DETAILED 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 .
Allowable Subject Matter
Claims 7 and 8 are allowable over the prior art but remain rejected under 35 USC 101. If these claims overcome the 101 rejection, they would be allowable only if rewritten in independent form to include all the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-20) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Certain methods of organizing human activity), as set forth in exemplary claim 1, are: selecting a particular automated assistant service of a set of predefined automated assistant services; …generating a trigger component defining an automation initiation criteria; and in response to a third user input to the automation generation user interface, generating an automation component to be executed in response to the automation initiation criteria being satisfied, the automation component specifying the particular automated assistant service and defining a natural language command text string to be provided to the particular automated assistant service in response to the automation component being executed. Claim 9: in response to an event within the collaboration platform satisfying an automation initiation criteria of the automation rule, initiating execution of the automation rule…the first automation component causing a first automated assistant service to generate a first prompt using a natural language command text string defined by the first automation component …the second automation component generating content within the collaboration platform, the content generated using the generative response produced by the first automated assistant service; Claim 15: generating a trigger component defining an automation initiation criteria for the automation rule; …generating an automation component to be executed in response to the automation initiation criteria being satisfied, the automation component specifying: a particular automated assistant service of a set of automated assistant services, the particular automated assistant service …and a natural language command to be inserted into the prompt used by the particular automated assistant service…;
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: Claim 1: A computer-implemented method for generating an automation rule including an automated assistant service within a collaboration platform… at a frontend of the collaboration platform, causing display of a generative interface panel having an input region; in response to a natural language user input provided to the input region… causing the particular automated assistant service to generate a prompt comprising: predetermined query prompt text associated with the particular automated assistant service; and at least a portion of the natural language user input; causing display of a generative response in the generative interface panel, the generative response produced by a generative output engine in response to providing the prompt to the generative output engine; in response to a first user input to the frontend of the collaboration platform, initiating an automation generation user interface, the automation generation user interface configured to generate the automation rule to be executed by the collaboration platform; in response to a second user input to the automation generation user interface…; Claim 9: A computer-implemented method for executing an automation rule within a collaboration platform, the method comprising: causing display of a frontend of the collaboration platform… execution of the automation rule comprising: causing execution of a first automation component of the automation rule… and providing the first prompt to a generative output engine, the generative output engine configured to produce a generative response in response to the prompt; and causing execution of a second automation component of the automation rule… and causing display of a content item managed by the collaboration platform, the content item including the content generated by the second automation component Claim 15: in response to a first user input to a frontend of the collaboration platform, causing display of an automation generation user interface;in response to a second user input to the automation generation user interface… and in response to a third user input to the automation generation user interface… configured to provide a prompt to a generative output engine and return a generative response produced by the generative output engine…, the prompt causing a respective generative response to be produced in response to execution of the automation component.
However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: Claim 1: A computer-implemented method for generating an automation rule including an automated assistant service within a collaboration platform… at a frontend of the collaboration platform, causing display of a generative interface panel having an input region; in response to a natural language user input provided to the input region… causing the particular automated assistant service to generate a prompt comprising: predetermined query prompt text associated with the particular automated assistant service; and at least a portion of the natural language user input; causing display of a generative response in the generative interface panel, the generative response produced by a generative output engine in response to providing the prompt to the generative output engine; in response to a first user input to the frontend of the collaboration platform, initiating an automation generation user interface, the automation generation user interface configured to generate the automation rule to be executed by the collaboration platform; in response to a second user input to the automation generation user interface…; Claim 9: A computer-implemented method for executing an automation rule within a collaboration platform, the method comprising: causing display of a frontend of the collaboration platform… execution of the automation rule comprising: causing execution of a first automation component of the automation rule… and providing the first prompt to a generative output engine, the generative output engine configured to produce a generative response in response to the prompt; and causing execution of a second automation component of the automation rule… and causing display of a content item managed by the collaboration platform, the content item including the content generated by the second automation component Claim 15: in response to a first user input to a frontend of the collaboration platform, causing display of an automation generation user interface;in response to a second user input to the automation generation user interface… and in response to a third user input to the automation generation user interface… configured to provide a prompt to a generative output engine and return a generative response produced by the generative output engine…, the prompt causing a respective generative
for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0080]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-8, 10-14, and 16-20) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-8 “the prompt is a first prompt;the particular automated assistant service is configured to provide a second prompt to the generative output engine in response to the automation component being executed; and the second prompt includes the predetermined query prompt text used in the first prompt, and the natural language command text string; the automation component is a first automation component;the first automation component is configured to produce a first generative response in response to the second prompt being provided to the generative output engine; andin response to a fourth user input to the automation generation user interface, generating a second automation component to be executed in response to the first automation component completing execution, the second automation component configured to receive the first generative response produced by the first automation component; the particular automated assistant service is a first automated assistant service;the first generative response received comprises a natural language text string;the second automation component specifies a second automated assistant service;the second automated assistant service is configured to generate a structured data object based on the natural language text string of the first generative response; andthe second automated assistant service is configured to generate the structured data object by generating a third prompt including predetermined prompt text and the natural language text string, and providing the third prompt to the generative output engine to receive a second generative output including the structured data object; in response to a fifth user input to the automation generation user interface, generating a third automation component to be executed in response to the second automation component completing execution, the third automation component configured to receive the structured data object produced by the third automation component;the third automation component specifies a third automated assistant service; andthe third automated assistant service is configured to produce a third generative output by providing the structured data object produced by the second automated assistant service to the generative output engine; the particular automated assistant service is configured to generate text content; and execution of the automation rule causes the text content to be inserted into a content item of the collaboration platform; the content item is an electronic page of the collaboration platform;the text content is inserted as a comment to the electronic page; andthe collaboration platform causes display of the comment with an author attribution associated with the particular automated assistant service; wherein execution of the automation rule causes the particular automated assistant service to have a permissions role that corresponds to a user permissions role of a user that generated the automation rule”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (10-14and 16-20) recite the similar claim language for performing the method of claims 2-8. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, 9-10, 13, 15-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20250173652 (hereinafter “Rash”) et al., in view of U.S. PGPub 20240202582 to (hereinafter “Schill”) et al.
