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 action is responsive to the Application filed on 07/10/2026
Claims 1 and 20 are pending in the case. Claims 1 and 20 are independent claims. Claims 1 and 20 have been currently amended.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/10/2026 has been entered.
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) 1-16, 19-24 and 26-30 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (Pub No.: 20240273291 A1), hereinafter referred to as Smith, in view of AYKUT et al. (Pub No.: 20240152544 A1), hereinafter referred to as AYKUT.
With respect to claim 1, Smith disclose:
A computer system, the computer system comprising: a network interface (In Fig. 2 and paragraph [0047], Smith discloses a generative collaborative network 220 with an interface 212.)
At least one processing device comprising one or more arithmetic logic units, registers, and buses, the at least one processing device operable to: detect a prompt entered into a user interface field from a user device provided to a generative model (In Fig. 2 and paragraph[0234], Smith discloses that a prompt containing detailed instructions is sent to a generative language mode, which generates an entire document package — including the document, summary, notification, and metadata.)
Detect a response to the prompt, output by the generative model (In Fig. 18 and paragraph [0301-0307], Smith discloses operation 1804 receives a first set of title prompts as input to the generative language model. Operation 1804 outputs a first set of document titles in response to the prompts. Operation 1806 then uses those generated titles to create document prompts, thereby necessarily receiving and detecting the generated response before further processing.)
Generate feedback reflective of the contribution of the first item of content to the training and/or output of the generative model
Wherein the feedback comprises a token reflective of the contribution (In paragraph [0331], Smith discloses that the processing device receives feedback for the first document, where the feedback includes a rating for the first piece of writing.)
Transfer the token to a destination associated with a source of the first item of content via the network interface (In paragraph [0398], Smith discloses sending the first piece of writing to a pre-publication editing tool and receiving feedback from the pre-publication editing tool, where the feedback is generated by a user of the pre-publication editing tool.)
With respect to claim 1, Smith do not explicitly disclose:
Estimate a contribution of a first item of content used for training the generative model to the training and/or output of the generative model, wherein the estimation is performed using an ensemble of attribution techniques and is reflective of a contribution to an adjustment of parameters of the generative model
Wherein the ensemble of attribution techniques comprises two or more of:
using labels assigned to the first item of content to estimate the contribution of the first item of content to the training and/or output of the generative model;
determining an amount or percentage that the first item of content contributes to a neuron parameter comprising a change in a neuron weight in the generative model;
analyzing content output by the generative model using another model comprising a language model, to determine a contribution of the first item of training content to the training and/or output of the generative model;
performing a stylometric analysis of the generative model output to determine authorship or contribution using one or more of a Support Vector Machine configured to perform classification tasks by finding a hyperplane that best separates different classes in a feature space, a Random Forest, a probabilistic algorithm, deep learning techniques, and/or a K-Nearest Neighbors algorithm
Wherein respective outputs of the ensemble of attribution techniques are combined and differently weighted, reflecting their respective reliability and/or accuracy
However, AYKUT is known to disclose:
Estimate a contribution of a first item of content used for training the generative model to the training and/or output of the generative model, wherein the estimation is performed using an ensemble of attribution techniques and is reflective of a contribution to an adjustment of parameters of the generative model (In paragraph [0067], AYKUT discloses determining the influence of contributors during training and output generation. In paragraph [0068], AYKUT discloses quantifying, using percentage values that determine the influence of a set of content creators. In paragraph [0068-0069], AYKUT discloses adjusting the attribution of creators to individual training content items.)
Wherein the ensemble of attribution techniques comprises two or more of:
using labels assigned to the first item of content to estimate the contribution of the first item of content to the training and/or output of the generative model;
determining an amount or percentage that the first item of content contributes to a neuron parameter comprising a change in a neuron weight in the generative model;
analyzing content output by the generative model using another model comprising a language model, to determine a contribution of the first item of training content to the training and/or output of the generative model; performing a stylometric analysis of the generative model output to determine authorship or contribution using one or more of a Support Vector Machine configured to perform classification tasks by finding a hyperplane that best separates different classes in a feature space, a Random Forest, a probabilistic algorithm, deep learning techniques, and/or a K-Nearest Neighbors algorithm (The examiner selects: (using labels assigned to the first item of content to estimate the contribution of the first item of content to the training and/or output of the generative model and determining the amount or percentage that the first item of content contributes to a neuron parameter comprising a change in a neuron weight in the generative model) In paragraph [0068], AYKUT disclose determining the influence of a set of content creators, selected by the user, on the generative AI when generating the output. The adjusted attribution may determine individual percentages of influence associated with each of the selected creators, with each percentage ranging from 0% to 100%.)
