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 Objections
Claim 11 is objected to because of the following informalities:
Claim 11 recites “the optimum assignment of assign the plurality of first entities”. This appears to be a typographical error and should possibly recite “the optimum assignment of the plurality of first entities”.
Appropriate correction is required.
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.
Claim(s) 1, 6-9, and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owechko in view of Campardo. Apparatus claims 9 and 14-16 will be discussed before method claims 1 and 6-8 and computer program product claim 17.
Regarding claim 9, Owechko teaches a system comprising:
a processor (Fig. 10 elem. 1006; Col 15 Line 4-23);
a generic memory in operable communication with the processor and storing computer program code that when executed on the processor causes the processor to execute a process operable to perform the operations of (Fig. 10 elem. 1008; Col 15 Line 4-23);
receiving a request for an answer to a problem, the problem comprising an optimum assignment of a plurality of first entities to a plurality of second entities (Fig. 3; Col 11 Line 44-57; Col 11 Line 64 – Col 12 Line 4; transmission of the image windows/views to classifier as request, the matched objects in the different views as answer, matching objects in different views as problem, Col 13 Line 22-32; “Optimal pairing (correspondence) between the inter-view points” as optimum assignment, objects in the first view as plurality of first entities, objects in the second view as plurality of second entities);
defining, for the plurality of first entities and plurality of second entities, a particle swarm optimization (PSO) (Fig. 3; Col 13 Line 22-32; objects in the first view as plurality of first entities, objects in the second view as plurality of second entities, Col 6 Line 43-47, classifier agents/particles as particles, Fig. 1, “cognitive swarm of classifier agents” as a particle swarm optimization (PSO));
the PSO associated with a swarm comprising a plurality of particles (Fig. 1 Col 4 Line 35-36, “cognitive swarm of classifier agents” as the PSO, Col 6 Line 43-47, classifier agents/particles as particles);
each particle having a respective particle location representative of at least one assignment of at least one first entity from the plurality of first entities to at least one second entity of the plurality of second entities (Col 6 Line 43-47, classifier agents/particles as particles, Col 11 Line 64 – Col 12 Line 10, “associated 3D location X and an object height h” as respective particle location, Col 13 Line 22-32, “Optimal pairing (correspondence) between the inter-view points” as representative of at least one assignment for, Col 12 Line 62 – Col 13 Line 21, “an object in view 1” as at least one first entity from the plurality of first entities, “an object in view 2” as at least one second entity of the plurality of second entities);
wherein the PSO is configured to determine at least one solution to the optimum assignment of the plurality of first entities to the plurality of second entities (Fig. 1 Col 4 Line 35-36, “cognitive swarm of classifier agents” as the PSO, Fig. 3; Col 13 Line 22-32, for "Optimal pairing (correspondence) between the inter-view points" as configured to determine at least one solution to the optimum assignment, objects in the first view as plurality of first entities, objects in the second view as plurality of second entities);
defining, for the plurality of first entities and plurality of second entities, a cost matrix configured to analyze each solution determined in the PSO in accordance with a Hungarian algorithm (Fig. 3; Col 13 Line 22-32; objects in the first view as plurality of first entities, objects in the second view as plurality of second entities, Col 13 Line 54 – Col 14 Line 6, “cost/distance matrix” as cost matrix, “Optimal pairing between the inter-view points is based on the cost matrix” as configured to analyze each solution determined in the PSO, “the problem can be solved using the Hungarian algorithm” as in accordance with a Hungarian algorithm);
wherein the cost matrix is configured to optimize at least one constraint associated with the plurality of first entities and plurality of second entities (Col 13 Line 54 – Col 14 Line 6, “cost/distance matrix” as cost matrix, “encapsulating both geometric and appearance constraints” as configured to optimize at least one constraint, Fig. 3; Col 13 Line 22-32; objects in the first view as plurality of first entities, objects in the second view as plurality of second entities);
running a first iteration of the PSO on the plurality of first entities and plurality of second entities (Col 2 Line 17-34, “Each agent is also configured to perform at least one iteration” as running a first iteration of the PSO, Fig. 3; Col 13 Line 22-32; objects in the first view as plurality of first entities, objects in the second view as plurality of second entities);
to generate a first set of PSO solutions corresponding to at least one potential answer to the problem (Col 2 Line 17-34, “Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions” as to generate a first set of PSO solutions, “associated with an observed best solution (pbest) that the agent has identified” as corresponding to at least one potential answer to the problem);
each PSO solution corresponding to a respective particle at a respective particle location (Col 2 Line 17-34, “an observed best solution (pbest) that the agent has identified” as each PSO solution, Col 11 Line 64 – Col 12 Line 10, “associated 3D location X and an object height h” as corresponding to a respective particle at a respective particle location);
(e) applying the cost matrix to the first set of PSO solutions generated to determine a cost score for each respective particle (Col 13 Line 54 – Col 14 Line 6, “cost/distance matrix” as cost matrix, “all possible inter-view pairs of identified object locations” as the first set of PSO solutions generated, Col 11 Line 64 – Col 12 Line 10, “cost function guiding the particle's search trajectory” as determine a cost score for each respective particle); and
selecting the solution having the particle with best cost score in the first set of PSO solutions, to be an optimized global best particle location for a next iteration of the PSO (Col 3 Line 48-52, “cost function, minimization of which ensures a consistent localization of an object in the 3D spatial coordinates” as selecting the solution having the particle with best cost score in the first set of PSO solutions, Col 9 Line 13-31, “global best parameter (gbest) is used to store the best location” as an optimized global best particle location, “The velocity of each particle is then changed towards pbest and gbest in a probabilistic way” as a next iteration of the PSO).
