DETAILED ACTION Receipt of the preliminary Amendment, filed April 11, 2023 is acknowledged. Claims 1-20 were cancelled. Claims 21-40 were newly added. Claims 21-40 are pending in this office action. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Interpretation With regard to claim 21, the claim recites “calling an interface exposed by a generative artificial intelligence (Al) model application programming interface (API) to receive an Al prompt and a response generated by a generative Al model based on the Al prompt;” This claim limitation appears to recite an intended use of the claimed calling functionality. Method claims are limited by the functions that are claimed to be performed. In this limitation, the claimed function is the calling of the interface. The claimed method does not perform the generation of the response or the receipt of the prompt. These are the intended results of the calling that are performed by the generative AI model application programming interface (API). It is suggested that the claims be amended to directly claim the functionality to which patent coverage is desired. To be clear, it is suggested that the claims be amended to recite the method receiving an AI prompt, and generating a response should the applicant desire such functions to be within the scope of the claims. With regard to claims 21 and 24, claim 21 recites “storing the prompt record for access by an application through the generative AI model API.” Claim 24 recites similar language and has been interpreted in a similar manner. This claim limitation appears to recite an intended use of the claimed storing. The functionality recited is the storing of the prompt record. What the stored prompt record is use for is an intended use of the act of storing said data. The claim does not actually require accessing the stored prompt record. It is suggested that the claim be amended to positively recite the functionality to which patent coverage is desired instead of reciting the intentions of use of claimed functionality. With regard to claim 28, the claim recites “processing the A I prompt to identify whether the A I prompt is a surreptitious prompt”. This claim limitation appears to recite an intended use of the processing. The claim is written in a results - oriented manner without actually reciting the functionality that achieves the result. The functionality recited is the “processing the AI prompt” . There is no recitation of what this processing entails nor what the functionality actually is, merely ‘processing’. The intended use, e.g. the results, of the processing is to identify whether the AI prompt is a surreptitious prompt. The claimed method does not actually require identifying whether the AI prompt is surreptitious, the claim merely requires that the AI prompt be processed to achieve the intended result. It is suggested that the claims be amended to positively recite the functionality performed by the system, e.g. recite that the method includes identifying whether the AI prompt is a surreptitious prompt. The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: Claim 35: “ an application programming interface (API) interaction system configured to call an interface exposed by a generative artificial intelligence (AI) model application programming interface (API) to receive an AI prompt and a response generated by a generative Al model based on the AI prompt ”. Claim 35: “ a prompt/response record processor configured to automatically generate a prompt record based on the AI prompt received from the generative AI model API, the prompt record including prompt content data indicative of content of the generative AI prompt and prompt evaluation data indicative of a performance of the generative AI prompt, the prompt/response record processor being configured to automatically provide the prompt record for storage in a data store in a user data storage system ” (Please note that when read within context the term ‘processor’ has been understood as being part of the generic placeholder term, and not referring to circuitry) Claim s 36 and 38 further defines the “prompt/response record processor” Claim 37: “ a surreptitious prompt identification system configured to process the AI prompt to identify whether the AI prompt is a surreptitious prompt and, if so, tag the prompt record to identify the AI prompt as a surreptitious AI prompt. ” Claim 38: “ a development system configured to interact with an AI development system to receive a development system AI prompt and a response generated by a generative AI model based on the development system AI prompt ” Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Please note that should these limitation be interpreted as not invoking 112f, then the question of the claim being directed to software or signals per sae would be raised. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Objections Claim s 21-38 are objected to because of the following informalities. Appropriate correction is required. With regard to claim 25, the claim recites “receiving a call from the generative AL model API”. This claim depends from claim 21 which recites “ calling an interface exposed by a generative artificial intelligence (Al) model application programming interface (API)”. This claim limitation lacks antecedent basis. One of ordinary skill in the art would recognize the act of ‘calling’ as one party issuing the call, while a second party receives said call. Thus the recitation of receiving a call lacks antecedent basis as it is unclear if applicant is reciting a second calling step or attempting to refer to the previously recited calling step. For examination purposes this claim limitation has been