Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This is the initial office action based on the application filed on February 14th, 2024, which claims 1-20 are presented for examination.
Status of Claims
Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 11, and 16 are presented in independent form.
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by the Examiner via PTO Automated Interview Request web service. To schedule an
interview, applicant is encouraged to use the USPTO Automated Interview Request
(AER) at http://www.uspto.gov/interviewpractice.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
The information disclosure statement, filed on May 20th, 2025, complies with the provisions of 37 CFR 1.97, 1.98. The complied IDS has been placed in the application file and the information referred to therein has been considered as to the merits.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1 recites the limitation “a user” in line 4. It should be -- the [[a]] user --.
Claim 2 recites limitation “a user input” in line 2. It should be -- the [[a]] user input --.
Claim 5 recites limitation “a selection of an API call” in line 5. It should be -- [[a]] the selection of [[an]] the API call --.
Claim 7 recites limitation “the output of the AI integration” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. In the interest of compact prosecution, the examiner subsequently interprets this limitation as reading -- [[the]] an output of the AI integration --.
Claim 8 recites limitations “a deployment configuration” and “a transformer type” in lines 6-7. They should be -- [[a]] the deployment configuration -- and -- [[a]] the transformer type --, respectively.
Claim 9 recites limitations “a selection of the icon” in line 2 and “the output” in line 5. They should be -- [[a]] the selection of the icon -- and -- [[the]] an output --, respectively.
Claim 10 recites limitations “a selection of the icon” in line 7 and “the output” in line 10. They should be -- [[a]] the selection of the icon -- and -- [[the]] an output --, respectively.
Claims 2-10: are dependent on claim 1 but not cure the deficiencies of that claim. Accordingly, they are objected for the same reasons.
Claim 11 and 14 recite limitation “type of transformer” in lines 5-6, 16, and 5-6, respectively. It should be -- type of the transformer --.
Claim 15 recites limitation “a selection of an API call” in line 8. It should be -- [[a]] the selection of [[an]] the API call --.
Claims 12-15: are dependent on claim 11 but not cure the deficiencies of that claim. Accordingly, they are objected for the same reasons.
Claim 16 recites limitations “type of transformer” in line 8-9 and 19 and “an AI integration” in line 21. They should be -- type of the transformer -- and -- [[an]] the AI integration --, respectively.
Claim 17 and 20 recite limitation “a selection of the icon” in lines 2 and 7, respectively. It should be -- [[a]] the selection of the icon --.
Claim 18 recites limitation “the generation of the AI integration” in line 2-3. There is insufficient antecedent basis for this limitation in the claim. In the interest of compact prosecution, the examiner subsequently interprets this limitation as reading -- a [[the]] generation of the AI integration --.
Claim 20 recites limitation “the output of the AI integration” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. In the interest of compact prosecution, the examiner subsequently interprets this limitation as reading -- [[the]] an output of the AI integration --.
Claims 17-20: are dependent on claim 16 but not cure the deficiencies of that claim. Accordingly, they are objected for the same reasons.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the second paragraph of 35 U.S.C. 112:
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 3, 6, 14, and 17 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 3 recites limitation “a user” in lines 4 and 6. It is unclear whether this user is same as “the user” in claim 1. In the interest of compact prosecution, the examiner subsequently interprets this limitation as reading -- a second user --.
Claim 6 recites limitation “the request” in line 2. It is unclear whether this request refers to “request to generate a prompt” in line 2 of claim 1 or “request to test a performance of the prompt” in claim 6.
Claim 14 recites limitation “the request” in line 3. It is unclear whether this request refers to “request to generate a transformer” in line 4 of claim 1 or “request to test a performance of the prompt” in claim 14.
Claim 17 recites limitation “the output” in line 4. It is unclear whether this output refers to “an output” in line 12 of claim 16 or “an output of the AI integration” in claim 17.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-7, 9-17, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cella et al. (Pub. No.: US 2023/0252047 – IDS filed 05/20/2025 – hereinafter, Cella).