As per claim 1, Rash teaches A computer-implemented method for generating an automation rule including an automated assistant service within a collaboration platform, the method comprising: (see, Rash 0250)
in response to a first user input to the frontend of the collaboration platform, initiating an automation generation user interface, the automation generation user interface configured to generate the automation rule to be executed by the collaboration platform;Rash 0114: “FIG. 3C provides visual examples of distinct instants within the workflow configuration phase, where the integration of multiple GUI components alongside a canvas contributes to the facilitation of the construction process. In the uppermost panel of FIG. 3C, a user initiates the configuration phase by introducing a first block 320 and opting for a trigger through a trigger drop-down menu 322. As soon as the trigger is chosen, the descriptive label of block 320 undergoes modification to mirror the selected trigger, as portrayed in the second panel of FIG. 3C.”
in response to a second user input to the automation generation user interface, generating a trigger component defining an automation initiation criteria; Rash 0252: “wherein the AI block is powered by an AI agent, the AI agent may be selectable from a plurality of available AI agents. The AI agent powering the AI block may consist of processes or tools such as those that perform machine learning, deep learning, computer vision, natural language processing, or a combination of different AI techniques. The selection of the AI agent may be performed in different ways, including during the configuration or construction phase of the workflow with interaction on the canvas. In some embodiments, the selection of the AI agent from a plurality of available AI agents may be performed via the AI block. When accessing the editing interface of the AI block, the user may be presented with various GUI components like checkboxes or dropdown menus to choose the AI agent powering the block. The plurality of available AI agents may include AI agents who specialize in different types of operations. The workflow management platform, where the workflow including the AI block is designed and executed, may pre-validate or pre-approve the plurality of available AI agents. This pre-validation process may involve compliance checks to ensure the AI agents comply with relevant regulations governing the platform. In some embodiments, the process may further include determining a preselection of pertinent AI agents to be presented during the AI agent selection process. This preselection aims to assist users in making their selections by suggesting pertinent AI agents. For instance, when the user is choosing the AI agent through a dropdown menu, a preselection of suggested AI agents may be displayed at the beginning of the dropdown menu. This preselection may be based on an analysis of the context of the AI block performed by the workflow management platform, for example by using a predetermined AI agent dedicated to this task. In the case of workflow diagram 1300, an AI agent specifically designed for document pattern recognition may be suggested due to its relevance to the task at hand.”
and in response to a third user input to the automation generation user interface, generating an automation component to be executed in response to the automation initiation criteria being satisfied, the automation component specifying the particular automated assistant service and defining a natural language command text string to be provided to the particular automated assistant service in response to the automation component being executed;0260-0262: “Prompt may be defined during the configuration process of the AI block by a user, via an editing interface of the AI block for example… FIG. 13, the prompt of AI block 1302-3 may encompass the order form, which is the output of block 1302-2, as well as some information for the internal order database such as the order ID or the name of the person in the sales team that have created the new item. Alongside these items, specific instructions such as “Analyze order form to extract relevant order details” may be included within the prompt. To achieve a more accurate and desirable outcome, the sales representative configuring AI block 1302-3 may choose to provide a more specific prompt. This could involve explicitly listing the desired order details, such as the customer's name and information, the quantity of the product ordered, and the type of product ordered. By supplying these specific instructions, the sales representative aims to enhance the precision and relevance of the AI block's response.”
Rash may not explicitly teach the following. However, Schill teaches:
at a frontend of the collaboration platform, causing display of a generative interface panel having an input region;Schill 0039: “computing device 104 may include a user interface that is part of an application (e.g., application 116) or a plurality of applications (e.g., as a shared framework or as functionality that is provided by an operating system of computing device 104). In such an example, natural language input may be provided via the user interface (e.g., as text input and/or as voice input), which may be processed according to aspects described herein and used to generate a skill chain accordingly. In examples, a skill of the skill chain may interact with one or more command interfaces, each of which may be associated with an application (e.g., application 116) and/or an operating system of computing device 104, among other examples. For example, the operating system may provide a command interface via which interactions may be performed, for example through an accessibility API and/or an extensibility API.”
in response to a natural language user input provided to the input region, selecting a particular automated assistant service of a set of predefined automated assistant services;Schill 0024: “a skill library stores model and/or programmatic skills, from which a set of skills may be identified and used to generate a skill chain accordingly (e.g., thereby performing a set of associated ML model evaluations). As an example, a chain orchestrator may extract an intent from a given input (e.g., from a user and/or an application), which may be mapped to one or more skills of the skill library, thereby generating a chain model and/or programmatic skills with which to process the input. In examples, new skills are added to the skill library (e.g., by an application or by a user or developer), such that they may be dynamically identified and used as part of a skill chain with which to process a given input according to aspects described herein.”
causing the particular automated assistant service to generate a prompt comprising: predetermined query prompt text associated with the particular automated assistant service; and at least a portion of the natural language user input;Schill 0074: “Flow progresses to operation 406, where a prompt is generated. In examples, an indication of a model skill (e.g., corresponding to a prompt template) is received as part of a received request (as noted above with respect to operation 402). In some instances, the prompt template may be obtained based on an association with the model skill in a skill library (e.g., skill library 112 and/or 120 in FIG. 1, and/or skill library 212 in FIG. 2). The prompt template is processed to incorporate at least a part of the obtained input and, in some examples, the obtained context. For example, one or more fields, regions, or other parts of the prompt template may be replaced or otherwise populated with such aspects, thereby generating a prompt with which model output may be generated for a given model skill.”