While Owechko discloses a generic memory storing code, Owechko does not teach a non-volatile memory storing the code.
However, Campardo teaches a non-volatile memory storing code (Fig. 2, 1.1 Introduction, “Non-Volatile Memory design, i.e. devices capable of keeping the stored information even without external power supply.”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date to modify Owechko’s generic memory to be the specific non-volatile memory taught by Campardo. This modification would have been obvious because it would prevent data loss if power is lost (Campardo Fig. 2, 1.1 Introduction).
Regarding claim 14, Owechko teaches a system wherein the constraint (Col 10 Line 43-Col 11 Line 3,“the classifier level” as the constraint) comprises at least one of: cost, time, efficiency, power consumption, resource utilization, and growth, a factor to be maximized, and an undesired effect to be minimized (Col 10 Line 43-Col 11 Line 3, “consisting of object location, scale, and other classifier parameter dimensions” as cost, time, efficiency, power consumption, resource utilization, and growth, a factor to be maximized, and an undesired effect to be minimized);
Regarding claim 15, Owechko teaches a system wherein each respective particle location corresponds to an assignment of at least one first entity from the plurality of first entities to at least one second entity of the plurality of second entities, at a specific time (Col 11 Line 64 – Col 12 Line 10, “associated 3D location X and an object height h” as respective particle location, Col 13 Line 22-32; “Optimal pairing (correspondence) between the inter-view points” as an assignment, “an object in view 1” as one first entity from the plurality of first entities, “an object in view 2” as one second entity of the plurality of second entities, Col 14 Line 59 – Col 15 Line 2, “correctly label objects across different views and time” as at a specific time);
Regarding claim 16, Owechko teaches a system wherein at least one of the plurality of first entities and the plurality of second entities comprises at least one of: a task to be performed, an entity capable of performing a task, an entity configured for having a task performed on it, a method of performing a task, a path for performing a task, a location for performing a task, a resource for performing a task, and an asset for performing a task (Col 13 Line 22-32, objects in the first view as plurality of first entities, objects in the second view as plurality of second entities, Col 11 Line 64 – Col 12 Line 4, matching objects in different views as task).
Claims 1 and 6-8 are directed to a method that would be practiced by the apparatus of claims 9, and 14-16, respectively as configured. All steps recited in the method claims of 1 and 6-8 are practiced by the apparatus of claims 9, and 14-16, respectively as configured. The claim 9, and 14-16 analysis applies equally to claims 1, and 6-8 respectively
Claim 17 is a computer program product claim corresponding to apparatus claim 9. It is rejected for the same reasons.
Claim(s) 2-5, 10-13, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Owchechko in view of Campardo as applied to claims 1, 9, and 17 above, and further in view of Engelbrecht. Apparatus claims 10-13 will be discussed before corresponding method claims 2-5 or corresponding media claims 18-20.
Regarding claim 10, Owechko in view of Campardo teaches the invention substantially as claimed. See the rejection of claim 9 above. In addition, Owechko teaches a system further comprising:
providing computer program code that when executed on the processor causes the processor to perform the operations of (Col 15 Line 4-23); and
(g) running a next iteration of the PSO using the optimized global best particle location determined in (e) as a location towards which particles in the PSO will swarm during the next iteration of the PSO (Col 9 Line 13-31, “velocity of each particle is then changed towards pbest and gbest in a probabilistic way” as a next iteration of the PSO, “global best parameter (gbest) is used to store the best location” as optimized global best particle location).
