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 Objections
Claims 7-10 are objected to under 37 CFR 1.75 as being a substantial duplicate of claim 1 since claims 7 and 9 each repeats the same limitation already presented in claim 1. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 1-19 are 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.
Claim 1 recites “acquisition unit to an outside”. It is unclear what this outside refers to. Is it outside of the transmission processing unit, a subsequent network acquisition unit, an image processing device or something else?
Claim 1 appears to recite that the recited “artificial intelligence model” is external to the subsequent network acquisition unit. It further recites that the acquisition unit “selects” one of subsequent networks and these subsequent networks are subsequent to the artificial intelligence model. If so, it is unclear if this AI model is internal or external to the subsequent network acquisition unit. Or can this network be subsequent to the predetermined intermediate layer in the AI which the AI model is external? Clarification/explanation is respectfully requested.
Claim 1 recites “a trained network serving as a base”. It is unclear what it means by serving as a base.
Claims 4 and 5 recite the limitation of “the intermediate feature map is data in which a number of pixels of an image area related to personal information when visualized is less than 64/144 pixels”. It is unclear what the difference between “data” and “image area” is. Is it implying that the data is the image area or that the data includes the image area? Also, it is unclear what it means by “when visualized”. Is the visualization applied to the data, image area or something else?
Claims 11, 12 and 18 are similarly analyzed and rejected as presented in claim 1.
Claims 15 and 16 are similarly analyzed and rejected as presented in claim 4 and 5.
Claim 19 recites “an external device outside a device”. It is unclear if there is any difference between “an external device and “outside a device”.
Claim 19 appears to recite that the recited “artificial intelligence model” is external to the subsequent network acquisition unit. It further recites that the acquisition unit “selects” one of subsequent networks and these subsequent networks are subsequent to the artificial intelligence model. If so, it is unclear if this AI model is internal or external to the subsequent network acquisition unit. Or can this network be subsequent to the predetermined intermediate layer in the AI which the AI model is external? Clarification/explanation is respectfully requested.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-5, 7-16, 18 and 19 are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Park USPGPUB 2022/0156596.
With respect to claim 1, Park discloses an information processing device comprising:
a subsequent network acquisition unit that receives, from an external device, an intermediate feature map obtained in a predetermined intermediate layer of an artificial intelligence model (extracting an image feature map from the trained model in paragraph 50) when input data is given to the artificial intelligence model, which has a neural network and receives detection data from a sensor device as the input data (input image from a camera in paragraph 47), and selects one of subsequent networks from among a plurality of candidate subsequent networks based on the intermediate feature map, the subsequent networks being networks subsequent to the predetermined intermediate layer in the artificial intelligence model (deciding a candidate learning model based on the feature map in paragraphs 12 & 18), or generates one of the subsequent networks based on a trained network serving as a base and the intermediate feature map; and
a transmission processing unit that performs processing of transmitting configuration data of the subsequent network selected (paragraph 75) or generated by the subsequent network acquisition unit to an outside.
With respect to claim 2, Park discloses the information processing device according to claim 1, wherein the intermediate feature map is output data of a second or subsequent intermediate layer in the artificial intelligence model (paragraph 50).
With respect to claim 3, Park discloses the information processing device according to claim 1, wherein the sensor device is an imaging sensor, and the artificial intelligence model is an artificial intelligence model for performing image recognition processing (AI performing the recognition and extraction in paragraph 50) using a captured image obtained by the imaging sensor as input data (input image from a camera in paragraph 47).
With respect to claim 4, Park discloses the information processing device according to claim 3, wherein the intermediate feature map is data in which a number of pixels of an image area related to personal information when visualized is less than 144 pixels (when a pedestrian is detected/recognized, each pixel in the box representing the person relates to personal information in paragraph 47. Since every pixel relates to an image area having personal information, it is said to be less than 64 pixels).
With respect to claim 5, Park discloses the information processing device according to claim 3, wherein the intermediate feature map is data in which a number of pixels of an image area related to personal information when visualized is less than 64 pixels (when a pedestrian is detected/recognized, each pixel in the box representing the person relates to personal information in paragraph 47. Since every pixel relates to an image area having personal information, it is said to be less than 64 pixels).
With respect to claim 7, Park discloses the information processing device according to claim 1, wherein the subsequent network acquisition unit selects one of the subsequent networks from among the plurality of candidate subsequent networks based on the intermediate feature map (deciding a candidate learning model based on the feature map in paragraphs 12 & 18).
With respect to claim 8, Park discloses the information processing device according to claim 7, wherein the plurality of candidate subsequent networks are generated by machine learning as active learning (paragraphs 49~54).