Wherein respective outputs of the ensemble of attribution techniques are combined and differently weighted, reflecting their respective reliability and/or accuracy (In paragraph [0076], AYKUT discloses that when the output is generated, each of the weights may be activated differently. The activated output of the weights 414 are the activations. Averaging the resulting multiplied values for each content item, the attribution of each content item on the resulting output may be determined.)
Smith and AYKUT are analogous pieces of art because both references concern the generative language models. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Smith, with one or more generative models that are configured to machine-generate other forms of digital content, such as images, audio, video, etc., as taught by Smith, with generative AI using the input to generate an output that includes derivative content derived from the training content as taught by AYKUT. The motivation for doing so would have been to improve prompts and subsequent GLM outputs (See[0042] of Smith.)
Regarding claim 2, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein the system is configured to estimate contribution percentages of a plurality of content items to the generative model output and to transmit corresponding pro rata feedback to respective sources of items in the plurality of content items (In paragraph [0068], AYKUT discloses determining the influence of a set of content creators, selected by the user, on the generative AI when generating the output. The adjusted attribution may determine individual percentages of influence associated with each of the selected creators, with each percentage ranging from 0% to 100%.)
Regarding claim 3, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the generative model comprises a large language model (In paragraph [0054], Smith disclose one or more large language models (LLMs).)
Regarding claim 4, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein the generative model comprises an image generator (In paragraph [0019], AYKUT discloses that Generative artificial intelligence (AI), implemented using, for example, a diffusion model or similar AI, may be used to generate digital content.)
Regarding claim 5, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the generative model output comprises text, still image data, video image data and/or audio data (In paragraph [0051], Smith disclose a generative language model that are configured to machine-generate other forms of digital content, such as images, audio, video, etc.)
Regarding claim 6, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the first item of content comprises still image data, video image data, and/or audio data (In paragraph [0051], Smith disclose a generative language model that are configured to machine-generate other forms of digital content, such as images, audio, video, etc.)
Regarding claim 7, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the first item of content comprises text data (In paragraph [0054], Smith disclose a multimodal neural network of the generative collaborative publishing system is capable of outputting digital content that includes a combination of two or more of text, images, video or sound.)
Regarding claim 8, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein the estimated contribution of the first item of content to the generative model output comprises an estimated contribution of style (In paragraph [0067], AYKUT discloses determining the influence of contributors during training and output generation. In paragraph [0068], AYKUT discloses quantifying, using percentage values determines the influence of a set of content creators. In paragraph [0068-0069], AYKUT discloses adjusting the attribution of creators to individual training content items.)
Regarding claim 9, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the estimated contribution of the first item of content to the generative model output comprises an estimated contribution of vocabulary choice, sentence structure, grammar and punctuation, tone and voice, themes and topics, and/or rhetorical devices to the generative model output (In paragraph [0063], Smith discloses an editing tool 116 receives documents from grouping mechanism 112 and can make one or more edits to those documents. The edits may modify words, phrases, sentences, and can change the document's content style, tone, grammar, or overall structure.)
Regarding claim 10, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the estimated contribution of the first item of content to the generative model output comprises an estimated contribution of symbols, shapes, motifs, and/or iconography to the generative model output (In paragraph [0225], Smith disclose document metadata includes, for example, special characters or symbols that signify aspects of the structure or organization of the document )
Regarding claim 11, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein the estimated contribution of the first item of content to the generative model output comprises an estimated contribution to one or more claims of the generative model output (In paragraph [0068], AYKUT disclose quantifying, using percentage values determines the influence of a set of content creators. In paragraph [0068-0069], AYKUT disclose adjusting the attribution from creators to individual training content items.)
