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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim 1 recites “facilitating the management of AI (artificial intelligence) agents, AI agent skills, and customer access..” is incomplete as being indefinite for failing to particularly point out and distinctly claim the subject matter.
The claim recites “for selectively enabling and/or disabling Al agent..” covers enable only, disable only or both. It is unclear whether on control must support both directions.
The claim recites “: identifying an AI agent and AI agent skills that the AI agent can utilize when interacting with different customers; identifying a plurality of customers that the Al agent is capable of interacting with..” do not say who identifies.
Claim 1 recites the limitation "AI agent skills" in “one or more of the AI agent skills..”. There is insufficient antecedent basis for this limitation in the claim.
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 an abstract idea without significantly more. The claim recites A method for facilitating the management of AI (artificial intelligence) agents, AI agent skills, and customer access to Al agents and AI agent skills, the method comprising: identifying an AI agent and AI agent skills that the AI agent can utilize when interacting with different customers; identifying a plurality of customers that the Al agent is capable of interacting with while utilizing one or more of the Al agent skills; generating an interface that identifies the plurality of customers and that is displayed with interactive permission controls for each identified customer, the interactive permission controls for each identified customer being configured to receive user input for selectively enabling and/or disabling Al agent interactions by the Al agent with a corresponding identified customer using the one or more Al agent skills. The claim under its broadest reasonable interpretation, recites certain methods of organizing human activity and mental processes. MPEP 2106.04(a)(2).
Organizing human activity. The claim recites a method for facilitating the management of AI (artificial intelligence) agents, AI agent skills, and customer access to Al agents and AI agent skills.
Mental processes. The claim recites “identifying an AI agent and AI agent skills that the AI agent can utilize when interacting with different customers; identifying a plurality of customers that the Al agent is capable of interacting with while utilizing one or more of the Al agent skills” can be performed in the human mind or with pen or paper (listing an assistant, listing its capabilities, listing the customers it can serve). MPEP 2106.04(a)(2)(III). Data structure is required to perform the identifying. If a claim limitation, under its broadest reasonable interpretation, is an action of certain methods of organizing human activity, then it falls within the “certain methods of organizing human activity and mental processes” grouping of abstract ideas. Accordingly, the claimed elements recite an abstract idea. This judicial exception is not integrated into a practical application.
The additional generating an interface that identifies the plurality of customers and that is displayed with interactive permission controls for each identified customer, the interactive permission controls for each identified customer being configured to receive user input for selectively enabling and/or disabling Al agent interactions by the Al agent with a corresponding identified customer using the one or more Al agent skills. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible.
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 (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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7 and 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. US Patent Application Publication No. 2018/0332141) in view of Machado et al. (US 2021/0141865) and further in view of Will, IV et al. (US 2024/0020478).
Regarding claim 1, Brown discloses a method for facilitating the management of AI (artificial intelligence) agents, AI agent skills, and customer access to Al agents and AI agent skills, the method comprising [see para. 0002; Virtual assistants (also known as “intelligent personal assistants”) are software applications that perform various tasks and services for users. Virtual assistants implemented using a variety of different computing devices and respond to a variety of commands from users. The various features of virtual assistants may be referred to as “skills” of the virtual assistant]:
identifying an AI agent and AI agent skills that the AI agent can utilize when interacting with different customers [see abstract, para. 0002; Users, such as software developers, deploy and remove software packages from different groups, and precisely define the members in any number of different deployment groups that access the deployed virtual assistant software. Virtual assistants implemented using a variety of different computing devices and respond to a variety of commands from users. The various features of virtual assistants may be referred to as “skills” of the virtual assistant; which corresponds to identify a virtual assistant and the skills that assistant use when interacting with members of different groups];
identifying a plurality of customers that the AI agent is capable of interacting with while utilizing one or more of the Al agent skills [see para. 0021, 0030 and figures 3-4; Different development groups may have the same, or different members, and there may be multiple instances of the same development group. For example, a developer might choose to deploy a first virtual assistant skill software package to a first instance of group for testing and deploy a second virtual assistant software package to a second instance of group. In this example, the first and second instances of the group include the same members, but a different software package is deployed to each instance; which corresponds to users can access the deployed virtual assistant software ];
generating an interface that identifies the plurality of customers and that is displayed with interactive permission controls for each identified customer, the interactive permission controls for each identified customer being configured to receive user input [see para. 0028, 0036 and figure 4; set of deployment groups , four groups are depicted: a “self” group, a “group” group, a “tenant” group, and a “product” group. The groups in this example are also arranged (from left to right) based on size, with the smallest number of members on the left (self group) and the largest on the right (product group). Developers, publishers, and other users (e.g., of the third party servers) responsible for the software application define the membership of the deployment groups in conjunction with submitting the software application for deployment and the deployment groups are arranged such that deployments start at the self group and migrated forward (i.e., to the right to groups), the system allows deployments from left to right in the set of groups but not right to left. Accordingly, a self-deployed skill move into the group, tenant, or product groups, while Group-deployed skills can move into the tenant or product groups, and tenant-deployed skills can move into the product group; which corresponds to an admin interface that identifies customers or group and provides interactive controls to enable or disable skill access for customers]; however, Brown fails to explicitly teach AI agent interactions by the AI agent with a corresponding identified customer using the one or more AI agent skills.