construed to mean -- receiving a retrieval call from the generative AL model API -- and the reference to “the call” further recited in claim 25 has been understood as referring to --the retrieval call--. With regard to claims 28 and 29, claim 28 recites “a surreptitious prompt” multiple times, as well as the claim label “a surreptitious AI prompt”. Within the context of the invention, all the prompts are AI prompts, as such one of ordinary skill in the art may reasonably read the surreptitious prompt as being the same element as the surreptitious AI prompt. The use of the same label for two distinct claim elements lacks antecedent basis. The use of two distinct labels for the same claim element lacks antecedent basis. It is unclear if applicant is reciting a new claim element or referring to a previously recited claim element. For examination purposes all recitations of surreptitious prompt and surreptitious AI prompt have been understood as referring to the same claim element. It is suggested that the claim be amended to use consistent claim language throughout the claim. With regard to claim s 30 and 31 , claim 30 recites “generating the prompt record with, as the evaluation data, a set of evaluation metrics and evaluation metric values”. The recitation of “evaluation data” lacks antecedent basis. The parent claim has recited “prompt evaluation data” while the instant claim appears to use the label “evaluation data”. Each unique claim label is expected to refer to a unique claim element. It is unclear if applicant is attempting to recite a new claim element or refer to the previously recited element. It is suggested that the claim labels be used consistently throughout the claims. The recitation of “generating the prompt record” lacks antecedent basis. The claim limitation is written in a manner that suggests the claim is reciting a new generating step. The distinction between this generating the prompt record step and the generating reciting in claim 21 is unclear. It is suggested that the claim be amended to make it clear when applicant is further defining a step, or reciting a new step. Claim 31 further complicated this by reciting “a set of evaluation metrics and evaluation metric values” , which appears to be a duplicate recitation of a limitation recited in claim 30. It is unclear if applicant is attempting to refer to the previously recited claim limitations or attempting to recite new claim elements. It is suggested that the claim be amended to use the term ‘the’ to ensure that it is clear that the applicant is referring to the previously recited claim elements. For examination purposes this claim limitation has been construed to mean –wherein the prompt evaluation data comprises a set of evaluation metrics and evaluation metric values --. With regard to claim 32, the claim recites “wherein processing the AI prompt to generate a prompt record comprises: generating the prompt record with an indication of context data and augmented data used by the generative AI model in generating the response.” With regard to the “prompt record”, it is unclear if applicant is attempting to define a new claim element or attempting to refer to the previously recited claim element. With regard to the “indication” it is unclear if applicant is attempting to recite a new claim element or if applicant is attempting to refer to the “prompt content data” which is “ indicative of content of the generative AI prompt”. It is noted that ‘indication’ is a noun, referring to an element, ‘indicative of’ an adjective modifying a noun. One of ordinary skill in the art may reasonably read the recited ‘indication’ as referring to the content data that was recited as being ‘indicative of’ the prompt content data. Yet the use of a distinct claim label renders is reasonable to interpret the indication as being a distinct from the prompt content data. For examination purposes this claim limitation has been construed to mean – wherein the content data indicates context data and augmented data— With regard to claim 33, the claim recites “wherein generating the prompt record with an indication of context data and augmented data comprises: identifying data extraction scripts corresponding to the prompt; and generating the prompt record with an indication of the data extraction scripts.” This claim limitation lacks antecedent basis. The claim h as previously recited “an indication of context data and augmented data” in parent claim 32 as well as reciting “prompt content data indicative of …” and “prompt evaluation data indicative of …”. In parent claim 31. It is unclear if applicant is attempting to recite a new claim element or attempting to refer one of the previously recited claim elements . Furthermore it is noted that the claim does not actually recite what any of the claim elements are. The claim may reasonably be interpreted to mean that the prompt content data indicates the context data and augmented data, which itself indicates the data extraction scripts. This places no restriction regarding what the claimed prompt content data actually is . It is unclear what limitation is reasonably imposed on a first data element when it is indicative of a second data element which itself is indicative of a third data element. Stating what something indicates does not place any restriction on what that something is, or the functionality that is performs. It is suggested that the claims be amended to recite what the structural and functional elements are , and to clearly claim their relationship to each other. When read in light of the instant specification, the “extraction scripts” are parsed from the request (Paragraph [0041]) … and can be run in order to extract