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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/17/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claim(s)
Claims 3-6 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.
Claim(s) 1, 2 and 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by CALDERARA (WO 2019138329 A1).
Claim Objections
Claims 3-6 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.
Claim Rejections - 35 USC § 102
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 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, 2 and 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by CALDERARA (WO 2019138329 A1).
Regarding Claim 1:
CALDERARA (WO 2019138329 A1) teaches: A method of performing individual identification of a target animal, the method comprising (Page 3, Lines 6-11, “The present invention more particularly relates to the sector of methods and systems for biometric animal recognition which make use of technologies based on artificial intelligence and on computer vision which permit the univocal identification of an animal; more particularly, the present invention uses deep learning techniques to create and manage a biometric database which is capable, when queried, to link to any animal registry details.”):
(a) acquiring a target image related to a back of the target animal (page 15-16, Lines 24-4, “The embeddings of the animal domain 103.2 obtained from the real images, 107, will be stored in the database 1 1 1. The training step starting from images from the animal domain makes use of at least four images of the animal viewed from the right-hand side and at least four images from the left-hand side and four images from a front view of the animal and/or video of the animal. In a preferred embodiment, the images will represent views of the animal's head. In a further embodiment, the images may represent the animal's entire body.”);
(b) determining an embedding vector from the target image by using a deep metric learning model (Page 8-9, Summary of the invention, “In particular, the present invention combines computer vision techniques with deep learning techniques, or those techniques which are part of the field of machine learning by means of neural networks, the functioning of which mimics human learning techniques. More particularly, the present invention makes use of the experience gained in a human context in relation to neural networks trained for facial recognition in order to transfer this knowledge to the animal domain. The present invention provides a method which is made up of the following steps:
a. a training step on a human domain and an animal domain to obtain animal embeddings in a latent space homologous to the human latent space by means of convolutional neural networks”);
(c) calculating a distance between the registered embedding vector and the embedding vector by using registered data including a registered embedding vector generated in advance for each of a plurality of registered individuals (Page 13-14, Lines 16-1, “Thus the method provided by the invention accordingly starts from human embeddings obtained from networks already trained for this task, the convolutional network A 102; these already thoroughly trained networks make it possible to obtain embeddings with distances which are considered valid; in this way, images from the same person will be a small distance apart, and images from different people will be a large distance apart; this defines a human latent space in which the distances between embeddings will be considered valid. The method provided by the present invention is made up of an innovative training step which uses images from both domains (human and animal) and, by means of different convolutional neural networks, makes it possible to transform the embeddings of the human domain into animal images.”); and
(d) determining an individual of the target animal from among the plurality of registered individuals by using the distance (Page 23, lines 21-23, “unambiguous, contactless identification of the animals: a photograph of the animal is taken or a video acquired (even from a distance of some metres) which the system compares with each of the images present in the database on the server and/or in the cloud until an unambiguous identification is made;”).
Regarding Claim 2:
CALDERARA teaches: The method according to claim 1, wherein (a) includes:
(a1) acquiring a back image related to the back of the target animal (page 15-16, Lines 24-4, “The embeddings of the animal domain 103.2 obtained from the real images, 107, will be stored in the database 1 1 1. The training step starting from images from the animal domain makes use of at least four images of the animal viewed from the right-hand side and at least four images from the left-hand side and four images from a front view of the animal and/or video of the animal. In a preferred embodiment, the images will represent views of the animal's head. In a further embodiment, the images may represent the animal's entire body.”);
(a2) detecting a plurality of key points from the back image by using an object recognition model (Page 16-17, Training steps create a graph structure of features of animal and created embedded features for registered animals); and
(a3) generating the target image by cutting out a feature portion of the back from the back image with positions of the plurality of key points as a reference (page 15-16, Lines 24-4, “The embeddings of the animal domain 103.2 obtained from the real images, 107, will be stored in the database 1 1 1. The training step starting from images from the animal domain makes use of at least four images of the animal viewed from the right-hand side and at least four images from the left-hand side and four images from a front view of the animal and/or video of the animal. In a preferred embodiment, the images will represent views of the animal's head. In a further embodiment, the images may represent the animal's entire body.”) [Image 4 further demonstrates a portion of the animal being selected which could be a side or back of the animal].