Regarding claim 1:
Cella discloses a method comprising:
receiving, from a user, a request to generate a prompt, the prompt defining an instruction to one or more machine learning models to perform a task or generate an output based at least in part on an input received from a user (“The artificial intelligence system 1160 takes in the raw data, pre-processes it and applies machine learning algorithms to generate the predictive maintenance model. In embodiments, the artificial intelligence system 1160 may store the predictive model in a model datastore within data storage layer 624” (See para [0773]). “In embodiments, the customer profile digital twin 1730 or other customer digital twin may be created interactively and cooperatively with a customer, such as by allowing a customer to request, select, modify, delete, or otherwise influence a set of properties, states, behaviors, or other aspects represented in the digital twin 1730” (See para [0810]). “In embodiments, the on-boarding user may define the types of enterprise digital twins that may be generated by the digital twin system 8004 on behalf of the enterprise being on-boarded. In embodiments, the on-boarding user may select different types of digital twins that will be supported for the enterprise by the EMP 8000 via a GUI presented by the enterprise configuration system 8002… By contrast, if the CEO has less financial experience or training, the CEO digital twin may be configured with summary financial data and may include prompts (which may be generated by an intelligent agent trained on a set of enterprise and/or industry outcomes) to obtain CFO input when states deviate from normal operating conditions.” (See para [0916]));
receiving, from the user, a selection of a type of transformer, the transformer configured to transform an input sequence of vectors into an output sequence by passing the input sequence of vectors through a series of encoder layers and decoder layers (“The training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e., a supervisory signal. Each of the training examples may be represented in the machine learning model 3000 by an array and/or a vector, i.e., a feature vector” (See para [0535]). “In some embodiments, training data, outcome data 2060, and/or simulation data 2022 may be fed into a data lake (e.g., a Hadoop data lake)... Depending on the type of model, the machine-learning algorithm will vary. Examples of learning algorithms/models include (e.g., deep neural networks, convolution neural networks, and many others as described throughout this disclosure), statistical models (e.g., regression-based models and many others), decision trees and other decision models, random/hidden forests, Hidden Markov models, Bayesian models, and the like” (See para [0615]). “When presented with a vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as using the spread values. The resulting value may then be passed to the summation layer” (See para [1147]). “In embodiments, artificial intelligence modules 8804 may include and/or provide access to a neural network module 8814. In embodiments, the neural network module 8814 is configured to train, deploy, and/or leverage artificial neural networks (or “neural networks”) on behalf of an intelligence service client 8836…where a given type of neural network takes inputs from a data source or other neural network and provides outputs that are included within the input sets of another neural network until a flow is completed and a final output is provided” (See para [1239]). “The example transformer includes an encoder 8802 and a decoder 8804… Decoder 8804 stage 5 provides a linear transformation followed by a softmax function to normalize a resulting vector of K numbers into a probability distribution 8806 including K probabilities proportional to exponentials of the K input numbers” (See paras [1287] – 1288]));
receiving, from the user, a selection of the prompt (“For example, a goal to determine what is happening in a warehouse may be achieved with a variety of microservices with minimal cost such as vision-based service, series of regular prompts that may ask and receive, reading off of event logs or feeds, and the like.” (See para [0688]). “For example, a digital twin 1700 may receive queries from a user about the value chain network entities 652, generate responses for the queries, and communicate such responses to the user.” (See para [0708]));
receiving, from the user, a deployment configuration, the deployment configuration indicating an enterprise system and a deployment location within the enterprise system (“In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission” (See para [1142]). Also, see para [0916]);
receiving, from the user, a selection of an icon (“In embodiments, one of the processes automated by robotic process automation involves selection of visual elements for presentation in a digital twin.” (See para [0437]));
generating, based at least in part on the deployment configuration, the type of transformer, and the prompt, a configuration object associated with an artificial intelligence (AI) integration, the AI integration configured to perform the task utilizing the one or more machine learning models (“For example, certain kinds of inputs, if available, would provide very high value for automation, such as video data sets that capture very experienced and/or highly expert workers performing complex tasks… In embodiments, such videos may be processed by natural language processing, such as to automatically develop a sequence of labeled instructions that can be used by a developer to facilitate a map, a graph, or other models of a process that assists with development of automation for the process” (See para [0441]). Also, see paragraphs [0535], [0615], [1147], [1239], and [1287]); and
presenting, via a user interface and based at least in part on the deployment configuration, the icon associated with the AI integration (“The digital twins 1700 may have a visual user interface, e.g., in the form of 3D models, or may consist of system specifications or ontologies describing the architecture, including components and their interfaces, of the value chain network entities 652.” (See para [0708]). Also, see paragraphs [0437], [0441], [0535], [0615], [1147], [1239], and [1287]).
