goip services, weather services, push notification services, etc.), third party applications 102 (e.g., any system external that wants to query/command an API of the agent, such as a chatbot, website, custom interface, etc.), and the enterprise system 14. ).
Thus, It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teachings of Singh with the teachings of Brown “ensuring that users are delivered information at a level of detail that is consistent with a level of know-how determined from the context of a query may result in more rapid learning by the user and faster resolution of a problem for which the user has posed the query.” (see Brown, para 106).
As to claim 12, Singh teaches wherein the generative AI service comprises a large language model (e.g., para [0019] As discussed above, generative artificial intelligence models (generative AI models) often take the form of large language models.).
As to claim13, Singh teaches wherein the prompt program comprises a first prompt program included in a plurality of prompt programs executable by the processor (e.g., para {0059] , “, API interaction system 296 can interact with API 106 to submit the generative AI requests “, “ the user or developer can submit prompts and receive responses on the actual types of generative AI models “, “ GPUs to execute the models”).
As to claim 14, Singh teaches wherein code comprising the prompt program is included in or with the API call (e.g., para 0065] “multiple calls (e.g., chained prompts) can be made to service the generative AI request” , “ calls to execute the generative AI request “)
As to claim 15, Singh teaches wherein an identifier associated with the prompt program the is included in or with the API call and the processor is further configured to map the identifier to the prompt program (e.g., para 41, “ identify chained prompts or calls that are to be made to service the request”).
As to claim 16, Singh teaches wherein the request includes one or more arguments operated on or otherwise used by the prompt program (e.g., para [0041] “words in the request, data extraction scripts, model parameters, etc.” , [0042] , “ identifier 198 identifies the type of generative AI model that is being called to service the request and model parameter identifier 200 identifies the operational model parameters that are provided with the generative AI request. “ ).
As to claim 17, see rejection of claim 1 above.
As to claim 20, see rejection of claim 1 above. Singh teaches further a computer program product embodied in a non-transitory computer readable medium and comprising computer instructions ( see FIG. 12).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schnitt, Matt et al. ( US 20220292465) discloses to enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing payment-related services that may utilize checkout parameters, a link generation service, a payment processing service, and a post-payment service. The link generation service may serve a checkout page using a set of the checkout parameters. The payment processing service may receive a transaction notification indicating whether a payment was successful or unsuccessful. The post-payment service may initiate a post-transaction workflow corresponding to an outcome indicated by the transaction notification.
Urdiales (US 20210357378 )discloses to collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDOU K SEYE whose telephone number is (571)270-1062. The examiner can normally be reached M-F 9-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at 5712724215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDOU K SEYE/Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
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