While Owechko discloses computer code that when executed runs a next iteration of the PSO based on the global best particle location, the combination of Owechko in view of Campardo does not teach the next iteration generating updated PSO solutions, repeating iterations until stop criteria is met, or returning a response to the request that comprises a new global best particle location based on the most recent iteration of the PSO that ran before meeting the stop criteria.
However, Engelbrecht teaches the next iteration generating updated PSO solutions, repeating iterations until stop criteria is met, and returning a response to the request that comprises a new global best particle location based on the most recent iteration of the PSO that ran before meeting the stop criteria (Section 16.1.6 Page 295-296; Section 16.5.5 Page 334-337).
It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date to combine the object recognition system that incorporates swarming classifiers of Owechko with the PSO algorithm of Engelbrecht with the aim of optimizing the global best particle location to generate an optimal set of PSO solutions.
Regarding claim 11, Owechko in view of Campardo and Engelbrecht teaches the invention substantially as claimed. See the rejection of claim 10 above. Owechko also teaches the following:
the optimum assignment of assign the plurality of first entities to the plurality of second entities (Col 13 Line 22-32; “Optimal pairing (correspondence) between the inter-view points” as optimum assignment, objects in the first view as plurality of first entities, objects in the second view as plurality of second entities).
While Owechko discloses request for the optimum assignment between a plurality of first and second entities, Owechko does not teach the response to the request comprising information necessary to provide a recommendation of the optimum assignment.
However, Engelbrecht teaches the response to the request comprising information necessary to provide a recommendation of the optimum assignment (Section 16.5.5 Page 334-337).
It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date to combine the request for an optimum assignment of Owechko with the information necessary to provide a recommendation of the optimum assignment in the response to the request of Engelbrecht with the aim of optimizing the generation of an optimal set of PSO solutions.
Regarding claim 12, Owechko in view of Campardo teaches the invention substantially as claimed. See the rejection of claim 9 above. Owechko also teaches a system further comprising:
providing computer program code that when executed on the processor causes the processor to perform the operations of (Fig. 10 elem. 1008; Col 15 Line 4-23);
(g) running a next iteration of the PSO using the optimized global best particle location determined in (e) as a location towards which particles in the PSO will swarm during the next iteration of the PSO (Col 9 Line 13-31, “velocity of each particle is then changed towards pbest and gbest in a probabilistic way” as a next iteration of the PSO, “global best parameter (gbest) is used to store the best location” as optimized global best particle location).
While Owechko discloses computer code that when executed runs a next iteration of the PSO based on the global best particle location, the combination of Owechko in view of Campardo does not teach the next iteration generating updated PSO solutions, repeating iterations until stop criteria is met, or returning a response to the request that comprises a new global best particle location based on the most recent iteration of the PSO.
However, Engelbrecht teaches the next iteration generating updated PSO solutions, repeating iterations until stop criteria is met, and returning a response to the request that comprises a new global best particle location based on the most recent iteration of the PSO (Section 16.1.6 Page 295-296; Section 16.5.5 Page 334-337).
It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date to combine the object recognition system that incorporates swarming classifiers of Owechko with the PSO algorithm of Engelbrecht with the aim of optimizing the global best particle location to generate an optimal set of PSO solutions.
Regarding claim 13, Owechko teaches a system wherein:
the global best particle location from the next iteration of the PSO (Col 9 Line 13-31, “global best parameter (gbest) is used to store the best location” as global best particle location, “The velocity of each particle is then changed towards pbest and gbest in a probabilistic way” as next iteration of the PSO);
provides information necessary to provide a recommendation for the optimum assignment of assign the plurality of first entities to the plurality of second entities (Col 9 Line 13-31, “global best parameter (gbest) is used to store the best location” as information necessary to provide a recommendation, Col 13 Line 22-32; “Optimal pairing (correspondence) between the inter-view points” as optimum assignment, objects in the first view as plurality of first entities, objects in the second view as plurality of second entities).
Claims 2-5 are directed to a method that would be practiced by the apparatus of claims 10-13, respectively as configured. All steps recited in the method claims of 10-13 are practiced by the apparatus of claims 2-5, respectively as configured. The claim 2-5 analysis applies equally to claims 10-13 respectively.
Regarding claims 18-20, they are computer program product claims corresponding to apparatus claims 11-13. The claims correspond as follows: 18 with 12, 19 with 13, 20 with 11. They are rejected for the same reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON B NGUYEN whose telephone number is (571)272-8967. The examiner can normally be reached Monday - Friday, 7 AM - 4 PM.
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, Andrew Caldwell can be reached at (571) 272-3702. 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.
/J.B.N/ Jason B. NguyenExaminer, Art Unit 2182
(571) 272-8967
/ANDREW CALDWELL/Supervisory Patent Examiner, Art Unit 2182
Prosecution Insights