Regarding claim 12, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein transmitting the feedback generated based at least in part on the estimated contribution of the first item of content to the generative model output to one or more networked destinations, further comprises transmitting feedback to a plurality of networked destinations based at least in part on estimated percentage contributions of a plurality of items of content to the generative model output (In paragraph [0071], Smith disclose the post-publication feedback mechanism 124 generates post-publication contribution feedback 136 based on one or more of the received contributions 128 and returns the post-publication contribution feedback 136 to the generative language model)
Regarding claim 13, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the system is configured to transmit an aggregated feedback for a first period of time to the one or more networked destinations (In paragraph [0398], Smith disclose sending the first piece of writing to a pre-publication editing tool receiving feedback from the pre-publication editing tool, where the feedback is generated by a user of the pre-publication editing tool )
Regarding claim 14, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein the system is operable to estimate the contribution of the first item of content, used to train the generative model, to the generative model output based at least in part on labels associated with the first item of content, changes in weights of the generative model caused at least partly by training of the generative model using the first item of content, and/or based on an analysis of the output of the generative model (In paragraph [0076], AYKUT discloses that when the output is generated, each of the weights may be activated differently. The activated output of the weights 414 are the activations. Averaging the resulting multiplied values for each content item, the attribution of each content item on the resulting output may be determined.)
Regarding claim 15, Smith in view of AYKUT disclose the elements of claim 1. In addition, Smith disclose:
The computer system as defined in Claim 1, wherein the feedback comprises label weights associated with labels assigned to the first item of content, an identification of a contribution to an adjustment of a weight of the generative model, and/or a token (In paragraph [0331], Smith disclose the processing device receives feedback for the first document, where the feedback includes a rating for the first piece of writing.)
Regarding claim 16, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein estimating the contribution of the first item of content to the generative model output further comprises estimating a style contribution of the first item of content to the generative model output (In paragraph [0100], AYKUT discloses the process (the trained AI) may begin generating an output based on the input. While generating the output, the process may determine (i) activations of neural pathways in the trained AI and (ii) the amount (weight) of individual activations.)
Regarding claim 19, Smith in view of AYKUT disclose the elements of claim 1. In addition, AYKUT disclose:
The computer system as defined in Claim 1, wherein the computer system is operable to adjust an attribution score for at least one content source based at least in part on user feedback with respect to generative model outputs generated using content from the at least one content source (In paragraph [0068-0069], AYKUT discloses adjusting the attribution of creators to individual training content items.)
With respect to claim 20, Smith disclose:
A computer-implemented method, the method comprising: accessing an item of generated content from non-transitory memory (In paragraph [0393], Smith disclose a processor executes instructions stored in a non-transitory machine -readable storage medium to perform the disclose methods.
Determining a feedback comprising a first token amount based at least in part on the estimated contribution of the first item of content to the generated content (In paragraph [0398], Smith discloses generating training data based on the feedback and training the generative language model based on the generated training data.)
Transferring the first token amount to a first networked destination associated with a source of the first item of content (In paragraph [0398], Smith disclose sending the first piece of writing to a pre-publication editing tool receiving the feedback from the pre-publication editing tool, where the feedback is generated by a user of the pre-publication editing tool)
The first token amount determined based at least in part on the estimated contribution of the first item of content to the generated content (In paragraph [0331], Smith discloses that the processing device receives feedback for the first document, where the feedback includes a rating for the first piece of writing.)
With respect to claim 20, Smith do not explicitly disclose:
Estimating, using at least one processing device comprising one or more arithmetic logic units, registers, and buses, a contribution of a first item of content, used to train a generative model which generated the generated content, to the generated content
Estimating, using at least one processing device comprising one or more arithmetic logic units, registers, and buses, a contribution of a first item of content, used to train a generative model which generated the generated content, to the generated content, wherein the estimated contribution is determined via an ensemble of attribution techniques comprising two or more of: a determined modification to neuron weights of the generative model, wherein when the first item of content is used to train the generative model modification of the neuron weights are monitored during training and corresponding data is stored; use labels assigned to the first item of content to predict or estimate a contribution of the first item of content to the training and/or output of the generative model; generate prompts comprising content from a plurality of data sources including the first item of content, provide the generated prompts to the generative model to generate respective outputs, and determine similarities between the generative model outputs and respective content from the content sources; analyze content output by the generative model using another model to determine a contribution of the first item of content to the training and/or output of the generative model; a stylometric analysis of the generative model output performed using a Support Vector machine configured to perform authorship attribution, a random forest, a probabilistic algorithm, deep learning techniques, and/or a K-Nearest Neighbors algorithm
Wherein respective outputs of the ensemble of attribution techniques are combined and at least two of the ensemble of attribution techniques are weighted differently reflecting respective reliability and/or accuracy in estimating the contribution of the first item of content to the generative model output comprising the generative content
However, AYKUT is known to disclose:
Estimating, using at least one processing device comprising one or more arithmetic logic units, registers, and buses, a contribution of a first item of content, used to train a generative model which generated the generated content, to the generated content (In paragraph [0067], AYKUT discloses determining the influence of contributors during training and output generation. In paragraph [0068], AYKUT discloses quantifying, using percentage values that determine the influence of a set of content creators. In paragraph [0068-0069], AYKUT discloses adjusting the attribution of creators to individual training content items.)