causing display of a generative response in the generative interface panel, the generative response produced by a generative output engine in response to providing the prompt to the generative output engine;Schill 0070-0076: “Eventually, method 300 arrives at operation 316, where an indication of the generated output is provided. For example, the indication may be provided to a client computing device, as may be the case in instances where aspects of method 300 are performed by a chain orchestrator of the machine learning platform. Additionally, or alternatively, the indication may be provided by a multi-stage machine learning framework of the client computing device. For example, the indication is provided to an application (e.g., application 116 in FIG. 1) for subsequent processing. In some instances, an indication of at least a part of the generated output is provided to a user of the computing device. As noted above, the resulting output may include any of a variety of content, including, but not limited to, natural language output, speech and/or audio output, image output, video output, and/or programmatic output. Method 300 terminates at operation 316… Flow progresses to operation 410, where model output is generated. In examples, operation 410 comprises processing the prompt that was generated at operation 406 according to the ML model that was determined at operation 408. Aspects of an example ML model that may be used to perform such processing are described below with respect to FIGS. 5A-5B.” Also, see 0039
Rash and Schill are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash with the aforementioned teachings from Schill with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Schill 0076].
As per claim 2, Rash and Schill teach all the limitations of claim 1.
In addition, Rash teaches:
the prompt is a first prompt; the particular automated assistant service is configured to provide a second prompt to the generative output engine in response to the automation component being executed; 0260: “a prompt may refer to any sort of input, command, or query directed to the AI block or AI agent, with the intention of eliciting a desired response or outcome, such as generating specific content or providing an answer. Prompts may take the form of questions, commands, or instructions that guide the AI agent in producing the desired output. The quality of the prompt may play a valuable role in obtaining a desired response with a sufficient level of accuracy and quality. A well-crafted prompt increases the likelihood of receiving a responsive output of high quality.”
Rash may not explicitly teach the following. However, Schill teaches:
and the second prompt includes the predetermined query prompt text used in the first prompt, and the natural language command text string; 0074: Flow progresses to operation 406, where a prompt is generated. In examples, an indication of a model skill (e.g., corresponding to a prompt template) is received as part of a received request (as noted above with respect to operation 402). In some instances, the prompt template may be obtained based on an association with the model skill in a skill library (e.g., skill library 112 and/or 120 in FIG. 1, and/or skill library 212 in FIG. 2). The prompt template is processed to incorporate at least a part of the obtained input and, in some examples, the obtained context. For example, one or more fields, regions, or other parts of the prompt template may be replaced or otherwise populated with such aspects, thereby generating a prompt with which model output may be generated for a given model skill.
As per claim 3, Rash and Schill teach all the limitations of claim 2.
In addition, Rash teaches:
the automation component is a first automation component; the first automation component is configured to produce a first generative response in response to the second prompt being provided to the generative output engine; and in response to a fourth user input to the automation generation user interface, generating a second automation component to be executed in response to the first automation component completing execution, the second automation component configured to receive the first generative response produced by the first automation component; 0258: “Some disclosed embodiments may involve providing an output from the AI block, wherein a prompt of the AI block includes data from the source of dynamic data. “Providing an output from the AI block” refers to generating a result or response using the AI mechanisms associated with the AI block. For example, the output may represent the outcome or information derived from the AI block's computations or actions. The output generation is represented by way of example, in FIG. 12 at step 1208. The output may take various forms depending on the specific AI block and its purpose within the workflow. It may include textual information, numerical data, visual representations, or any other format suitable for conveying the AI block's findings or recommendations. The purpose of providing this output is to communicate the insights, predictions, or actions determined by the AI block to the user or downstream components of the workflow. It serves as the tangible outcome of the AI block's computation, enabling users to make informed decisions or trigger subsequent steps based on the generated output. The output generated by the AI block holds significant value within the workflow and may be effectively utilized in conjunction with other components, including global variables and internal/external resources. In some embodiments, the output of the AI block may be directly displayed on the canvas after execution of the AI block.” 0264: “Some disclosed embodiments may involve enabling placement of one or more additional workflow blocks at a position downstream of the AI block in a same branch as the AI block. “Downstream” refers to a later position in the workflow. A branch refers to a specific path or sequence of activities that diverges from the main flow. A same branch is that specific path in which the subject workflow block is located. Enabling placement refers to the software or the systems ability to permit the additional blocks to be placed. The additional blocks may be granted the capability to access the source of dynamic data, as well as access the output generated by the AI block. This access is facilitated by the downstream position of the additional blocks in relation to the AI block. By allowing the placement of subsequent blocks in this manner, a sequential flow is established where the output of the AI block may become an input for the subsequent additional blocks. This may enable a seamless transfer of data and information within the workflow, ensuring that the output generated by the AI block may be further utilized and processed by subsequent blocks. The downstream positioning of these additional blocks in the same branch may enable them to seamlessly access the source of dynamic data as well. This integration may ensure a cohesive flow of data and operations, where each block within the workflow may leverage the outputs and inputs of the preceding and succeeding blocks, respectively. Placement of the one or more additional workflow blocks may be performed in accordance with any of the methods described above in relation to the plurality of blocks or the AI blocks. Particularly, it is to be appreciated that any of the one or more additional workflow blocks may be a preconstructed block selected from a template library.”
As per claim 6, Rash and Schill teach all the limitations of claim 1.