Machado discloses AI agent interactions by the AI agent with a corresponding identified customer using the one or more AI agent skills [see para. 0016, 0025, 0027; A multi-tenant system performs custom configuration of a tenant-specific chatbot. The chatbot processes and acts upon natural language requests to allow users to perform actions of the multi-tenant system. The chatbot uses machine learning based models that do not require requiring tenant-specific training. A chatbot is also referred to as a conversational agent, a dialog system, virtual assistant, or artificial intelligence (AI) assistant and the conversation engine determines whether additional information is needed to perform the permitted action and performs further conversation with the user to receive the additional information. After collecting the required information, the multi-tenant system performs the requested action; which corresponds to identify an AI assistant and generates a UI in which an administrator selects which skills that assistant use for that customer].
It would have been obvious to one of an ordinary skill in the art before the affective filing date to combine Brown’s virtual assistant skill development UI with Machado ‘s multi-tenant AI chatbot.
One would have been motivated to make such a combination in order to predict result of an interface that lists customers and for each customer interactive controls to enable and disable the AI agent’s skills for that customer.
Brown and Machado do not teach for selectively enabling and/or disabling.
Will, IV discloses for selectively enabling and/or disabling [see para. 0047, 0049; a chatbot compares an inputted user utterance to a predefined list of user intents, without an ability to “turn off” or restrict specific user intents for a particular user during the chatbot session. As a result, if user intent filtering is needed, then chatbot dialog code must generated to manage each user intent individually. In other words, the chat bot could have different replies based on user data, but each change and all the different scenarios would have to be handled one by one on each dialog path and allow user intents recognized by a natural language understanding model of the chatbot to be processed or blocked based on identified characteristics of the user, such as, for example, user location, user job role, user employment status. Thus, enable the chatbot to recognize when a particular user should not have access to content corresponding to a given user intent; which corresponds to selective enabling and disabling chatbot/agent interactions that use particular skills for customer].
It would have been obvious to further modify Brown and Machado with Will, IV to one of an ordinary skill in the art before the affective filing date to combine Brown’s virtual assistant skill development UI with Machado ‘s multi-tenant AI chatbot to include Will’s using a natural language understanding model of a chatbot.
One would have been motivated to make such a combination in order to predict result of an interface that lists customers and for each customer interactive controls to enable and disable the AI agent’s skills for that customer and user intent enable/disable so different customers do not receive the same skill set.
Regarding claim 2, Will, IV discloses the method further comprising receiving user input disabling Al agent interactions involving one or more of the Al agent skills by the Al agent for a particular customer identified in the plurality of customers [see para. 0047, 0049; a chatbot compares an inputted user utterance to a predefined list of user intents, without an ability to “turn off” or restrict specific user intents for a particular user during the chatbot session. As a result, if user intent filtering is needed, then chatbot dialog code must generated to manage each user intent individually. In other words, the chat bot could have different replies based on user data, but each change and all the different scenarios would have to be handled one by one on each dialog path and allow user intents recognized by a natural language understanding model of the chatbot to be processed or blocked based on identified characteristics of the user, such as, for example, user location, user job role, user employment status; which corresponds to selective enabling and disabling chatbot/agent interactions that use particular skills for customer].
Regarding claim 3, Will, IV discloses the method further comprising receiving user input enabling Al agent interactions involving one or more of the Al agent skills by the AI agent for a particular customer identified in the plurality of customers [see para. 0046, 049; allow user intents recognized by a natural language understanding model of the chatbot to be processed or blocked based on identified characteristics of the user, such as, for example, user location, user job role, user employment status. Thus, enable the chatbot to recognize when a particular user should not have access to content corresponding to a given user intent; which corresponds to selective enabling and disabling chatbot/agent interactions that use particular skills for customer].
Regarding claim 4, Brown discloses where the one or more AI agent skills includes a skill for answering a question [see para. 0002; Virtual assistants (also known as “intelligent personal assistants”) are software applications that can perform various tasks and services for users. Virtual assistants implemented using a variety of different computing devices, and respond to a variety of commands from users. The various features of virtual assistants referred to as “skills” of the virtual assistant].
Regarding claim 5, Machado discloses where the one or more AI agent skills includes a skill for scheduling a meeting [see para. 0017; The multi-tenant system configures a user interface that displays a set of actions and allows the user to configure a tenant-specific set of permitted action. Examples of actions that the user may request include searching for a contact, updating an account with certain information, updating status of a project, and so on. The tenant-specific set of permitted actions may represent the actions that a set of users having a particular role are allowed to perform using the multi-tenant system].
Regarding claim 6, Machado discloses where the one or more AI agent skills includes a skill for fulfilling a purchase order [see para. 0029; The data store implemented as a relational database storing one or more tables. Each table contains one or more data categories logically arranged as columns or fields. Each row or record of a table contains an instance of data for each category defined by the fields. For example, a data store include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table describe a purchase order, including fields for information such as customer, product, sale price, date, etc].
Regarding 7, Brown discloses where the one or more AI agent skills includes a skill for generating an electronic communication [see para. 0032; The system may also receive and process requests from developers, publishers and other users involved in producing virtual assistant software. For example, the system may receive (e.g., in an electronic communication from a third party server over network) a request to modify a deployment group and/or software application].
Regarding claim 9, Brown discloses where the generated interface identifying the plurality of customers further visually identifies [see para. 0002, 0008; virtual assistant systems by providing dynamic, customizable deployment groups for virtual assistant software features. Users, such as software developers, can deploy and remove software packages from different groups, and precisely define the members in any number of different deployment groups that can access the deployed virtual assistant software].
Will, IV discloses which Al agent skills are enabled and disabled for each identified customer [see para. 0047, 0049; a chatbot compares an inputted user utterance to a predefined list of user intents, without an ability to “turn off” or restrict specific user intents for a particular user during the chatbot session. As a result, if user intent filtering is needed, then chatbot dialog code must generated to manage each user intent individually].
One would have been motivated to make such a combination in order to predict result of an interface that lists customers and for each customer interactive controls to enable and disable the AI agent’s skills for that customer and user intent enable/disable so different customers do not receive the same skill set.
Regarding claim 10, Will, IV discloses where the visual identification of which AI agent skills are enabled or disabled for each identified customer is accomplished via a displayed selectable toggle object for each Al agent skill [see para. 0021; provide easy visibility into the virtual assistant skills in each deployment group, and helps to avoid confusion among general users as skill features are added, removed, or modified during testing. Moreover, while the embodiments of the present disclosure focus on the deployment of software for virtual assistants, various features of the present disclosure may be applicable to the deployment of other forms of software as well].
Regarding claim 11, Brown discloses where Al agent skills that are enabled for an identified customer are visually displayed in the form of a skill visualization including a text label representing the AI agent skill [see para. 0036; the system allows deployments from left to right in the set of groups, but not right to left. Accordingly, a self-deployed skill can move into the group, tenant, or product groups, while Group-deployed skills can move into the tenant or product groups, and tenant-deployed skills can move into the product group, a developer allowed to promote or demote deployments as desired, so a deployment to the tenant group rolled back to self or group or promoted to product].
Regarding claim 12, Machado discloses where AI agent skills that are disabled for an identified customer are not visually displayed [see para. 0054; the computer initializes a user intent mapping table that contains a plurality of user intent names and initializes a set of user intent eligibility filters corresponding to the characteristics associated with the user for the session with the chatbot. Further, the computer removes any user intent names from the user intent mapping table that do not match a valid user location filter in the set of user intent eligibility filters based on a current geographic location of the user identified in the session data. Furthermore, the computer makes a determination as to whether another user intent eligibility filter exists in the set of user intent eligibility filters].
Regarding claim 13, Machado discloses the method further comprising: displaying on the interface a display field capable of receiving natural language input from a user, where one or more behaviors of the one or more AI agents are modified based on the natural language input from the user [see para. 0016; A chatbot is also referred to as a conversational agent, a dialog system, virtual assistant, or artificial intelligence (AI) assistant. A tenant refers to an entity, for example, an organization enterprise that is a customer of the multi-tenant system. The term tenant as used herein refer to the set of users of the entire organization that is the customer of the multi-tenant system or to a subset of users of the organization. Accordingly, the tenant-specific chatbot may be customized for a set of users, for example, the entire set of users of the organization, a specific group of users within the organization, or an individual user within the organization].
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 14-20 are allowed over prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Kozaris discloses systems and methods are provided for controlling the creation, management, presentation of and interaction with memory data structures for AI agents. Interfaces are also provided for facilitating presentation of and interaction with memory data structures (e.g., via memory visualizations) on user interfaces, such that the user can intuitively understand and modify specific memory data structures to train the AI agent to better perform a variety of tasks and modify the behavior of the AI agent(s) according to user preference.
Kinsella et al. (US 2025/038255) discloses systems and methods are provided for managing AI (Artificial Intelligence) agents interactions with electronic communications that are displayed at an electronic communications interface, such as an email interface. The systems parse electronic communications to determine whether they correspond to new or existing requests and actions to be performed when responding to the request(s). The systems also generate notifications identifying the set of actions with visual identifiers that visually distinguish the set of actions based on whether they have been completed or not and whether they need authorization to be completed. The systems also display selectable controls for controlling how the AI agent performs the actions.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
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/CAO H NGUYEN/ Primary Examiner, Art Unit 2171
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