Estimating, using at least one processing device comprising one or more arithmetic logic units, registers, and buses, a contribution of a first item of content, used to train a generative model which generated the generated content, to the generated content, wherein the estimated contribution is determined via an ensemble of attribution techniques comprising two or more of: a determined modification to neuron weights of the generative model, wherein when the first item of content is used to train the generative model modification of the neuron weights are monitored during training and corresponding data is stored; use labels assigned to the first item of content to predict or estimate a contribution of the first item of content to the training and/or output of the generative model; generate prompts comprising content from a plurality of data sources including the first item of content, provide the generated prompts to the generative model to generate respective outputs, and determine similarities between the generative model outputs and respective content from the content sources; analyze content output by the generative model using another model to determine a contribution of the first item of content to the training and/or output of the generative model; a stylometric analysis of the generative model output performed using a Support Vector machine configured to perform authorship attribution, a random forest, a probabilistic algorithm, deep learning techniques, and/or a K-Nearest Neighbors algorithm (The examiner selects: (using labels assigned to the first item of content to estimate the contribution of the first item of content to the training and/or output of the generative model and determining the amount or percentage that the first item of content contributes to a neuron parameter comprising a change in a neuron weight in the generative model) In paragraph [0068], AYKUT disclose determining the influence of a set of content creators, selected by the user, on the generative AI when generating the output. The adjusted attribution may determine individual percentages of influence associated with each of the selected creators, with each percentage ranging from 0% to 100%.)
Wherein respective outputs of the ensemble of attribution techniques are combined and at least two of the ensemble of attribution techniques are weighted differently reflecting respective reliability and/or accuracy in estimating the contribution of the first item of content to the generative model output comprising the generative content (In paragraph [0076], AYKUT discloses that when the output is generated, each of the weights may be activated differently. The activated output of the weights 414 are the activations. Averaging the resulting multiplied values for each content item, the attribution of each content item on the resulting output may be determined.)
Smith and AYKUT are analogous pieces of art because both references concern the generative language models. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Smith, with one or more generative models that are configured to machine-generate other forms of digital content, such as images, audio, video, etc., as taught by Smith, with generative AI using the input to generate an output that includes derivative content derived from the training content as taught by AYKUT. The motivation for doing so would have been to improve prompts and subsequent GLM outputs (See[0042] of Smith.)
Regarding claim 21, Smith in view of AYKUT disclose the elements of claim 20. In addition, AYKUT disclose:
The computer-implemented method as defined in Claim 20, the method further comprising: estimating contribution percentages of a plurality of content items to the generated content; and transmitting corresponding pro rata feedback to respective sources of items in the plurality of content items (In paragraph [0068], AYKUT discloses determining the influence of a set of content creators, selected by the user, on the generative AI when generating the output. The adjusted attribution may determine individual percentages of influence associated with each of the selected creators, with each percentage ranging from 0% to 100%.)
Regarding claim 22, Smith in view of AYKUT disclose the elements of claim 20. In addition, Smith disclose:
The computer-implemented method as defined in Claim 20, wherein the generated content comprises text, image data, or audio data (In paragraph [0051], Smith disclose a generative language model that are configured to machine-generate other forms of digital content, such as images, audio, video, etc.)
Regarding claim 23, Smith in view of AYKUT disclose the elements of claim 20. In addition, AYKUT disclose:
The computer-implemented method as defined in Claim 20, wherein the generated content is generated using a generative model, wherein estimating the contribution of the first item of content to the generated content is based at least in part on labels associated with the first item of content, changes in weights of a generative model used to generate the generated content caused at least partly by training of the generative model using the first item of content, and/or based on an analysis of the output of the generative model (In paragraph [0076], AYKUT discloses that when the output is generated, each of the weights may be activated differently. The activated output of the weights 414 are the activations. Averaging the resulting multiplied values for each content item, the attribution of each content item on the resulting output may be determined.)