With respect to claim 9, Park discloses the information processing device according to claim 1, wherein the subsequent network acquisition unit generates one of the subsequent networks based on the trained network serving as the base and the intermediate feature map (paragraphs 65, 75 & 79).
With respect to claim 10, Park discloses the information processing device according to claim 9, wherein the subsequent network acquisition unit generates one of the subsequent networks by performing knowledge distillation using the trained network serving as the base as a teacher model based on the intermediate feature map (paragraphs 65, 75 & 79).
With respect to claim 11, arguments analogous to those presented for claim 1, are applicable.
With respect to claim 12, Park discloses an information processing device comprising:
a transmission processing unit that performs processing of transmitting, to an outside, an intermediate feature map obtained in a predetermined intermediate layer of an artificial intelligence model (extracting an image feature map from the trained model in paragraph 50) when input data is given to the artificial intelligence model which has a neural network and receives detection data from a sensor device as the input data (input image from a camera in paragraph 47);
a reception processing unit that performs processing of receiving configuration data of any subsequent network that is either one of subsequent networks selected by an external device from among a plurality of candidate subsequent networks (paragraph 75) based on the intermediate feature map transmitted by the transmission processing unit, the subsequent networks being networks subsequent to the predetermined intermediate layer in the artificial intelligence model, or one of the subsequent networks generated by the external device based on a trained network serving as a base and the intermediate feature map transmitted by the transmission processing unit; and
an inference processing unit that performs inference processing using the subsequent network achieved by the configuration data received by the reception processing unit (paragraphs 65, 75 & 79).
With respect to claim 13, arguments analogous to those presented for claim 2, are applicable.
With respect to claim 14, arguments analogous to those presented for claim 3, are applicable.
With respect to claim 15, arguments analogous to those presented for claim 4, are applicable.
With respect to claim 16, arguments analogous to those presented for claim 5, are applicable.
With respect to claim 18, arguments analogous to those presented for claim 12, are applicable.
With respect to claim 19, Park discloses an information processing system comprising:
a first transmission processing unit that performs processing of transmitting, to an outside, an intermediate feature map obtained in a predetermined intermediate layer of an artificial intelligence model (extracting an image feature map from the trained model in paragraph 50) when input data is given to the artificial intelligence model which has a neural network and receives detection data from a sensor device as the input data (input image from a camera in paragraph 47);
a subsequent network acquisition unit that is provided in an external device outside a device including the first transmission processing unit, and selects one of subsequent networks from among a plurality of candidate subsequent networks based on the intermediate feature map transmitted by the first transmission processing unit, the subsequent networks being networks subsequent to the predetermined intermediate layer in the artificial intelligence model (deciding a candidate learning model based on the feature map in paragraphs 12 & 18), or generates one of the subsequent networks based on a trained network serving as a base and the intermediate feature map transmitted by the first transmission processing unit;
a second transmission processing unit that is provided in the external device outside the device including the first transmission processing unit and performs processing of transmitting configuration data of the subsequent network selected or generated by the subsequent network acquisition unit to an outside (paragraph 75);
a reception processing unit that is provided in the device including the first transmission processing unit and performs processing of receiving the configuration data (paragraphs 65, 75 & 79); and
an inference processing unit that is provided in the device including the first transmission processing unit and performs inference processing using the subsequent network achieved by the configuration data received by the reception processing unit (paragraphs 65, 75 & 79).
Claim Rejections - 35 USC § 103
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over Park as applied to claim 1 above, and further in view of Tan et al. USPGPUB 2022/0156910 (hereinafter Tan).
With respect to claim 6, Park discloses the information processing device according to claim 1, but it does not explicitly disclose that the intermediate feature map is data that is not decodable by a decoding unit of an auto-encoder obtained by self-encoding learning of the artificial intelligence model.
Tan, the same field of endeavor of computer vision algorithm, discloses a system that uses an encoder to generate a feature map that is not decodable by a decoding unit of an auto-encoder obtained by self-encoding learning of the artificial intelligence model (the feature map generated at step S2 goes through steps S3 & S4 before the image gets reconstructed by the decoder in fig. 2. Especially a similar feature map in S4 needs to be generated, not the feature map of S2, in order to convert into the reconstructed image by the decoder).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system of Park to include the autoencoder system of Tan.
The suggestion/motivation for doing so would have been to generate a reconstructed image using the training image data.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Park as applied to claim 12 above, and further in view of Tan.
With respect to claim 17, arguments analogous to those presented for claim 6, are applicable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAN S PARK whose telephone number is (571)272-7409. The examiner can normally be reached Monday-Friday 8:30am-5:00pm.
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/CHAN S PARK/ Supervisory Patent Examiner, Art Unit 2669
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