In addition, Rash teaches:
wherein: the particular automated assistant service is configured to generate text content; and execution of the automation rule causes the text content to be inserted into a content item of the collaboration platform; 0254-0258: “the AI block may be enabled to modify data in the source of dynamic data. Modifying data within the source of dynamic data may include various types of data manipulation mechanisms such as editing, adding, suppressing, merging, or any other form of data manipulation. By having the ability to modify data within the source of dynamic data, the AI block may update the source of dynamic data and actively contribute to the data management process. For example, with reference to FIG. 13, AI block 1302-3 may perform data modifications within the internal order database. It may edit a generic order ID initially provided with a more specific order ID after fetching the name of the customer, or it may populate the database with the relevant order details extracted from the order form, ensuring that the database is updated with the most accurate and up-to-date information… It may include textual information, numerical data, visual representations, or any other format suitable for conveying the AI block's findings or recommendations. The purpose of providing this output is to communicate the insights, predictions, or actions determined by the AI block to the user or downstream components of the workflow. It serves as the tangible outcome of the AI block's computation, enabling users to make informed decisions or trigger subsequent steps based on the generated output.”
As per claim 9, Rash teaches a computer-implemented method for executing an automation rule within a collaboration platform, the method comprising: (see, Rash 0248)
causing display of a frontend of the collaboration platform; 0245: “Some disclosed embodiments involve displaying a canvas for containing and interconnecting workflow blocks. Displaying a canvas, refers to rendering a visual surface.”
in response to an event within the collaboration platform satisfying an automation initiation criteria of the automation rule, initiating execution of the automation rule, execution of the automation rule comprising:0248: “In accordance with some disclosed embodiments, the plurality of blocks in the workflow diagram may include at least one trigger block and at least one action block. As mentioned earlier, a trigger block functions substantially as a trigger for subsequent downstream workflow blocks, while an action block performs a specific action. For instance, referring to FIG. 13, block 1302-1 acts as a trigger by responding to the creation of a new order item in the internal order database. It initiates the trigger/action flow for the subsequent downstream blocks in workflow 1300. On the other hand, block 1302-2 represents an action block that performs the action of fetching the order details associated with the newly created order.”
causing execution of a first automation component of the automation rule, the first automation component causing a first automated assistant service to generate a first prompt using a natural language command text string defined by the first automation component…;
0262: “To illustrate, in reference to FIG. 13, the prompt of AI block 1302-3 may encompass the order form, which is the output of block 1302-2, as well as some information for the internal order database such as the order ID or the name of the person in the sales team that have created the new item. Alongside these items, specific instructions such as “Analyze order form to extract relevant order details” may be included within the prompt. To achieve a more accurate and desirable outcome, the sales representative configuring AI block 1302-3 may choose to provide a more specific prompt. This could involve explicitly listing the desired order details, such as the customer's name and information, the quantity of the product ordered, and the type of product ordered. By supplying these specific instructions, the sales representative aims to enhance the precision and relevance of the AI block's response.”
and causing execution of a second automation component of the automation rule, the second automation component generating content within the collaboration platform, the content generated using the generative response produced by the first automated assistant service; 0254-0264: “ Some disclosed embodiments may involve enabling placement of one or more additional workflow blocks at a position downstream of the AI block in a same branch as the AI block. “Downstream” refers to a later position in the workflow. A branch refers to a specific path or sequence of activities that diverges from the main flow. A same branch is that specific path in which the subject workflow block is located. Enabling placement refers to the software or the systems ability to permit the additional blocks to be placed. The additional blocks may be granted the capability to access the source of dynamic data, as well as access the output generated by the AI block. This access is facilitated by the downstream position of the additional blocks in relation to the AI block. By allowing the placement of subsequent blocks in this manner, a sequential flow is established where the output of the AI block may become an input for the subsequent additional blocks. This may enable a seamless transfer of data and information within the workflow, ensuring that the output generated by the AI block may be further utilized and processed by subsequent blocks. The downstream positioning of these additional blocks in the same branch may enable them to seamlessly access the source of dynamic data as well. This integration may ensure a cohesive flow of data and operations, where each block within the workflow may leverage the outputs and inputs of the preceding and succeeding blocks, respectively. Placement of the one or more additional workflow blocks may be performed in accordance with any of the methods described above in relation to the plurality of blocks or the AI blocks. Particularly, it is to be appreciated that any of the one or more additional workflow blocks may be a preconstructed block selected from a template library.”
and causing display of a content item managed by the collaboration platform, the content item including the content generated by the second automation component;Rash 0258: “Some disclosed embodiments may involve providing an output from the AI block, wherein a prompt of the AI block includes data from the source of dynamic data. “Providing an output from the AI block” refers to generating a result or response using the AI mechanisms associated with the AI block. For example, the output may represent the outcome or information derived from the AI block's computations or actions. The output generation is represented by way of example, in FIG. 12 at step 1208. The output may take various forms depending on the specific AI block and its purpose within the workflow. It may include textual information, numerical data, visual representations, or any other format suitable for conveying the AI block's findings or recommendations. The purpose of providing this output is to communicate the insights, predictions, or actions determined by the AI block to the user or downstream components of the workflow. It serves as the tangible outcome of the AI block's computation, enabling users to make informed decisions or trigger subsequent steps based on the generated output. The output generated by the AI block holds significant value within the workflow and may be effectively utilized in conjunction with other components, including global variables and internal/external resources. In some embodiments, the output of the AI block may be directly displayed on the canvas after execution of the AI block.”
Rash may not explicitly teach the following. However, Schill teaches:
and providing the first prompt to a generative output engine, the generative output engine configured to produce a generative response in response to the prompt;
0076: “Flow progresses to operation 410, where model output is generated. In examples, operation 410 comprises processing the prompt that was generated at operation 406 according to the ML model that was determined at operation 408. Aspects of an example ML model that may be used to perform such processing are described below with respect to FIGS. 5A-5B.”
Rash and Schill are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash with the aforementioned teachings from Schill with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Schill 0076].