Regarding Claim 7:
CALDERARA (WO 2019138329 A1) teaches: A non-transitory computer-readable storage medium storing a computer program, the computer program being configured to cause a processor to execute a process of performing individual identification of a target animal, the computer program causing the processor to execute (Figure 4 and Page 21, “The system hence provides a device for acquisition of an image of an animal subject which may be a smartphone or a simple camera; one or more servers will monitor the network training, image processing and database management function for recognising identities. The system 1 also comprises a remote server 7 on which resides a software for operationally interacting with said at least one server 5, with said at least one mobile electronic device 2 or at least one camera 3 connectable to at least one PC 4, desktop or laptop, for training the convolutional neural networks and for processing the images acquired by means of the mobile electronic device and/or a camera and for managing a central database.”) [One of ordinary skill in the art can understand the computer and severs with a database includes non-transitory computer readable medium storages):
(a) a process of acquiring a target image related to a back of the target animal (page 15-16, Lines 24-4, “The embeddings of the animal domain 103.2 obtained from the real images, 107, will be stored in the database 1 1 1. The training step starting from images from the animal domain makes use of at least four images of the animal viewed from the right-hand side and at least four images from the left-hand side and four images from a front view of the animal and/or video of the animal. In a preferred embodiment, the images will represent views of the animal's head. In a further embodiment, the images may represent the animal's entire body.”);
(b) a process of determining an embedding vector from the target image by using a deep metric learning model (Page 8-9, Summary of the invention, “In particular, the present invention combines computer vision techniques with deep learning techniques, or those techniques which are part of the field of machine learning by means of neural networks, the functioning of which mimics human learning techniques. More particularly, the present invention makes use of the experience gained in a human context in relation to neural networks trained for facial recognition in order to transfer this knowledge to the animal domain. The present invention provides a method which is made up of the following steps:
a. a training step on a human domain and an animal domain to obtain animal embeddings in a latent space homologous to the human latent space by means of convolutional neural networks”);
(c) a process of calculating a distance between the registered embedding vector and the embedding vector by using registered data including a registered embedding vector generated in advance for each of a plurality of registered individuals (Page 13-14, Lines 16-1, “Thus the method provided by the invention accordingly starts from human embeddings obtained from networks already trained for this task, the convolutional network A 102; these already thoroughly trained networks make it possible to obtain embeddings with distances which are considered valid; in this way, images from the same person will be a small distance apart, and images from different people will be a large distance apart; this defines a human latent space in which the distances between embeddings will be considered valid. The method provided by the present invention is made up of an innovative training step which uses images from both domains (human and animal) and, by means of different convolutional neural networks, makes it possible to transform the embeddings of the human domain into animal images.”); and
(d) a process of determining an individual of the target animal from among the plurality of registered individuals by using the distance (Page 23, lines 21-23, “unambiguous, contactless identification of the animals: a photograph of the animal is taken or a video acquired (even from a distance of some metres) which the system compares with each of the images present in the database on the server and/or in the cloud until an unambiguous identification is made;”).
Allowable Subject Matter
Claims 3-6 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.
Relevant Prior Art Directed to State of Art
KAMATA (US 20250111692 A1)
Kim (US 11847849 B2)
Holderrieth (US 20240194303 A1)
Khoury (US 9824692 B1)
TAN (CN 114067351 A)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN J SHERRILLO whose telephone number is (703)756-5605. The examiner can normally be reached 1st week of bi-week: Mon-Wed 7am-5:30pm PST, Thurs: 7am-4:30pm PST, Fri off / 2nd week of bi-week: Mon-Wed 7am-5:30pm PST, Thurs-Fri: 7am-4:30pm PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.J.S./Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