Regarding claim 24, Smith in view of AYKUT disclose the elements of claim 20. In addition, Smith disclose:
The computer-implemented method as defined in Claim 20, wherein the feedback comprises label weights associated with labels assigned to the first item of content, an identification of a contribution to an adjustment of a weight of a generative model used to generate the generated content, and/or a token (In paragraph [0331], Smith disclose the processing device receives feedback for the first document, where the feedback includes a rating for the first piece of writing.)
Regarding claim 26, Smith in view of AYKUT disclose the elements of claim 20. In addition, Smith disclose:
The computer-implemented method as defined in Claim 20, wherein the estimated contribution of the first item of content to the generated model comprises an estimated contribution of vocabulary choice, sentence structure, grammar and punctuation, tone and voice, themes and topics, and/or rhetorical devices to the generated model (In paragraph [0063], Smith discloses an editing tool 116 receives documents from grouping mechanism 112 and can make one or more edits to those documents. The edits may modify words, phrases, sentences, and can change the document's content style, tone, grammar, or overall structure.)
Regarding claim 27, Smith in view of AYKUT disclose the elements of claim 20. In addition, Smith disclose:
The computer-implemented method as defined in Claim 20, wherein the estimated contribution of the first item of content to the generated content comprises an estimated contribution of symbols, shapes, motifs, and/or iconography to the generated content (In paragraph [0225], Smith disclose document metadata includes, for example, special characters or symbols that signify aspects of the structure or organization of the document )
Regarding claim 28, Smith in view of AYKUT disclose the elements of claim 20. In addition, AYKUT disclose:
The computer-implemented method as defined in Claim 20, wherein the estimated contribution of the first item of content to the generated content comprises an estimated contribution to one or more claims of the generated content (In paragraph [0068], AYKUT disclose quantifying, using percentage values determines the influence of a set of content creators. In paragraph [0068-0069], AYKUT disclose adjusting the attribution from creators to individual training content items.)
Regarding claim 29, Smith in view of AYKUT disclose the elements of claim 20. In addition, Smith disclose:
The computer-implemented method as defined in Claim 20, wherein transmitting the feedback generated based at least in part on the estimated contribution of the first item of content to the generated content to one or more networked destinations, further comprises transmitting feedback to a plurality of networked destinations based at least in part on estimated percentage contributions of a plurality of items of content to the generated content (In paragraph [0071], Smith disclose the post-publication feedback mechanism 124 generates post-publication contribution feedback 136 based on one or more of the received contributions 128 and returns the post-publication contribution feedback 136 to the generative language model.)
Regarding claim 30, Smith in view of AYKUT disclose the elements of claim 20. In addition, Smith disclose:
The computer-implemented method as defined in Claim 20, the method further comprising transmitting an aggregated feedback for a first period of time to the one or more networked destinations (In paragraph [0398], Smith disclose sending the first piece of writing to a pre-publication editing tool receiving feedback from the pre-publication editing tool, where the feedback is generated by a user of the pre-publication editing tool )
Claim(s) 17-18 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Smith in view of AYKUT and further in view of Poirier et al. (Pub No.: 20240202539 A1), hereinafter referred to as Poirier.
Regarding claim 17, Smith in view of AYKUT disclose the elements of claim 1. Smith in view of AYKUT do not explicitly disclose:
The computer system as defined in Claim 1, wherein the computer system is operable to: chunk text of at least a first document into a plurality of overlapping chunks
generate embeddings comprising vectors corresponding to the plurality of overlapping chunks
store the embeddings corresponding to the plurality of overlapping chunks in a vector database
However, Poirier disclose the limitation:
The computer system as defined in Claim 1, wherein the computer system is operable to: chunk text of at least a first document into a plurality of overlapping chunks (In paragraph [0219], Poirier discloses that the system can outline a process for information retrieval. One approach for these starts with first embedding the content of the text nodes (and/or other textual metadata associated with other modalities) (step 1629), and storing the embeddings in a vector store.)