Regarding claim 3:
The rejection of claim 1 is incorporated, Cella further discloses wherein the task is associated with at least one of:
generating a summary of a document or message; generating feedback for a user; identifying recommendations from a text; identifying a user with a similar goal; generating a list of competencies; identifying a topic from a goal; rephrasing a text to remove bias; or rephrasing a text to match a tone (“In embodiments, the types of actions that an expert agent may be trained to perform/recommend include: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, configuration of settings, flagging an item for review, providing an alert, providing a summary report of data,…” (See para [0931]). Also, see paragraph [0986]).
Regarding claim 4:
The rejection of claim 1 is incorporated, Cella further discloses wherein the transformer comprises at least one of an options transformer, a direct transformer, or a multi-step transformer (“In embodiments, transformer-based, encoder-decoder architectures using attention mechanisms may be used in conjunction with or in place of convolutional neural networks.” See para [1819])).
Regarding claim 5:
The rejection of claim 4 is incorporated, Cella further comprising:
based at least in part on receiving a selection of the multi-step transformer to associate with the AI integration, causing presentation of a graphical element to enable selection of an API call (“In embodiments, the visualization may include display of information related to the visualized digital twins, including graphical information, graphical information depicting physical characteristics, graphical information depicting financial characteristics, graphical information depicting performance characteristics, recommendations from intelligence service 13004, predictions from intelligence service 13004, probability of failure data, maintenance history data, time to failure data, cost of downtime data, probability of downtime data, cost of repair data, cost of replacement data (e.g., replacing a smart container or smart container component), and the like.” (See para [2430])); and
receiving, from the user, a selection of an API call to associate with the AI integration ((“In embodiments, one of the processes automated by robotic process automation involves selection of visual elements for presentation in a digital twin.” (See para [0437]). “In embodiments, the visualization may include display of information related to the visualized digital twins, including graphical information, graphical information depicting physical characteristics, graphical information depicting financial characteristics, graphical information depicting performance characteristics, recommendations from intelligence service 13004, predictions from intelligence service 13004, probability of failure data, maintenance history data, time to failure data, cost of downtime data, probability of downtime data, cost of repair data, cost of replacement data (e.g., replacing a smart container or smart container component), and the like.” (See para [2430])).
Regarding claim 6:
The rejection of claim 1 is incorporated, Cella further comprising:
receiving a request to test a performance of the prompt, the request including sample text (“In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission” (See para [1142]));
generating, based at least in part on the sample text and the type of transformer, a result of the test (“When presented with a vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as using the spread values. The resulting value may then be passed to the summation layer” (See para [1147])); and
presenting, via the user interface, the result of the test proximate the sample text (“The digital twins 1700 may have a visual user interface, e.g., in the form of 3D models, or may consist of system specifications or ontologies describing the architecture, including components and their interfaces, of the value chain network entities 652.” (See para [0708])).