Claim 10 recites substantially the same subject matter as claim 1. Thus, the same art and rationale applied to claim 1 are applied to claim 10.
As per claim 13, Rash and Schill teach all the limitations of claim 9.
In addition, Rash teaches:
wherein the automation rule includes a third automation component that is configured to be executed subsequent to the first automation component and prior to the second automation component; 0264: “Some disclosed embodiments may involve enabling placement of one or more additional workflow blocks at a position downstream of the AI block in a same branch as the AI block. “Downstream” refers to a later position in the workflow. A branch refers to a specific path or sequence of activities that diverges from the main flow. A same branch is that specific path in which the subject workflow block is located. Enabling placement refers to the software or the systems ability to permit the additional blocks to be placed. The additional blocks may be granted the capability to access the source of dynamic data, as well as access the output generated by the AI block. This access is facilitated by the downstream position of the additional blocks in relation to the AI block. By allowing the placement of subsequent blocks in this manner, a sequential flow is established where the output of the AI block may become an input for the subsequent additional blocks. This may enable a seamless transfer of data and information within the workflow, ensuring that the output generated by the AI block may be further utilized and processed by subsequent blocks. The downstream positioning of these additional blocks in the same branch may enable them to seamlessly access the source of dynamic data as well. This integration may ensure a cohesive flow of data and operations, where each block within the workflow may leverage the outputs and inputs of the preceding and succeeding blocks, respectively. Placement of the one or more additional workflow blocks may be performed in accordance with any of the methods described above in relation to the plurality of blocks or the AI blocks. Particularly, it is to be appreciated that any of the one or more additional workflow blocks may be a preconstructed block selected from a template library.
Claim 15 recites substantially the same subject matter as claim 1. Thus, the same art and rationale applied to claim 1 are applied to claim 15.
As per claim 16, Rash and Schill teach all the limitations of claim 15.
In addition, Rash teaches:
the automation generation user interface comprises:a proposed automation flow region displaying a sequence of automation components to be executed by the automation rule; anda control region displaying a respective set of input controls for defining a respective component of the sequence of automation components; 0245-0252: “Some disclosed embodiments involve displaying a canvas for containing and interconnecting workflow blocks… wherein the AI block is powered by an AI agent, the AI agent may be selectable from a plurality of available AI agents. The AI agent powering the AI block may consist of processes or tools such as those that perform machine learning, deep learning, computer vision, natural language processing, or a combination of different AI techniques. The selection of the AI agent may be performed in different ways, including during the configuration or construction phase of the workflow with interaction on the canvas. In some embodiments, the selection of the AI agent from a plurality of available AI agents may be performed via the AI block. When accessing the editing interface of the AI block, the user may be presented with various GUI components like checkboxes or dropdown menus to choose the AI agent powering the block. The plurality of available AI agents may include AI agents who specialize in different types of operations. The workflow management platform, where the workflow including the AI block is designed and executed, may pre-validate or pre-approve the plurality of available AI agents. This pre-validation process may involve compliance checks to ensure the AI agents comply with relevant regulations governing the platform. In some embodiments, the process may further include determining a preselection of pertinent AI agents to be presented during the AI agent selection process. This preselection aims to assist users in making their selections by suggesting pertinent AI agents. For instance, when the user is choosing the AI agent through a dropdown menu, a preselection of suggested AI agents may be displayed at the beginning of the dropdown menu. This preselection may be based on an analysis of the context of the AI block performed by the workflow management platform, for example by using a predetermined AI agent dedicated to this task. In the case of workflow diagram 1300, an AI agent specifically designed for document pattern recognition may be suggested due to its relevance to the task at hand.”
As per claim 17, Rash and Schill teach all the limitations of claim 15.
In addition, Rash teaches:
the automation generation user interface comprises a set of input controls for defining the automation component, the set of input controls comprising: a first control for selecting a particular automated assistant service of the set of automated assistant services; and an editor region configured to receive the natural language command; 0252-0260: “When accessing the editing interface of the AI block, the user may be presented with various GUI components like checkboxes or dropdown menus to choose the AI agent powering the block. The plurality of available AI agents may include AI agents who specialize in different types of operations… As discussed earlier, non-compliance-related guidance or technical instructions provided to the AI agent may be included in prompts thereby influencing the behavior of the AI agent. These additional instructions may be embedded in the free language prompt, included as settings messages sent to the AI agent API before making the request, or hardcoded into the AI agent's API itself. Prompt may be defined during the configuration process of the AI block by a user, via an editing interface of the AI block for example.”
As per claim 19, Rash and Schill teach all the limitations of claim 15.
In addition, Schill teaches:
causing display of a generative interface in the frontend of the collaboration platform;in response to a natural language user input provided to the generative interface, selecting the particular automated assistant service from the set of automated assistant services based on an analysis of the natural language user input;causing the particular automated assistant service to generate a second prompt comprising:predetermined query prompt text associated with the particular automated assistant service; andat least a portion of the natural language user input; andcausing display of a second generative response in the generative interface, the second generative response produced by the generative output engine in response to providing the second prompt to the generative output engine; Schill 0039: “computing device 104 may include a user interface that is part of an application (e.g., application 116) or a plurality of applications (e.g., as a shared framework or as functionality that is provided by an operating system of computing device 104). In such an example, natural language input may be provided via the user interface (e.g., as text input and/or as voice input), which may be processed according to aspects described herein and used to generate a skill chain accordingly. In examples, a skill of the skill chain may interact with one or more command interfaces, each of which may be associated with an application (e.g., application 116) and/or an operating system of computing device 104, among other examples. For example, the operating system may provide a command interface via which interactions may be performed, for example through an accessibility API and/or an extensibility API… 0070-0076: “Eventually, method 300 arrives at operation 316, where an indication of the generated output is provided. For example, the indication may be provided to a client computing device, as may be the case in instances where aspects of method 300 are performed by a chain orchestrator of the machine learning platform. Additionally, or alternatively, the indication may be provided by a multi-stage machine learning framework of the client computing device. For example, the indication is provided to an application (e.g., application 116 in FIG. 1) for subsequent processing. In some instances, an indication of at least a part of the generated output is provided to a user of the computing device. As noted above, the resulting output may include any of a variety of content, including, but not limited to, natural language output, speech and/or audio output, image output, video output, and/or programmatic output. Method 300 terminates at operation 316… Flow progresses to operation 410, where model output is generated. In examples, operation 410 comprises processing the prompt that was generated at operation 406 according to the ML model that was determined at operation 408. Aspects of an example ML model that may be used to perform such processing are described below with respect to FIGS. 5A-5B.”
