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 § 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al.(U.S. Patent No. 10,430,876) in view of Breed et al. (U.S. Pub No. 20030209893).
Regarding claims 1, 12, 19, Tang et al. disclose a method, system and non-transitory computer readable medium comprising: generating, by one or more processors, a model (model generator 120; col. 5, lines 43-57);
training, by the one or more processors, the model, wherein the training is performed with images of different interiors of vehicles that are in a default state (See col. 6, lines 4-26); and
sending, by the one or more processors, the trained model to a vehicle, wherein the trained model is configured to output an indication associated with an occupancy of the vehicle. Col. 6, lines 18-26 as the identify the vehicle would obviously associate with an occupancy of the vehicle). Tang et al. does not specifically disclose a trained model is configured to output an indication associated with an occupancy of the vehicle.
However, Breed et al. specifically discloses a trained model (trained neural networks) is configured to output an indication associated with an occupancy of the vehicle (See paragraph 0627) also discloses different interiors of the vehicle (See paragraph 0064, different zone classification). It would have been obvious to one of ordinary skill in the art with a reasonable expectation of success to modify the system of Tang et al. with that of Breed et al. by having training performed with images of different interiors of vehicles and wherein the trained model is configured to output an indication associated with an occupancy of the vehicle because it would have achieved a desired result for the classification of the occupant in the vehicle.
Regarding claims 2, 13, 20, Tang et al. disclose all but not specifically disclose wherein the vehicles that are in the default state are empty. In an analogous art Breed et al. disclose wherein the vehicles that are in the default state are empty (See Breed et al. paragraph 0872, 0875). It would have been obvious to one of ordinary skill in the art with a reasonable expectation of success to modify the system of Tang et al. with that of Breed et al wherein the vehicles that are in the default state are empty because it would have achieved a desired result for the classification of the occupant in the vehicle.
Regarding claims 3, 14, Tang et al. disclose wherein the different interiors appear under different lighting conditions, color schemes, or vehicle ages. See col. 6, lines 41-50). (See also paragraph 0272 of Breed et al.)
Regarding claims 4, 15, Tang et al. disclose all but do not specifically disclose wherein the output indicates whether the vehicle is occupied or not occupied. In an analogous art, Breed et al. disclose wherein the output indicates whether the vehicle is occupied or not occupied (See Breed et al. paragraph 0339).
Regarding claims 5, 16, Tang et al. disclose all but fail to specifically disclose wherein the output identifies passengers and objects located within the vehicle. In an analogous art, Breed et al. disclose wherein the output identifies passengers and objects located within the vehicle (See Breed et al. paragraph 0339). It would have been obvious to one of ordinary skill in the art with a reasonable expectation of success to modify the system of Tang et al. with that of Breed et al because it would have achieved a desired result for the classification of the occupant in the vehicle.
Regarding claims 6, 17, Tang et al. disclose all but fail to specifically disclose wherein the output indicates whether a child is in the vehicle. In an analogous art, Breed et al. disclose wherein the output indicates whether a child is in the vehicle (See Breed et al. paragraph 0339, 0343). ). It would have been obvious to one of ordinary skill in the art with a reasonable expectation of success to modify the system of Tang et al. with that of Breed et al because it would have achieved a desired result for the classification of the occupant in the vehicle.
Regarding claim 7, Tang et al. disclose all but fail to specifically disclose: deactivating, by the one or more processors, an airbag adjacent to a seat in which the child is located. In an analogous art, Breed et al. disclose deactivating, by the one or more processors, an airbag adjacent to a seat in which the child is located (See Breed et al. 0390).
Regarding claim 8, Tang et al. disclose all but Breed et al. more specifically disclose sending, by the one or more processors, a notification requesting confirmation that the child is allowed to ride in the vehicle (See paragraph 0390 a child is placed in the child seat and therefore the child is allowed to ride in the vehicle).
Regarding claims 9, 18,Tang et al. disclose wherein the model is trained by inputting a plurality of images associated with different states of the vehicle into the model (See Tang et al. Col. 6, lines 18-26).
Regarding claim 10, Tang et al. disclose wherein the model is trained by labeling the plurality of images with details about passengers in the vehicle (See Tang et al. col. 6, lines 4-26, wherein it is obvious to also have images about passengers in the vehicle);
[See also Tang et al. col. 22, line 62-col. 23, line 2; “ Server system 105 may receive a plurality of images to generate an attribute identification function in step 1502. These images may be collected by image classifier 130. In some aspects these images may be scraped from, for example, websites and/or inventories of vehicle dealers or vehicle manufacturers. The collected images may be associated with metadata describing the type of image and whether the image is exterior or interior.”]
Regarding claim 11, Tang et al. disclose all but do not specifically disclose wherein the details indicate one or more of a number of the passengers, where the passengers are located in the vehicle, whether the passengers are adults or children, or whether the passengers are wearing a seatbelt. In an analogous art, Breed et al. disclose the details indicate one or more of a number of the passengers, where the passengers are located in the vehicle, whether the passengers are adults or children, or whether the passengers are wearing a seatbelt ( See Breed et al. paragraph 0718). It would have been obvious to one of ordinary skill in the art with a reasonable expectation of success to modify the system of Tang et al. with that of Breed et al because it would have achieved a desired result for the classification of the occupant in the vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vasisht et al. (U.S. Patent No. 10140553) disclose an artificial intelligence system for identifying attributes in an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: extracting first features; categorizing the first images in a first group or a second group; modifying first metadata associated with each image in the first images to include a binary label; calculating a classification function; classifying a second plurality of images using the classification function; extracting second features from the second images classified in the first group; categorizing the second images in the first group by attribute; calculating an attribute identification function that identifies attributes of the second images; and identifying at least one attribute associated with a client image using the attribute identification function, the client image being received from the client device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERTRUDE ARTHUR JEANGLAUDE whose telephone number is (571)272-6954. The examiner can normally be reached Monday-Thursday, 7:30-8:00 EST.
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/GERTRUDE ARTHUR JEANGLAUDE/Primary Examiner, Art Unit 3661