Regarding claim 7:
The rejection of claim 1 is incorporated, Cella further comprising:
receiving AI integration usage data (“In some embodiments, the expert agent system 8008 receives feedback from users regarding respective executive agents. For example, in some embodiments, a client application 8052 that leverages an expert agent may provide an interface by which a user can provide feedback regarding an action output by an expert agent.” (See para [0935]));
determining an error occurred in association with the output of the AI integration (“In embodiments, the user provides the feedback that identifies and characterizes any errors by the expert agent. In some of these embodiments, a report may be generated (e.g., by the client application or the EMP 8000) that indicates the set of errors encountered by the expert. The report may be used to reconfigure/retrain the executive agent. In embodiments, the reconfiguring/retraining an executive agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and/or augmenting the set of inputs to the artificial intelligence system.” (See para [0935])); and
presenting an error message indicating a cause of the error (“In some of these embodiments, a report may be generated (e.g., by the client application or the EMP 8000) that indicates the set of errors encountered by the expert. The report may be used to reconfigure/retrain the executive agent. In embodiments, the reconfiguring/retraining an executive agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and/or augmenting the set of inputs to the artificial intelligence system.” (See para [0935])).
Regarding claim 9:
The rejection of claim 1 is incorporated, Cella further comprising:
receiving, from the user, a selection of the icon associated with the AI integration (“In embodiments, one of the processes automated by robotic process automation involves selection of visual elements for presentation in a digital twin.” (See para [0437])); and
generating, based at least in part on the selection of the icon and the prompt, the output of the AI integration, the output associated one or more selectable controls (“In embodiments, the customer profile digital twin 1730 or other customer digital twin may be created interactively and cooperatively with a customer, such as by allowing a customer to request, select, modify, delete, or otherwise influence a set of properties, states, behaviors, or other aspects represented in the digital twin 1730” (See para [0810]). “In embodiments, the on-boarding user may define the types of enterprise digital twins that may be generated by the digital twin system 8004 on behalf of the enterprise being on-boarded. In embodiments, the on-boarding user may select different types of digital twins that will be supported for the enterprise by the EMP 8000 via a GUI presented by the enterprise configuration system 8002… By contrast, if the CEO has less financial experience or training, the CEO digital twin may be configured with summary financial data and may include prompts (which may be generated by an intelligent agent trained on a set of enterprise and/or industry outcomes) to obtain CFO input when states deviate from normal operating conditions.” (See para [0916])).
Regarding claim 10:
The rejection of claim 1 is incorporated, Cella further discloses wherein the prompt is a first prompt, the instruction is a first instruction, and the task is a first task, the method further comprising:
receiving, in addition to the first prompt, a selection of a second prompt different than the first prompt, the second prompt defining a second instruction to the one or more machine learning models to perform a second task (“For example, a goal to determine what is happening in a warehouse may be achieved with a variety of microservices with minimal cost such as vision-based service, series of regular prompts that may ask and receive, reading off of event logs or feeds, and the like.” (See para [0688]). “For example, a digital twin 1700 may receive queries from a user about the value chain network entities 652, generate responses for the queries, and communicate such responses to the user.” (See para [0708]));
receiving, from the user, a selection of the icon associated with the AI integration (“In embodiments, one of the processes automated by robotic process automation involves selection of visual elements for presentation in a digital twin.” (See para [0437])); and
based at least in part on the selection of the icon, the first prompt, and the second prompt, generating the output of the AI integration (“For example, a goal to determine what is happening in a warehouse may be achieved with a variety of microservices with minimal cost such as vision-based service, series of regular prompts that may ask and receive, reading off of event logs or feeds, and the like.” (See para [0688]). “For example, a digital twin 1700 may receive queries from a user about the value chain network entities 652, generate responses for the queries, and communicate such responses to the user.” (See para [0708])).