Rash and Schill are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash with the aforementioned teachings from Schill with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Schill 0076].
As per claim 20, Rash and Schill teach all the limitations of claim 19.
In addition, Schill teaches:
wherein: the analysis of the natural language user input includes an action intent analysis and determines an action intent; and the action intent corresponds to a predicted action associated with the natural language user input; 0024-0040: “a skill library stores model and/or programmatic skills, from which a set of skills may be identified and used to generate a skill chain accordingly (e.g., thereby performing a set of associated ML model evaluations). As an example, a chain orchestrator may extract an intent from a given input (e.g., from a user and/or an application), which may be mapped to one or more skills of the skill library, thereby generating a chain model and/or programmatic skills with which to process the input. In examples, new skills are added to the skill library (e.g., by an application or by a user or developer), such that they may be dynamically identified and used as part of a skill chain with which to process a given input according to aspects described herein… FIG. 2 illustrates an overview of an example conceptual diagram 200 for processing a user input to generate model output using chained machine learning models according to according to aspects described herein. As illustrated, diagram 200 processes user input 202 according to a set of models (e.g., ML models 204 and 206, as orchestrated by chain orchestrator 203) to generate model output 208. For example, user input 202 may be received from a computing device, such as computing device 104 in FIG. 1. Aspects of chain orchestrator 203 may be similar to those discussed above with respect to chain orchestrator 108 and are therefore not necessarily redescribed below in detail.”
Rash and Schill are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash with the aforementioned teachings from Schill with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Schill 0076].
Claims 4-5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20250173652 (hereinafter “Rash”) et al., in view of U.S. PGPub 20240202582 to (hereinafter “Schill”) et al., in further view of U.S. PGPub 20250094703 (hereinafter “Malak”) et al.
As per claim 4, Rash and Schill teach all the limitations of claim 3.
In addition, Rash teaches:
the second automation component specifies a second automated assistant service; Rash 0252: “wherein the AI block is powered by an AI agent, the AI agent may be selectable from a plurality of available AI agents. The AI agent powering the AI block may consist of processes or tools such as those that perform machine learning, deep learning, computer vision, natural language processing, or a combination of different AI techniques. The selection of the AI agent may be performed in different ways, including during the configuration or construction phase of the workflow with interaction on the canvas. In some embodiments, the selection of the AI agent from a plurality of available AI agents may be performed via the AI block. When accessing the editing interface of the AI block, the user may be presented with various GUI components like checkboxes or dropdown menus to choose the AI agent powering the block. The plurality of available AI agents may include AI agents who specialize in different types of operations. The workflow management platform, where the workflow including the AI block is designed and executed, may pre-validate or pre-approve the plurality of available AI agents. This pre-validation process may involve compliance checks to ensure the AI agents comply with relevant regulations governing the platform. In some embodiments, the process may further include determining a preselection of pertinent AI agents to be presented during the AI agent selection process. This preselection aims to assist users in making their selections by suggesting pertinent AI agents. For instance, when the user is choosing the AI agent through a dropdown menu, a preselection of suggested AI agents may be displayed at the beginning of the dropdown menu. This preselection may be based on an analysis of the context of the AI block performed by the workflow management platform, for example by using a predetermined AI agent dedicated to this task. In the case of workflow diagram 1300, an AI agent specifically designed for document pattern recognition may be suggested due to its relevance to the task at hand.”
Rash and Schill may not explicitly teach the following. However, Malak teaches:
wherein: the particular automated assistant service is a first automated assistant service; the first generative response received comprises a natural language text string; 0021: “The prompt engine may generate other prompts which task the LLM service with making other selections relating to the visualization, such as creating a caption for the visualization to be displayed alongside. For example, the LLM service may be tasked with creating the caption in a natural language format which describes what the visualization shows, such as the type of visualization (e.g., bar chart, scatter plot, pie chart), the data that is included (according to the column names), and the manner in which the data is organized for display (such as in stacked bar chart where the data is categorized according to a third variable).”
the second automated assistant service is configured to generate a structured data object based on the natural language text string of the first generative response; 0044: “The computing device populates a JSON object with the response from the LLM service (step 205). With the prompt created, in an implementation, the computing device submits the prompt to the LLM service and receives a response from the LLM service including output generated according to the prompt. The computing device populates a JSON object with the output from the LLM service. For example, where the output includes the column selections by name in a JSON format, the computing device populates a column name attribute of the JSON object with the output.”
and the second automated assistant service is configured to generate the structured data object by generating a third prompt including predetermined prompt text and the natural language text string, and providing the third prompt to the generative output engine to receive a second generative output including the structured data object; 0067: “Next, the analytics application generates a second prompt for the LLM, this time based on prompt template 515. Prompt template 515 tasks the LLM with choosing a visualization type or chart type for the visualization based again on the user input 511 and including the column names specified in output 514. Notably, prompt template 515 includes a list of possible chart types and for each chart type, prompt template 515 includes an object identifier with the LLM is to return in a JSON format.”