the context data and other augment data that is used by the AI model (Paragraph [0042], [0076]). For examination purposes this claim limitation has been construed in light of these sections of the specification. With regard to claim 34, recites “ a prompt record … generating the prompt record with an indication of…”. This claim limitation lacks antecedent basis. With regard to the “prompt record”, it is unclear if applicant is attempting to define a new claim element or attempting to refer to the previously recited claim element. With regard to the “indication” it is unclear if applicant is attempting to recite a new claim element or if applicant is attempting to refer to the “prompt content data” which is “ indicative of content of the generative AI prompt”. For examination purposes this claim limitation has been construed to mean –the prompt record… comprises the identified type and the identified model parameters. — With regard to claim 35, the claim recites “ a prompt/response record processor ”. It is unclear if the claim is intended to recite a prompt record processor or a response record processor. The use of the character ‘/’ renders the meaning of the claim unclear. For examination purposes this claim limitation has been construed to mean -- a prompt and response record processor --. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 35-38 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. With regard to claims 35-38, the limitation s “an application programming interface (API) interaction system, “a prompt/response record processor, “a surreptitious prompt identification system”, “a development system” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph as detailed above . However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. No structural element is recited as performing the recited functionality. There is not any clear linking between the terminology used within the claim and any structural element or algorithm within the specification. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims 21-27, 30-36, 38-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Safronov904 [2020/0192904] . With regard to claim 21 Safronov904 teaches A computer implemented method, comprising: calling an interface as the communication link 114 ( Safronov904 , ¶80, “The system 100 comprises a search engine server 120, a tracking server 130 and a training server 140 coupled to the communication network 112 via their respective communication link 114”) exposed by a generative artificial intelligence (Al) model as machine learning model ( Safronov904 , ¶99 “executes one or more machine learning algorithms (MLAs) 126”) application programming interface (API) ( Safronov904 , ¶56 “appropriate hardware and is capable of receiving requests (e.g. from electronic devices) over a network, and carrying out those requests, or causing those requests to be carried out.”) to receive an Al prompt as the current query ( Safronov904 , ¶96 “when a given query ( such as a current query of a user of the first client device 104, for example) is received by the search engine server 120”) and a response as the ranked documents ( Safronov904 , ¶99 “the search engine server 120 executes one or more machine learning algorithms (MLAs) 126 for ranking documents in response to the given query”) generated by a generative Al model as the Machine learning algorithms (MLA) 126 which performs the ranking ( Safronov904 ¶99), based on the Al prompt as in response to the given query ( Safronov904 , ¶99 “the search engine server 120 executes one or more machine learning algorithms (MLAs) 126 for ranking documents in response to the given query”) ; generating, with a prompt record processor as the tracking server 130 ( Safronov904 , ¶107 “the tracking server 130 is configured to track user interactions with search results provided by the search engine server 120 in response to user requests”) , a prompt record as query log 136 ( Safronov904 , ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) based on the Al prompt as the respective quarriers ( Id ) received from the generative AI model API as the queries submitted via the respective client devices ( Safronov904 , ¶127 “acquires a set of past queries 202 from the query log 136, where each query of the set of past queries 202 has been previously submitted on the search engine server 120 by one or more users via respective associated client devices”) through the devices capable for transmitting through the network ( Safronov904 , ¶56) , the prompt record including prompt content data indicative ( Safronov904 , ¶61 see definition for ‘indication’) of content as the respective terms ( Safronov904 , ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) of the generative Al prompt as the respective query ( Id ) and prompt evaluation data indicative of a performance of the generative Al prompt as the user’s click indications ( Id ) ; and storing the prompt record as storing the tracked queries, user interactions and associated search results in the search log database 122 ( Safronov904 , ¶119 “In some embodiments, the tracking server 130 may send tracked queries, search result and user interactions to the search engine server 120, which may store the tracked queries, user interactions and associated search results in the search log database 122”) for access by an application as search engine, such general web searches ( Safronov904 , ¶122 “train one or more MLAs associated with the search engine provider for optimizing general web searches, vertical web searches, providing recommendations, predicting outcomes, and other applications. The training and optimization of the MLAs may be executed at predetermined periods of time, or when deemed necessary by the search engine provider.” ) through the generative AI model API ( Safronov904 , ¶56 “appropriate hardware and is capable of receiving requests (e.g. from electronic devices) over a network, and carrying out those requests, or causing those requests to be carried out.”) . With regard to claim 22 Safronov904 further teaches generating a response record based on the response received from the generative Al model API, the response record including response content data indicative of content of the response as user interaction log storing the reference document ID ( Safronov904 , ¶118 “As a non-limiting example, the user interaction log 138 may contain a reference to a document, which may be identified by an ID number or an URL, a list of queries, where each query of the list of queries has been used to access the document, and respective user interactions associated with the document for the respective query of the list of queries (if the document was interacted with)”) ; and storing the response record with a record indicator indicating that the response record is related to the prompt record as the list of queries, where each query of the list of queries has been used to access the document ( Id ) . With regard to claim 2 3 Safronov904 further teaches wherein processing the Al prompt to generate the response record comprises: generating the response record to include user interaction indicators indicative of user interactions with the response as the respective user interactions (( Safronov904 , ¶118 “As a non-limiting example, the user interaction log 138 may contain a reference to a document, which may be identified by an ID number or an URL, a list of queries, where each query of the list of queries has been used to access the document, and respective user interactions associated with the document for the respective query of the list of queries (if the document was interacted with)”) . With regard to claim 2 4 Safronov904 further teaches obtaining, from an AI development system as the training server ( Safronov904 , ¶127 “The training server 140 acquires a set of past queries 202 from the query log 136”) , a development system AI prompt as one of the past queries 202, e.g. first past query 204 ( Safronov904 , ¶127 “The training server 140 acquires a set of past queries 202 from the query log 136”; ¶130) and a response as the past document 140 ( Safronov904 , ¶130 “The training server 140 acquires, for the first past query 204, a set of past documents 210, the set of past documents 210 having been presented as search results in a search engine results page (SERP) to one or more of the plurality of client devices 102 in response to the first past query 204 having been submitted on the search engine server 120”) generated by a generative AI model as the past documents determined by the MLA126 ( Safronov904 , ¶131 “The set of past documents 210 generally includes a predetermined number of documents, such as the top 100 most relevant documents that have been presented in a SERP in response to the first past query 204, as determined by the MLA 126 of the search engine server 120.”) based on the development system Al prompt as the first past query 204 ( Id ) ; generating a development prompt record as the training database 142 ( Safronov904 , ¶124) based on the development system AI prompt received from the Al development system as selecting the past queries ( Safronov904 , ¶128 “How the training server 140 selects queries to be part of the set of past queries 202 is not limited”) , the development prompt record including prompt content data indicative of content of the development system AI prompt ( Safronov904 , ¶117 “More specifically, the query log 136 may include a list of queries with their respective terms”) and prompt evaluation data indicative of a performance of the development system AI prompt ( Safronov904 , ¶146 “As a non-limiting example, the first plurality of features 220 may include indications of user interactions or user engagement metrics tracked and compiled by the tracking server 130 such as one or more of:” ¶147-¶150) ; and storing the development prompt record for access by the AI development system ( Safronov904 , ¶168 “In other embodiments, the value 284 of the meta-feature 282 for each respective document 212 of the set of past documents 210 may be stored together with the first plurality of features 220 in the index 124 and/or the query log 136 and/or the user interaction log 138.”) . With regard to claim 2 5 Safronov904 further teaches receiving a call (Please note this claim limitation has been interpreted as being --a retrieval call--) from the generative AI model API as quiring the training database 142 (¶173, “The search engine server 120 may query the training database 142 to verify if the current query 304 is one of the set of past queries 202, in which case the search engine server 120 also retrieves respective values 334 of a meta-feature 282 computed for the respective set of past documents (not depicted) associated with the respective past query (not depicted) similar to the current query 304, which will be used for ranking the set of current documents 310, the set of current documents 310 including documents in the set of past documents”) ; and returning a prompt record to the generative AI model API based on the call as retrieving the respective values ( Id ) . With regard to claim 2 6 Safronov904 further teaches automatically populating a prompt library in a user data storage system with the prompt record as the search log database 122 ( Safronov904 , ¶119 “which may store the tracked queries, user interactions and associated search results in the search log database 122.”) . With regard to claim 2 7 Safronov904 further teaches wherein generating the prompt record comprises: identifying tokens in the AI prompt as search words (¶166 “More specifically, the query log 136 maintains terms of search queries (i.e. the associated search words) and the associated search results.”) ; and populating the prompt record with an indication of the tokens in the AI prompt as the query log 136 maintains the terms of the search query ( Id ) . With regard to claim 30 Safronov904 further teaches wherein processing the Al prompt to generate the prompt record comprises: generating the prompt record with, as the evaluation data ( Safronov904 , ¶117; Please note this claim limitation has been construed as referring to the --prompt evaluation data--, please see the 112b above) , a set of evaluation metrics and evaluation metric values indicative of the performance of the generative Al prompt ( Safronov904 , ¶26 “ applying, by the server, a user engagement metric on the respective sets of past documents based on the respective past user interactions to obtain the threshold, and the determining the usefulness of the meta - feature ”) . With regard to claim 31 Safronov904 further teaches wherein generating the prompt record with, as the evaluation data, a set of evaluation metrics and evaluation metric values comprises: receiving the set of evaluation metrics and metric values from a generative Al evaluation model as the MLA which includes the tracking server (¶99, ¶107, ¶108) . With regard to claim 32 Safronov904 further teaches wherein processing the AI prompt to generate a prompt record comprises: generating the prompt record ( Safronov904 , ¶119 “In some embodiments, the tracking server 130 may send tracked queries, search result and user interactions to the search engine server 120, which may store the tracked queries, user interactions and associated search results in the search log database 122”) with an indication of context data and augmented data used by the generative AI model in generating the response as click-through rate, number of clicks on an element, ect ( Safronov904 , ¶108 “ the tracking server 130 may compute a click-through rate (CTR), at predetermined intervals of time or upon receiving an indication, based on a number of clicks on an element and number of times the element was shown (impressions) in a SERP. ”; Please see the 112b above, this claim limitation has been construed to mean --wherein the content data indicates context data and augment data--) . With regard to claim 33 Safronov904 further teaches wherein generating the prompt record with an indication of context data and augmented data comprises: identifying data extraction scripts corresponding to the prompt as document retrieval operations, e.g. search query ( Safronov904 , ¶172 “ he search engine server 120 retrieves, from the index 124, based on terms of the current query 304 ”) ; and generating the prompt record with an indication of the data extraction scripts as storing the tracked queries, user interactions and associated search results in the search log database 122 ( Safronov904 , ¶119 “In some embodiments, the tracking server 130 may send tracked queries, search result and user interactions to the search engine server 120, which may store the tracked queries, user interactions and associated search results in the search log database 122” ; Please note this claim limitation has been read in light of Paragraphs [0041], [0042], and [0076] as stated above) . With regard to claim 34 Safronov904 further teaches wherein processing the AI prompt to generate a prompt record (Please see the 112b above regarding claim interpretation) comprises: identifying a type as the type of the evaluation metric ( Safronov904 , ¶188 “A type of the evaluation metric used to evaluate the usefulness of the meta-feature 282, and a type of the user interactions 378 and user interactions 388 being evaluated”) of the generative AI model as the evaluation metric used to train the training server, which is part of the MLA (¶188, ¶99) that generated the response as the set of past documents ( Safronov904 , ¶160 “the value 262 of the parameter 260 associated with the set of past documents 210.”) ; identifying model parameters ( Safronov904 , ¶160 “the value 262 of the parameter 260 associated with the set of past documents 210.”) used with the identified type as the type of the evaluation metric ( Safronov904 , ¶188 “A type of the evaluation metric used to evaluate the usefulness of the meta-feature 282, and a type of the user interactions 378 and user interactions 388 being evaluated”) of generative AI model as the evaluation metric used to train the training server, which is part of the MLA (¶188, ¶99) ; and generating the prompt record as query log 136 ( Safronov904 , ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) with an indication of the identified type as the type of the evaluation metric ( Safronov904 , ¶188 “ A type of the evaluation metric used to evaluate the usefulness of the meta-feature 282, and a type of the user interactions 378 and user interactions 388 being evaluated ”) of generative AI model as the evaluation metric used to train the training server, which is part of the MLA (¶188, ¶99) and the identified model parameters ( Safronov904 , ¶160 “ the value 262 of the parameter 260 associated with the set of past documents 210. ”) used by the generative AI model in generating the response as the set of documents 210 ( Id ) . With regard to claim 35 Safronov904 taches A computer system, comprising: an application programming interface (API) interaction system ( Safronov904 , ¶56 “appropriate hardware and is capable of receiving requests (e.g. from electronic devices) over a network, and carrying out those requests, or causing those requests to be carried out.”) configured to call an interface as the communication link 114 ( Safronov904 , ¶80, “The system 100 comprises a search engine server 120, a tracking server 130 and a training server 140 coupled to the communication network 112 via their respective communication link 114”) exposed by a generative artificial intelligence (AI) model as machine learning model ( Safronov904 , ¶99 “executes one or more machine learning algorithms (MLAs) 126”) application programming interface (API) ( Safronov904 , ¶56 “appropriate hardware and is capable of receiving requests (e.g. from electronic devices) over a network, and carrying out those requests, or causing those requests to be carried out.”) to receive an AI prompt as the current query ( Safronov904 , ¶96 “when a given query ( such as a current query of a user of the first client device 104, for example) is received by the search engine server 120”) and a response as the ranked documents ( Safronov904 , ¶99 “the search engine server 120 executes one or more machine learning algorithms (MLAs) 126 for ranking documents in response to the given query”) generated by a generative Al model as the Machine learning algorithms (MLA) 126 which performs the ranking ( Safronov904 ¶99) based on the AI prompt as in response to the given query ( Safronov904 , ¶99 “the search engine server 120 executes one or more machine learning algorithms (MLAs) 126 for ranking documents in response to the given query”) ; and a prompt/response record processor as the tracking server 130 ( Safronov904 , ¶107 “the tracking server 130 is configured to track user interactions with search results provided by the search engine server 120 in response to user requests”) configured to automatically generate a prompt record as query log 136 ( Safronov904 , ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) based on the Al prompt as the respective quarriers ( Id ) received from the generative AI model API as the queries submitted via the respective client devices ( Safronov904 , ¶127 “acquires a set of past queries 202 from the query log 136, where each query of the set of past queries 202 has been previously submitted on the search engine server 120 by one or more users via respective associated client devices”) through the devices capable for transmitting through the network ( Safronov904 , ¶56) , the prompt record including prompt content data indicative ( Safronov904 , ¶61 see definition for ‘indication’) of content as the respective terms ( Safronov904 , ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) of the generative Al prompt as the respective query ( Id ) and prompt evaluation data indicative of a performance of the generative Al prompt as the user’s click indications ( Id ) , the prompt/response record processor being configured to automatically provide the prompt record for storage as storing the tracked queries, user interactions and associated search results in the search log database 122 ( Safronov904 , ¶119 “In some embodiments, the tracking server 130 may send tracked queries, search result and user interactions to the search engine server 120, which may store the tracked queries, user interactions and associated search results in the search log database 122”) in a data store in a user data storage system as the database 112 ( Id ) . With regard to claim 36 Safronov904 further teaches wherein the prompt/response record processor is configured to generate a response record based on the response received from the generative AI model API, the response record including response content data indicative of content of the response as user interaction log storing the reference document ID (Safronov904, ¶118 “As a non-limiting example, the user interaction log 138 may contain a reference to a document, which may be identified by an ID number or an URL, a list of queries, where each query of the list of queries has been used to access the document, and respective user interactions associated with the document for the respective query of the list of queries (if the document was interacted with)”) and user interaction indicators indicative of user interactions with the response as the respective user interactions ((Safronov904, ¶118 “As a non-limiting example, the user interaction log 138 may contain a reference to a document, which may be identified by an ID number or an URL, a list of queries, where each query of the list of queries has been used to access the document, and respective user interactions associated with the document for the respective query of the list of queries (if the document was interacted with)”) . With regard to claim 38 Safronov904 further teaches a development system configured to interact with an AI development system as the training server (Safronov904, ¶127 “The training server 140 acquires a set of past queries 202 from the query log 136”) to receive a development system AI prompt as one of the past queries 202, e.g. first past query 204 (Safronov904, ¶127 “The training server 140 acquires a set of past queries 202 from the query log 136”; ¶130) and a response as the past document 140 (Safronov904, ¶130 “The training server 140 acquires, for the first past query 204, a set of past documents 210, the set of past documents 210 having been presented as search results in a search engine results page (SERP) to one or more of the plurality of client devices 102 in response to the first past query 204 having been submitted on the search engine server 120”) generated by a generative AI model as the past documents determined by the MLA126 (Safronov904, ¶131 “The set of past documents 210 generally includes a predetermined number of documents, such as the top 100 most relevant documents that have been presented in a SERP in response to the first past query 204, as determined by the MLA 126 of the search engine server 120.”) based on the development system Al prompt as the first past query 204 ( Id ) , the prompt/response record processor being configured to generate a development prompt record as the training database 142 (Safronov904, ¶124) based on the development system AI prompt received from the AI development system, as selecting the past queries (Safronov904, ¶128 “How the training server 140 selects queries to be part of the set of past queries 202 is not limited”) , the development prompt record including prompt content data indicative of content of the development system AI prompt (Safronov904, ¶117 “More specifically, the query log 136 may include a list of queries with their respective terms”) and prompt evaluation data indicative of a performance of the development system AI prompt (Safronov904, ¶146 “As a non-limiting example, the first plurality of features 220 may include indications of user interactions or user engagement metrics tracked and compiled by the tracking server 130 such as one or more of:” ¶147-¶150) . With regard to claim 39 Safronov904 teaches A computing system, comprising: one or more processors as a processor (Safronov904, ¶37 “ the server comprising: a processor, a non-transitory computer-readable medium comprising instructions. ”) ; and a memory storing computer executable instructions which, when executed by the one or more processors ( Id ) , cause the one or more processors to perform steps, comprising: calling as the communication link 114 (Safronov904, ¶80, “The system 100 comprises a search engine server 120, a tracking server 130 and a training server 140 coupled to the communication network 112 via their respective communication link 114”) a generative artificial intelligence (AI) model as machine learning model (Safronov904, ¶99 “executes one or more machine learning algorithms (MLAs) 126”) accessing system (Safronov904, ¶56 “appropriate hardware and is capable of receiving requests (e.g. from electronic devices) over a network, and carrying out those requests, or causing those requests to be carried out.”) to receive an AI prompt as the current query (Safronov904, ¶96 “when a given query ( such as a current query of a user of the first client device 104, for example) is received by the search engine server 120”) and a response as the ranked documents (Safronov904, ¶99 “the search engine server 120 executes one or more machine learning algorithms (MLAs) 126 for ranking documents in response to the given query”) generated by a generative Al model as the Machine learning algorithms (MLA) 126 which performs the ranking (Safronov904 ¶99), based on the Al prompt as in response to the given query (Safronov904, ¶99 “the search engine server 120 executes one or more machine learning algorithms (MLAs) 126 for ranking documents in response to the given query”) ; generating, with a prompt record generation system, as the tracking server 130 (Safronov904, ¶107 “the tracking server 130 is configured to track user interactions with search results provided by the search engine server 120 in response to user requests”) , a prompt record as query log 136 (Safronov904, ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) based on the Al prompt as the respective quarriers ( Id ) received from the generative AI model accessing system as the queries submitted via the respective client devices (Safronov904, ¶127 “acquires a set of past queries 202 from the query log 136, where each query of the set of past queries 202 has been previously submitted on the search engine server 120 by one or more users via respective associated client devices”) through the devices capable for transmitting through the network (Safronov904, ¶56) , the prompt record including prompt content data indicative (Safronov904, ¶61 see definition for ‘indication’) of content as the respective terms (Safronov904, ¶117 “the query log 136 may include a list of queries with their respective terms, with information about documents that were listed by the search engine server 120 in response to a respective query, a timestamp, and may also contain a list of users identified by anonymous IDs ( or without an ID altogether) and the respective documents they have clicked on after submitting a query”) of the generative Al prompt as the respective query ( Id ) and prompt performance data indicative of a performance of the generative Al prompt as the user’s click indications ( Id ) ; and storing the prompt record as storing the tracked queries, user interactions and associated search results in the search log database 122 (Safronov904, ¶119 “In some embodiments, the tracking server 130 may send tracked queries, search result and user interactions to the search engine server 120, which may store the tracked queries, user interactions and associated search results in the search log database 122”) for access by an AI system (Safronov904, ¶56 “appropriate hardware and is capable of receiving requests (e.g. from electronic devices) over a network, and carrying out those requests, or causing those requests to be carried out.”) . With regard to claim 40 Safronov904 further teaches automatically populating a prompt memory in a generative AI development environment with the prompt record as the search log database 122 (Safronov904, ¶119 “which may store the tracked queries, user interactions and associated search results in the search log database 122.”) . Claim Rejections - 35