Regarding claim 11:
Cella discloses one or more non-transitory computer-readable media storing instructions (FIG. 69 – 8120) that, when executed by one or more processors (FIG. 69 – 8100 and associated text “In embodiments, the digital twin system 8004 is executed by a computing system (e.g., one or more servers) that may include a processing system 8100 that includes one or more processors, a storage system 8120 that includes one or more computer-readable mediums, and a network interface 8130 that includes one or more communication units that communicate with a network (e.g., the Internet, a private network, and the like)” (See para [0945])), cause the one or more processors to perform operations comprising:
receiving, from a user computing device, a request to generate a transformer (“The artificial intelligence system 1160 takes in the raw data, pre-processes it and applies machine learning algorithms to generate the predictive maintenance model. In embodiments, the artificial intelligence system 1160 may store the predictive model in a model datastore within data storage layer 624” (See para [0773]). “In embodiments, the on-boarding user may define the types of enterprise digital twins that may be generated by the digital twin system 8004 on behalf of the enterprise being on-boarded. In embodiments, the on-boarding user may select different types of digital twins that will be supported for the enterprise by the EMP 8000 via a GUI presented by the enterprise configuration system 8002… By contrast, if the CEO has less financial experience or training, the CEO digital twin may be configured with summary financial data and may include prompts (which may be generated by an intelligent agent trained on a set of enterprise and/or industry outcomes) to obtain CFO input when states deviate from normal operating conditions.” (See para [0916]));
receiving, from the user computing device, a selection of a type of transformer (“The training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e., a supervisory signal. Each of the training examples may be represented in the machine learning model 3000 by an array and/or a vector, i.e., a feature vector” (See para [0535]). “In some embodiments, training data, outcome data 2060, and/or simulation data 2022 may be fed into a data lake (e.g., a Hadoop data lake)...Examples of learning algorithms/models include (e.g., deep neural networks, convolution neural networks, and many others as described throughout this disclosure), statistical models (e.g., regression-based models and many others), decision trees and other decision models, random/hidden forests, Hidden Markov models, Bayesian models, and the like” (See para [0615]). “In embodiments, artificial intelligence modules 8804 may include and/or provide access to a neural network module 8814. In embodiments, the neural network module 8814 is configured to train, deploy, and/or leverage artificial neural networks (or ‘neural networks’) on behalf of an intelligence service client 8836…where a given type of neural network takes inputs from a data source or other neural network and provides outputs that are included within the input sets of another neural network until a flow is completed and a final output is provided” (See para [1239]). “The example transformer includes an encoder 8802 and a decoder 8804… Decoder 8804 stage 5 provides a linear transformation followed by a softmax function to normalize a resulting vector of K numbers into a probability distribution 8806 including K probabilities proportional to exponentials of the K input numbers” (See paras [1287] – 1288]));
receiving, from the user computing device, a selection of a prompt, the prompt defining an instruction to one or more machine learning models to perform a task or generate an output based at least in part on an input (“For example, a goal to determine what is happening in a warehouse may be achieved with a variety of microservices with minimal cost such as vision-based service, series of regular prompts that may ask and receive, reading off of event logs or feeds, and the like.” (See para [0688]). “For example, a digital twin 1700 may receive queries from a user about the value chain network entities 652, generate responses for the queries, and communicate such responses to the user.” (See para [0708]));
receiving, from the user computing device, selection of a deployment configuration, the deployment configuration indicating an enterprise system to deploy an artificial intelligence (AI) integration and a deployment location within the enterprise system (“In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission” (See para [1142]). Also, see paragraph [0916]);
receiving, from the user computing device, a selection of an icon to associate with the AI integration (“In embodiments, one of the processes automated by robotic process automation involves selection of visual elements for presentation in a digital twin.” (See para [0437]));
generating, based at least in part on the type of transformer, the prompt, and the deployment configuration, a configuration object associated with the AI integration (“For example, certain kinds of inputs, if available, would provide very high value for automation, such as video data sets that capture very experienced and/or highly expert workers performing complex tasks… In embodiments, such videos may be processed by natural language processing, such as to automatically develop a sequence of labeled instructions that can be used by a developer to facilitate a map, a graph, or other models of a process that assists with development of automation for the process” (See para [0441]). Also, see paragraphs [0535], [0615], [1147], [1239], and [1287]); and
displaying, based at least in part on the deployment configuration, the icon associated with the AI integration in a user interface associated with the user computing device (“For example, a goal to determine what is happening in a warehouse may be achieved with a variety of microservices with minimal cost such as vision-based service, series of regular prompts that may ask and receive, reading off of event logs or feeds, and the like.” (See para [0688]). “For example, a digital twin 1700 may receive queries from a user about the value chain network entities 652, generate responses for the queries, and communicate such responses to the user.” (See para [0708]). Also, see paragraphs [0437], [0441], [0535], [0615], [1147], [1239], and [1287]).