Rash, Schill, and Malak are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash and Schill with the aforementioned teachings from Malak with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Malak 0044].
As per claim 5, Rash, Schill, and Malak teach teaches all the limitations of claim 4.
In addition, Rash teaches:
in response to a fifth user input to the automation generation user interface, generating a third automation component to be executed in response to the second automation component completing execution, the third automation component configured to receive the structured data object produced by the third automation component; the third automation component specifies a third automated assistant service; 0252-0264: “wherein the AI block is powered by an AI agent, the AI agent may be selectable from a plurality of available AI agents. The AI agent powering the AI block may consist of processes or tools such as those that perform machine learning, deep learning, computer vision, natural language processing, or a combination of different AI techniques. The selection of the AI agent may be performed in different ways, including during the configuration or construction phase of the workflow with interaction on the canvas. In some embodiments, the selection of the AI agent from a plurality of available AI agents may be performed via the AI block. When accessing the editing interface of the AI block, the user may be presented with various GUI components like checkboxes or dropdown menus to choose the AI agent powering the block. The plurality of available AI agents may include AI agents who specialize in different types of operations. The workflow management platform, where the workflow including the AI block is designed and executed, may pre-validate or pre-approve the plurality of available AI agents. This pre-validation process may involve compliance checks to ensure the AI agents comply with relevant regulations governing the platform. In some embodiments, the process may further include determining a preselection of pertinent AI agents to be presented during the AI agent selection process. This preselection aims to assist users in making their selections by suggesting pertinent AI agents. For instance, when the user is choosing the AI agent through a dropdown menu, a preselection of suggested AI agents may be displayed at the beginning of the dropdown menu. This preselection may be based on an analysis of the context of the AI block performed by the workflow management platform, for example by using a predetermined AI agent dedicated to this task. In the case of workflow diagram 1300, an AI agent specifically designed for document pattern recognition may be suggested due to its relevance to the task at hand… Some disclosed embodiments may involve enabling placement of one or more additional workflow blocks at a position downstream of the AI block in a same branch as the AI block. “Downstream” refers to a later position in the workflow. A branch refers to a specific path or sequence of activities that diverges from the main flow. A same branch is that specific path in which the subject workflow block is located. Enabling placement refers to the software or the systems ability to permit the additional blocks to be placed. The additional blocks may be granted the capability to access the source of dynamic data, as well as access the output generated by the AI block. This access is facilitated by the downstream position of the additional blocks in relation to the AI block. By allowing the placement of subsequent blocks in this manner, a sequential flow is established where the output of the AI block may become an input for the subsequent additional blocks. This may enable a seamless transfer of data and information within the workflow, ensuring that the output generated by the AI block may be further utilized and processed by subsequent blocks. The downstream positioning of these additional blocks in the same branch may enable them to seamlessly access the source of dynamic data as well. This integration may ensure a cohesive flow of data and operations, where each block within the workflow may leverage the outputs and inputs of the preceding and succeeding blocks, respectively. Placement of the one or more additional workflow blocks may be performed in accordance with any of the methods described above in relation to the plurality of blocks or the AI blocks. Particularly, it is to be appreciated that any of the one or more additional workflow blocks may be a preconstructed block selected from a template library.”
Rash and Schill may not explicitly teach the following. However, Malak teaches:
and the third automated assistant service is configured to produce a third generative output by providing the structured data object produced by the second automated assistant service to the generative output engine; 0063: “ Analytics application service 320 receives the request and prompt engine 322 configures a new prompt, Prompt N+1, for LLM service 321. Prompt N+1 includes the second user input and tasks LLM service 321 with identifying one or more attributes of the visualization to be modified and one or more new values for the attributes. In some instances, Prompt N+1 includes a list of attributes of the JSON attributes and may also include the current values of those attributes. When LLM service 321 receives Prompt N+1, it generates Output N+1 and returns it to prompt engine 322. Upon receiving Output N+1, analytics application service 320 revises the JSON object to include the new or updated values from Output N+1. Analytics application service 320 sends the revised JSON object to visualization engine 324 to request a revised version visualization. Visualization engine 324 generates a revised version of the visualization and returns it to analytics application service 320. Analytics application service 320 renders the revised version of the visualization in user interface 311.”
Rash, Schill, and Malak are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash and Schill with the aforementioned teachings from Malak with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Malak 0063].
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20250173652 (hereinafter “Rash”) et al., in view of U.S. PGPub 20240202582 to (hereinafter “Schill”) et al., in further view of U.S. PGPub 20250005299 (hereinafter “Padman”) et al.
As per claim 18, Rash and Schill teach all the limitations of claim 17.
Rash and Schill may not explicitly teach the following. However, Padman teaches:
a second control for adjusting a temperature of the generative response produced by the generative output engine; anda third control for adjusting a tone of the generative response produced by the generative output engine;0062-0064: “In FIG. 11, the prompt configuration interface 1102 includes a template section 1106 illustrates instructions to be included in the prompt template. The resource manager at 1104 may be used to insert data references for dynamically retrieving data for inclusion in the prompt. Configuration parameters shown at 1106 include elements such as the style to be used when generating the novel text (e.g., academic), the tone to be used in generating the novel text (e.g., professional and informative), the intended audience of the novel text (e.g., customer), the length of the requested novel text (e.g., 100 words, 200 words, 400 words), the language in which to generate the novel text (e.g., English), an indicator as to whether to present a visible toxicity score in the results, the model provider (e.g., Open AI), and the model to use when generating the novel text… FIG. 16, FIG. 17, FIG. 18, and FIG. 19 illustrate user interfaces generated as part of a flow for selecting and configuring a model to generate the novel text. In FIG. 16, a model selection interface is shown at 1602. In FIG. 17, model connection parameters are shown at 1702. In FIG. 18, model inputs and output parameters are shown at 1802 and 1804. Model hyperparameters such as temperature are shown in the model hyperparameter configuration interface at 1902 in FIG. 19.”
