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
Claims 3, 5, 10, 12, 17 and 19 are rejected under 35 U.S.C. 112(b) as indefinite for failing to particularly pointing out and distinctly claiming the subject matter of the invention:
The terms “processor-intensive subtask,” “memory-intensive subtask” and “network-intensive subtask” in claims 3, 10 and 17 “sensitive data” in claims 5, 12 and 19 are relative terms which renders the claims indefinite. The terms “intensive” and “sensitive” are not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For claim interpretation purposes the terms “intensive” and “sensitive” are ignored while interpreting the claims against prior art.
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.
Claim(s) 1 – 3, 11 – 14 and 18 – 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by (Kaplan, US 2021/0297494).
Regarding claim 1, Kaplan discloses:
a method for routing tasks to AI systems (see at least Fig. 9 and ph. [0093] – [0103]) , the method comprising:
receiving, by smart router circuitry (see at least ph. [0080] for the AI layer activating its router service to determine the appropriate aiOS or AI layer to send the service request too where system circuitry is implementing these layers and routers, where the circuitry would be included in the system of at least Fig. 13), a subtask request that is representative of instructions to execute an actionable subtask (see at least ph. [0101] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and as the overall request has portions, the these portions are subtasks);
determining, by the smart router circuitry, computational capabilities associated with one or more AI systems (see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layers and as the overall request has portions, and these portions are then subtasks and this is applied to figuring appropriate computational capabilities associated with the appropriate selected AI system such as in the finding of an appropriate handler for handling the facial recognition example in at least ph. [0080] – [0081]);
matching, by the smart router circuitry, the subtask request with a target AI system of the one or more AI systems (see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layer(s), which the offloading to the selected aiOS and/or AI layer(s) indicates that they were selected as the appropriate match to perform the additional processing); and
causing, by communications hardware, transmission of the subtask request to the target AI system (see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layers, the offloading indicating transmission and this would be accomplished by hardware would be included in the computing system of at least Fig. 13).
Regarding claim 2, the rejection of claim 1 is incorporated and Kaplan discloses:
detecting, by the smart router circuitry, a language associated with the subtask request, wherein the language comprises one or more of a spoken language, a written language, a sign language, or a coding language (see at least ph. [0086] for language support for speech patterns for the AI services and system which then ties into the aiOS and AI layers as and router service as per at least ph. [0101] – [0102]);
assessing, by the smart router circuitry, the subtask request to determine the computational capabilities required to process the subtask request (see at least ph. [0080] for the AI layer activating its router service to determine the appropriate aiOS or AI layer that can perform the actual portion(s) of the processing for that request as per at least ph. [0101] –[0102]); and
evaluating, by the smart router circuitry, a computational profile associated with each of the one or more AI systems, wherein a respective computational profile comprises one or more of processing data, memory data or storage data, networking data, timing data, current load data, expected availability data, total capacity data, geolocation data, or security data (see at least ph. [0080] – [0081] for the system figuring out what AI layer and/or aiOS will perform the facial recognition processing as the initial aiOS/AI layer cannot do it itself, where this is then selecting which other such system can perform the data processing necessary to carry out the data processing).
Regarding claim 3, the rejection of claim 1 is incorporated and Kaplan discloses:
determining, by the smart router circuitry and based on the computational capabilities, an intelligent routing for the subtask request (see at least ph. [0080] for the AI layer activating its router service to determine the appropriate aiOS or AI layer to send the service request too where system circuitry is implementing these layers and routers, where the circuitry would be included in the system of at least Fig. 13 and see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layers and as the overall request has portions, the these portions are subtasks), wherein determining the intelligent routing comprises at least one of:
matching a processor-intensive subtask request with a first computational profile indicating a processor-intensive metric associated with processing data (see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layers and as the overall request has portions, and these portions are then subtasks and this is applied to figuring appropriate computational capabilities associated with the appropriate selected AI system such as in the finding of an appropriate handler for handling the facial recognition example in at least ph. [0080] – [0081] where this requires some processing of some intensity to perform the task),
matching a memory-intensive subtask request with a second computational profile
indicating a memory-intensive metric associated with memory data or storage data (see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layers and as the overall request has portions, and these portions are then subtasks and this is applied to figuring appropriate computational capabilities associated with the appropriate selected AI system such as in the finding of an appropriate handler for handling the facial recognition example in at least ph. [0080] – [0081] where this requires some memory use of some intensity to perform the task involving memory data use and access), or
matching a network-intensive subtask request with a third computational profile
indicating an optimized network routing metric associated with networking data (see at least ph. [0101] – [0102] for the aiOS locally performing a portion of the request and offloading other portions to another aiOS and/or AI layers and as the overall request has portions, and these portions are then subtasks and this is applied to figuring appropriate computational capabilities associated with the appropriate selected AI system such as in the finding of an appropriate handler for handling the facial recognition example in at least ph. [0080] – [0081] where this requires some level of network intensity to perform the task to involve network resources as per the disclosures of at least ph. [0083] – [0084]).
Allowable Subject Matter
Claims 4 - 7, 11 - 14 and 18 - 20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
References Cited Not Relied Upon
Sivakumar et al. (US 2022/0318685) discloses biased based delegation/selection of machine learning models using a machine learning model that better learns to select among the models based on their biases for different tasks.
Mermoud et al. (US 2024/0137293) discloses predictive application aware routing engines that utilize network volume and application telemetry from routers to compute statistical and/or machine learning models to control the network.
Archer et al. (US 2024/0177083) discloses a router or other system component learning of new events and based on the learning of these events matches tasks and actors.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CRAIG C DORAIS whose telephone number is (571)270-3371. The examiner can normally be reached M-F 9:00 am - 6:00pm.
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, Pierre Vital can be reached at 5712724215. 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.
/CRAIG C DORAIS/Primary Examiner, Art Unit 2198
Prosecution Insights