Regarding claim 12:
The rejection of base claim 11 is incorporated. All the limitations of this claim have been noted in the rejection of claim 4 and is therefore rejected under similar rationale.
Regarding claim 13:
The rejection of base claim 11 is incorporated. All the limitations of this claim have been noted in the rejection of claim 3 and is therefore rejected under similar rationale.
Regarding claim 14:
The rejection of base claim 11 is incorporated. All the limitations of this claim have been noted in the rejection of claim 6 and is therefore rejected under similar rationale.
Regarding claim 15:
The rejection of base claim 11 is incorporated. All the limitations of this claim have been noted in the rejection of claim 5 and is therefore rejected under similar rationale.
Regarding claim 16:
This is a system version of the rejected non-transitory computer-readable media claim 11 above, wherein all the limitations of this claim have been noted in the rejection of claim 11 and is therefore rejected under similar rationale.
Regarding claim 17:
The rejection of base claim 16 is incorporated. All the limitations of this claim have been noted in the rejection of claim 9 and is therefore rejected under similar rationale.
Regarding claim 19:
The rejection of base claim 16 is incorporated. All the limitations of this claim have been noted in the rejection of claim 7 and is therefore rejected under similar rationale.
Regarding claim 20:
The rejection of base claim 16 is incorporated. All the limitations of this claim have been noted in the rejection of claim 10 and is therefore rejected under similar rationale.
Claim Rejections - 35 U.S.C § 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 2 is rejected under 35 U.S.C. § 103 as being unpatentable over Cella in view of Pu et al. (Pub. No.: US 2021/0042662 – IDS filed 05/20/2025 – hereinafter, Pu).
Regarding claim 2:
The rejection of claim 1 is incorporated, but Cella does not explicitly teach:
wherein the request to generate the prompt is associated with a user input indicating a prompt title and one or more tags.
However, Pu discloses:
wherein the request to generate the prompt is associated with a user input indicating a prompt title and one or more tags (FIG. 2 and associated text, such as, “A new custom prompt is defined by clicking on the custom prompt's item with a user-defined prompt's name or label. The user can choose to add more details to the prompt's name or label if desired” (See para [0091]). “New prompts can be created by simply inputting a prompt's name and/or description with the system filling in, based on heuristic and ML models whenever and wherever applicable and/or available, the rest of the prompt attributes such as pop-up conditions, prompt processing options (as-is, image text extraction (logo, label, etc.), NLP (entity extraction, topic modeling, syntax, sentiment, text analytics, etc.), formatting and style options, etc.” (See para [0127])).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pu into the teachings of Cella because that would have provided additional dimensions and information rending mechanisms for information presentment for better slice and dices and understanding of the captured information. It also connects and arranges the related information as captured for improving the ease of navigation, presentation and knowledge acquisition processes as suggested by Pu (See para [0017]).
Claims 8 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Cella in view of Turco et al. (Pub. No.: US 2019/0073570 – IDS filed 05/20/2025 – hereinafter, Turco).
Regarding claim 8:
The rejection of claim 1 is incorporated, Cella further comprising:
receiving AI integration data associated with the generation of the AI integration (“The training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e., a supervisory signal. Each of the training examples may be represented in the machine learning model 3000 by an array and/or a vector, i.e., a feature vector” (See para [0535]). “In some embodiments, training data, outcome data 2060, and/or simulation data 2022 may be fed into a data lake (e.g., a Hadoop data lake)... Depending on the type of model, the machine-learning algorithm will vary. Examples of learning algorithms/models include (e.g., deep neural networks, convolution neural networks, and many others as described throughout this disclosure), statistical models (e.g., regression-based models and many others), decision trees and other decision models, random/hidden forests, Hidden Markov models, Bayesian models, and the like” (See para [0615]). “When presented with a vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as using the spread values. The resulting value may then be passed to the summation layer” (See para [1147]). “In embodiments, artificial intelligence modules 8804 may include and/or provide access to a neural network module 8814. In embodiments, the neural network module 8814 is configured to train, deploy, and/or leverage artificial neural networks (or “neural networks”) on behalf of an intelligence service client 8836…where a given type of neural network takes inputs from a data source or other neural network and provides outputs that are included within the input sets of another neural network until a flow is completed and a final output is provided” (See para [1239])); and
generating a transformer [[table]] to be displayed at a computing device (“In embodiments, artificial intelligence modules 8804 may include and/or provide access to a neural network module 8814. In embodiments, the neural network module 8814 is configured to train, deploy, and/or leverage artificial neural networks (or “neural networks”) on behalf of an intelligence service client 8836…where a given type of neural network takes inputs from a data source or other neural network and provides outputs that are included within the input sets of another neural network until a flow is completed and a final output is provided” (See para [1239])), [[the transformer table comprising a title of the transformer, a date the transformer was generated, a deployment feature associated with the transformer, a deployment configuration associated with the transformer, and a transformer type]].
Cella does not explicitly teach:
the transformer table comprising a title of the transformer, a date the transformer was generated, a deployment feature associated with the transformer, a deployment configuration associated with the transformer, and a transformer type.
However, Turco discloses:
the transformer table comprising a title of the transformer, a date the transformer was generated, a deployment feature associated with the transformer, a deployment configuration associated with the transformer, and a transformer type (“Preferably, the data format used for storing models in the model repository is also independent of the number and type of components of the stored model, e.g. the number and type of hyperparameters, hyperparameter values, model parameters, the number and type of layers of neural network architectures, etc. For example, the model repository may be a file directory comprising models stored as individual XML or JSON files. According to other embodiments, the model repository consists of two database tables: a metadata table for storing model metadata such as model-ID, model name, author, model type, etc., and a content table which comprises a specification of the model in a generic data format, e.g. in JSON format, as a list of property-value pairs, in a binary format, etc” (See para [0032]). FIG. 1 and associated text, such as, “In the first repository table 138, a model identifier and meta-data of the model such as model name, model type, creator, creation date, number of hyperparameters or the like are stored. In the second model repository table 140, the specification (‘body’) of each model is stored in one or more respective table rows.” (See para [0096])).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Turco into the teachings of Cella because that would have been beneficial as the analytical SQL routines can be performed within a relational DB context highly efficiently, in particular if the input data is provided in table form (with appropriate indexation). Thus, also very large molecule libraries and a large number of predictive models can be tested highly efficiently. Analytical SQL routines performed within a relational DB context are much faster than program routines of higher-level application programs. Moreover, it is not necessary to export data from DB to the application context as suggested by Turco (See para [0053]).
Regarding claim 18:
The rejection of base claim 16 is incorporated. All the limitations of this claim have been noted in the rejection of claim 8 and is therefore rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Som et al. (Pub. No.: US 2025/0124352) discloses techniques for selecting prompt demonstrations to prompt a machine learning model to perform a task such as statement classification or statement generation. The computing system may generate embeddings for each sample statement of reference data. For example, the computing system may generate an embedding for a sample statement that characterizes content of the sample statement in a high dimensional vector space. The computing system may store the embeddings for the sample statements of the reference data to use for generating an improved prompt for classifying a query or target statement and/or generating a paraphrased statement for the query or target statement.
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/HANH THI-MINH BUI/Primary Examiner, Art Unit 2192 September 22nd, 2026
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