Rash, Schill, and Padman are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash and Schill with the aforementioned teachings from Padman with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Padman 0064].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20250173652 (hereinafter “Rash”) et al., in view of U.S. PGPub 20240202582 to (hereinafter “Schill”) et al., in further view of U.S. PGPub 20240273309 (hereinafter “Heller”) et al.
As per claim 12, Rash and Schill teach all the limitations of claim 9.
Rash and Schill may not explicitly teach the following. However, Heller teaches:
wherein the first prompt includes an instruction to suppress an output if the generative output engine cannot provide one or more results responsive to the natural language command text string; 0177: “The purpose of this task is to compose an answer to each question. Follow these instructions: [0178] Base the answer only on the information contained in the document, and no extraneous information. If a direct answer cannot be derived explicitly from the document, do not answer. [0179] Answer completely, fully, and precisely. [0180] Interpret each question as asking to provide a comprehensive list of every item instead of only a few examples or notable instances. Never summarize or omit information from the document unless the question explicitly asks for that. [0181] Answer based on the full text, not just a portion of it. [0182] For each and every question, include verbatim quotes from the text (in quotation marks) in the answer. If the quote is altered in any way from the original text, use ellipsis, brackets, or [sic] for minor typos. [0183] Be exact in your answer. Check every letter. [0184] There is no limit on the length of your answer, and more is better [0185] Compose a full answer to each question; even if the answer is also contained in a response to another question, still include it in each answer… [0194] Only valid JSON; check to make sure it parses, and that quotes within quotes are escaped or turned to single quotes, and don't forget the ‘,’ delimiters. <|endofprompt|> [0195] Here is the JSON array and nothing else”
Rash, Schill, and Heller are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash and Schill with the aforementioned teachings from Heller with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Heller 0177].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20250173652 (hereinafter “Rash”) et al., in view of U.S. PGPub 20240202582 to (hereinafter “Schill”) et al., in further view of U.S. PGPub 20230101588 (hereinafter “Dasdan”) et al., and in further view of U.S. PGPub 20250384330 (hereinafter “Riscutia”) et al.
As per claim 11, Rash and Schill teach all the limitations of claim 9.
Rash and Schill may not explicitly teach the following. However, Dasdan teaches:
wherein: execution of the automation rule causes generation of content within the collaboration platform;the content is generated based on the generative response produced by the first automation component;the content generation is performed using a permissions profile associated with a user that created the automation rule; 0038-0041: “The permissions service 112 can enable which actions a given user may perform with different resources, documents or other information of the collaboration system 100. The permissions service can be used to manage user access to documents such as various spaces and/or pages. For example, a user may be allowed to create, edit, comment and structure documents associated with their own content space, but only be able to read and comment on documents associated created and/or managed by other users. In some cases, the permission service 112 gives different permissions to different users. For example, some user may be assigned administrator privileges, which may give them more access to documents, such as the ability to edit, delete, structure/organize, format, or otherwise manipulate documents for other users of the system. In some cases, the permissions service 112 may manage special permission for specific users and/or other services such as the automations engines that allows operations such as updating hierarchical structures of hosted documents, archiving documents, creating templates and so on, which can include broader system side privileges. Additionally or alternatively, the permissions granted to the automation may correspond to those granted to the user, which may help avoid a permissions breach through the automation system… The automation engine 118 can execute recommendations and apply rules to documents hosted by the collaboration system 100. The automation engine may interface with the collaboration platform API to update, create, archive, or otherwise make changes to documents and/or other content hosted by the collaboration platform. In some cases, the rules may be suggested and accepted by a user, manually input by the user, or otherwise derived by the collaboration system 100 as described herein. The automation engine 118 can implement rules in a variety of ways including scheduling rules to execute on documents and/or other content items at defined intervals, under conditions associated with a particular rule, or the like. In some cases, the automation engine can execute recommendations to subsets of objects hosted by the collaboration system 100, which may include defined subsets of documents, particular types of documents, documents meeting defined criteria, or the like, as described herein. The automation engine 118 can generate recommendation in response to a variety of triggers including user interactions with documents, acceptance of a proposed automation rule, in response to request to create new documents, and so on.”
Rash, Schill, and Dasdan are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash and Schill with the aforementioned teachings from Dasdan with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Dasdan 0041].
Rash, Schill, and Dasdan may not explicitly teach the following. However, Riscutia teaches:
and authorship of the content is attributed to the first automated assistant service; 0051: “the AI response editability management approach starts with a prompt and a read-only view of its response (as a card similar to an existing Bing AI response). The user can edit/re-run the prompt to re-generate the response. The AI response editability management approach converts the response into editable content upon a user request. For example, once the user chooses to convert the response, the AI response editability management approach translates the card into a collaboration application document (e.g., Loop® document) content with attribution (e.g., initially attributing all content to the AI chatbot). At a final stage, one or more users can edit the editable content in the Loop® document, while the AI response editability management approach removes the ability to regenerate the response from the prompt (which would cause the loss of the user edits).”
Rash, Schill, Dasdan, and Riscutia are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Rash, Schill, and Dasdan with the aforementioned teachings from Riscutia with a reasonable expectation of success, by adding steps that allow the software to display data with the motivation to more efficiently and accurately organize and analyze data [Riscutia 0041].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kulkarni; Rakesh Suresh. SYSTEM AND METHOD FOR FORECASTING IN THE PRESENCE OF MULTIPLE SEASONAL PATTERNS IN PRINT DEMAND, .U.S. PGPub 2011/0196718A system for forecasting an inventory level for a consumable in a print production environment may include a computing device and a computer-readable storage medium in communication with the computing device
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625
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