generate embeddings comprising vectors corresponding to the plurality of overlapping chunks (In paragraph [0206], Poirier discloses a similarity machine learning process that determines a similarity between two or more segments and/or data records based on the embeddings (e.g., vector embeddings generated based on the data segments, and the contextual metadata).)
store the embeddings corresponding to the plurality of overlapping chunks in a vector database (In paragraph [0219], Poirier discloses the first embedding of the content of the text nodes (and/or other textual metadata associated with other modalities) (step 1629), and storing the embeddings in a vector store.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Smith in view of AYKUT to include Poirier, with generative artificial intelligence system architecture that includes one or more large language models (e.g., of an orchestrator module) that can develop a plan for responding to the input received in the input layer as taught by Poirier. The motivation for doing so would have been improve similarity evaluations used in retrieval operations (e.g., of a generative artificial intelligence process) (See [0023] of Poirier.)
Regarding claim 18, Smith in view of AYKUT disclose the elements of claim 1. Smith in view of AYKUT do not explicitly disclose:
The computer system as defined in Claim 1, wherein the computer system is operable to: generate a prompt instructing the generative model to use only specified document chunks in providing a response to the generated prompt
receive a response to the generated prompt from the generative model
determine similarities of the response to the generated prompt to the specified document chunks
However, Poirier disclose the limitation:
The computer system as defined in Claim 1, wherein the computer system is operable to: generate a prompt instructing the generative model to use only specified document chunks in providing a response to the generated prompt (In paragraph [0201], Poirier disclosed the computing system chunks, a plurality of data records from multiple enterprise data sources of the plurality of different data domains of the enterprise information environment. The chunking can generate one or two respective data record segments for each of the plurality of data records.)
receive a response to the generated prompt from the generative model (In paragraph [0168], Poirier disclosed that the large language model 844 can generate a final response 878 based on the rewritten query 866 and the combined extracts.)
determine similarities of the response to the generated prompt to the specified document chunks (In paragraph [0206], Poirier discloses a similarity machine learning process that determines a similarity between two or more segments and/or data records based on the embeddings (e.g., vector embeddings generated based on the data segments, and the contextual metadata). )
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Smith in view of AYKUT to include Poirier, with generative artificial intelligence system architecture that includes one or more large language models (e.g., of an orchestrator module) that can develop a plan for responding to the input received in the input layer as taught by Poirier. The motivation for doing so would have been improve similarity evaluations used in retrieval operations (e.g., of a generative artificial intelligence process) (See [0023] of Poirier.)
Regarding claim 25, Smith in view of AYKUT disclose the elements of claim 1. Smith in view of AYKUT do not explicitly disclose:
The computer-implemented method as defined in Claim 20, the method further comprising: generating a prompt instructing a generative model to use only specified document chunks in providing a response to a prompt
receiving a response to the prompt from the generative model
determining similarities of the response to the generated prompt to the specified document chunks
However, Poirier disclose the limitation:
The computer-implemented method as defined in Claim 20, the method further comprising: generating a prompt instructing a generative model to use only specified document chunks in providing a response to a prompt (In paragraph [0201], Poirier disclosed the computing system chunks, a plurality of data records from multiple enterprise data sources of the plurality of different data domains of the enterprise information environment. The chunking can generate one or two respective data record segments for each of the plurality of data records.)
receiving a response to the prompt from the generative model (In paragraph [0168], Poirier disclosed that the large language model 844 can generate a final response 878 based on the rewritten query 866 and the combined extracts.)
determining similarities of the response to the generated prompt to the specified document chunks (In paragraph [0206], Poirier discloses a similarity machine learning process that determines a similarity between two or more segments and/or data records based on the embeddings (e.g., vector embeddings generated based on the data segments, and the contextual metadata). )
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of Smith in view of AYKUT to include Poirier, with generative artificial intelligence system architecture that includes one or more large language models (e.g., of an orchestrator module) that can develop a plan for responding to the input received in the input layer as taught by Poirier. The motivation for doing so would have been improve similarity evaluations used in retrieval operations (e.g., of a generative artificial intelligence process) (See [0023] of Poirier.)
Response to Arguments
The applicant's arguments filed 07/10/2026 have been fully considered, but in part are not persuasive.
Pertaining to Rejection under 101
The rejection of 35 USC § 101 has been withdrawn.
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights