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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8 – 16, 18 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Stets et al. (US PAP 2018/0096284).
As per claims 1, 16, 19, Stets et al. teach a computing system comprising:
at least one memory; one or more hardware processor units coupled to the at least one memory; and one or more computer readable storage media storing computer-executable instructions that, when executed, cause the computing system to perform operations comprising (“at least one memory comprising instructions that when executed, cause the at least one processor to execute a first computational agent from a plurality of computational agents”; paragraphs 6, 7):
receiving data representing a hierarchically structured collection of a plurality of capabilities, wherein a first proper subset of the plurality of capabilities are, or represent, discrete agents whose execution can be called in response to a request from a natural language generator and a second proper subset of the plurality of capabilities correspond to a subcategory of a higher-level capability (“agent indices 124 may store, for each agent, an agent description and a list of capabilities in a semi-structured index of agent information…the assistant may determine capability levels of a first party agent (616) and third party agents (618) to perform the identified task.”; paragraphs 47,132 – 136);
submitting capabilities of a first hierarchical level of the hierarchically structured collection to a natural language generator; receiving from the natural language generator a selection of a capability of the second proper subset (“determining capability levels of respective computational agents of the plurality of agents to perform the second sub-set of elements; and responsive to determining that a capability level of a particular agent of the plurality of agents is a greatest of the determined capability levels, and that the capability level of the particular agent satisfies a threshold capability level, selecting the particular agent as the second computational agent.”; paragraphs 132 – 136, 159);
submitting capabilities of a second hierarchical level to the natural language generator; receiving from the natural language generator a selection of a capability of the second hierarchical level, the capability of the second hierarchical level being a capability of the first proper subset; executing the discrete agent corresponding to the capability of the second hierarchical level; and returning to the natural language generator execution results of the executing the agent (If the capability level of the first party agent does not satisfy the threshold capability level (““No” branch of 620), the assistant may determine whether the third party agent with the greatest capability level (hereinafter the “particular third party agent”) satisfies the threshold capability level (622). If the capability level of the particular third party agent satisfies the threshold capability level (“Yes” branch of 622), the assistant may select the particular third party agent to perform the task (608), and cause the particular third party agent to perform the task”; paragraphs 132 – 138, 159).
As per claims 8, 18, Stets et al. further disclose the discrete agents are computing language subclasses of a computing language agent base class (“perform, by the first computational agent, a first sub-set of elements of the multi-element task, wherein performing the first sub-set of elements comprises selecting a second computational agent from the plurality of computational agents to perform a second sub-set of elements of the multi-element task.”; paragraphs 4 – 9, 132 – 138).
As per claim 9, Stets et al. further disclose the discrete agents are defined in one or more plugins registered with an agent framework (paragraphs 47 – 49).
As per claim 10, Stets et al. further disclose registering at least one plugin of the one or more plugins with the agent framework; and extracting descriptive information for the discrete agent from the at least one plugin (“agent indices 124 may store, for each agent, an agent description and a list of capabilities in a semi-structured index of agent information. For instance, agent indices 124 may contain a single document with information for each available agent. A document included in agent indices 124 for a particular agent may be constructed from information provided by a developer of the particular agent.”; paragraphs 47 – 49).
As per claim 11, Stets et al. further disclose for respective agents of the of at least one plugin, storing in a database an identifier of a respective agent and at least a portion of the descriptive information for the respective agent (“agent indices 124 may store, for each agent, an agent description and a list of capabilities in a semi-structured index of agent information. For instance, agent indices 124 may contain a single document with information for each available agent. A document included in agent indices 124 for a particular agent may be constructed from information provided by a developer of the particular agent.”; paragraphs 47 – 49).
As per claim 12, Stets et al. further disclose receiving a prompt to be submitted to the natural language generator; and adding to the prompt the capabilities of the first hierarchical level to provide a revised prompt; wherein the submitting capabilities of the first hierarchical level to the natural language generator comprises submitting the revised prompt to the natural language generator (“The collected user reviews and ratings may be used to modify the agent quality scores. As one example, when an agent receives positive reviews and/or ratings, agent accuracy module 331 may increase the agent's agent quality score in agent index 224 or agent index 324.”; paragraphs 102, 115).
As per claim 13, Stets et al. further disclose the hierarchically structured collection is stored in one or more tables of a database comprising information for capabilities of the hierarchical structure, the information comprising, for respective capabilities of the capabilities, at least one attribute comprising descriptive information for the capability and a reference to at least one other capability of the capabilities corresponding to a parent capability or a child capability (“Agent selection module 227 may identify agent documents in agent index 224 that match either the utterance or the triggering phrases. Agent selection module 227 may rank the identified agent documents (e.g., based on a capability level to satisfy the utterance).”; paragraphs 95, 132 – 138, 159).
As per claim 14, Stets et al. further disclose at least one table of the one or more tables comprises an attribute indicating whether a respective capability corresponds to an agent (“Local assistance module 122A may also maintain a mapping of text-matching system's rules to the applicable agents.”; paragraphs 49 – 51).
As per claim 15, Stets et al. further disclose at least one table of the or more tables comprises an attribute identifying, for capabilities corresponding to agents, an identifier of an agent corresponding to the capability (“Local assistance module 122A may also maintain a mapping of text-matching system's rules to the applicable agents…the agents may use different identifiers”; paragraphs 22, 49 – 51).
As per claim 20, Stets et al. further disclose computer-executable instructions that, when executed by the computing system, cause the computing system to return to the natural language generator execution results of executing an agent corresponding to the capability of the second proper subset selected by the natural language generator (“Agent selection module 227 may analyze the rankings and/or the results from the web search to select an agent to satisfy the utterance. For instance, agent selection module 227 may inspect the web results to determine whether there are web page results associated with agents. If there are web page results associated with agents, agent selection module 227 may, insert the agents associated with the web page results into the ranked results (if said agents are not already included in the ranked results).”; paragraphs 97 – 99).
Allowable Subject Matter
Claim 2 -7, 17 are 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. The following is a statement of reasons for the indication of allowable subject matter:
As to claims 2 -7, 17, the prior art made of record does not teach or suggest
generating the hierarchically structured collection, the generating comprising: classifying a first plurality of agents into a first number of categories not exceeding a first threshold defined for the first hierarchical level; for at least one category of the first number of categories, determining that a number of agents classified in the at least one category exceeds a second threshold defined for at least the second hierarchical level, wherein the second threshold is the first threshold or is different than the first threshold; and in response to determining that a number of agents classified in the at least one category exceeds a second threshold, classifying a second plurality of agents into a second number of categories not exceeding a second threshold defined for the second hierarchical level.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sabharwal et al. teach SYSTEM AND METHOD FOR DESIGNING ARTIFICIAL INTELLIGENCE (AI) BASED HIERARCHICAL MULTI-CONVERSATION SYSTEM. Tan et al. teach ROUTING TEXT CLASSIFICATIONS WITHIN A CROSS-DOMAIN CONVERSATIONAL SERVICE. Nogima et al. teach DIALOG BASED SPEECH RECOGNITION. Wang et al. teach SELECTION OF COMPUTATIONAL AGENT FOR TASK PERFORMANCE. Nygaard et al. teach SYNTHESIZED VOICE SELECTION FOR COMPUTATIONAL AGENTS.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD SAINT-CYR whose telephone number is (571)272-4247. The examiner can normally be reached Monday- Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD SAINT-CYR/Primary Examiner, Art Unit 2658