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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1 (and the same for the rest of the claims):
Claim 1 is directed to idea of itself (abstract idea). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Step 1: Claim 1 recites concepts that can be performed entirely in the human mind (observation, evaluation, judgment) and fall squarely under the category of mental processes. Because a human could look at a sign, understand what it means, and translate it, the claim is a mental process, which is unpatentable.
Step 2A, the claim simply says "determining" and does not recite any specific rules, algorithms, or technological mechanisms for interpreting the sign object.
Step 2B, the additional limitations—"determining whether a target image contains a sign object" and "determining a meaning"—merely state the general concept of using a computer or a processor to perform the abstract idea. Thus, claim 1 as a whole is not significantly more than the abstract idea itself and is ineligible as do the dependent and other independent claims.
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gokturk (US 2014/0369626).
As per claims 1, 10, and 18 Gokturk teaches, a method and electronic device and a non-transitory computer readable storage medium for determining a sign meaning, comprising: determining whether a target image contains a sign object (Gokturk, ¶ [0057] “With regard to text carrying objects, the correlation process 40 may correlate recognition information 32 in the form of a string of alphanumeric characters, to a meaning or context, such as to a proper name, classification, brand-name, or dictionary meaning.” This represents determining whether a target image contains a sign object, by being able to tell from an image a meaning or context, see fig.1 image data 10 to object detection 20 and recognition 30 and then correlation information); and determining a meaning represented by the sign object in response to determining that the target image contains the sign object (Gokturk, ¶[0149] “Context and meaning for detected and recognized words may play an important part in a search algorithm. The meaning of the text in the image can be derived from the text tag, possibly in combination with other sources, which can include: (i) other tags extracted from the image, (ii) the image metadata, (iii) context of the image such as web links pointing to it, directory information on the user file system, file name of the image, content of the web page where the image is displayed, (iv) external knowledge sources such as dictionaries, natural language processing software, and (v) input from the user. The interpretation can then be used to enhance the relevance of the search based on the text found in the image.” This represents getting the meaning of the sign object, as these objects can be signs and ¶[0174] “A step of determining context may be performed as an additional, intelligent step of interpretation. One goal of interpretation is to establish the level of relevance of the recognized text to a particular task, function or use. For example, a large sign saying "WELCOME TO SAN FRANCISCO" on a photograph is relevant in determining the location of the event. A small street sign saying "NO PARKING" in the background of the picture might not be relevant to any search query. To establish a measure of relevance, several cues can be used, including but not limited to: the semantics of the text, the text location, size, contrast, and sharpness of focus. Dictionaries and thesauri can be used to determine the possible semantic classes the text belongs to (for example a city database is useful in determining that "San Francisco" is a city name, hence relevant as a location tag).”).
As per claims 2, 11 and 20 Gokturk teaches, the method according to claim 1, wherein the determining a meaning represented by the sign object in response to determining that the target image contains the sign object comprises: determining a category to which the sign object belongs in response to determining that the target image contains the sign object; determining a plurality of sub-sign objects under the category contained in the target image; and determining a meaning respectively represented by the plurality of sub-sign objects (Gokturk, ¶[0187] “Indexing enables functionality such as search and categorizing or sort. Thus, one embodiment provides that the image analysis module 1220 recognizes object from image data for purpose of enabling those object to be the subject of searches, whether performed manually by users, or programmatically by software.” This is the equivalent to sub-sign objects as they are designed to be able to be search queries).
As per claims 3 and 12, Gokturk teaches, the method according to claim 2, wherein the determining a meaning respectively represented by the plurality of sub-sign objects comprises: determining, for each sub-sign object in the plurality of sub-sign objects, a meaning represented by the sub-sign object according to a quantity of the sub-sign object in the target image (Gokturk, ¶[0188] “The use of separate indexes to maintain identifiers based on correlation information and quantitative recognition signatures is a design implementation to facilitate numerous types of functionality, including text searching for images, image search for images, and similarity or likeness searches (described in more detail below).” This represents the sub-sign object according to a quantity of the sub-sign object in the target image, as there are correlation information and quantitative recognition signatures).
As per claims 4 and 13, Gokturk teaches, the method according to claim 2, further comprising: displaying the plurality of sub-sign objects and a plurality of meanings corresponding to the plurality of sign objects on a one-to-one basis (Gokturk, ¶ [0150] As will be further described, related entities can be derived from the text, including: (i) orthographic variations and corrections, possibly based on a spell-checking algorithm, (ii) semantically related words which can broaden the scope of the search query, and (iii) related concepts, products, services, brand names, can be derived from the words to offer alternative search results.” This would represent multiple meanings i to iii. By different ways of spelling and meanings for example as seen).
As per claims 5 and 14, Gokturk teaches, the method according to claim 4, wherein the displaying the plurality of sub-sign objects and a plurality of meanings corresponding to the plurality of sign objects on a one-to-one basis comprises: recalling a plurality of sub-sign object images corresponding to the plurality of sub-sign objects on the one-to-one basis; and displaying the plurality of sub-sign object images and the plurality of meanings in an image-text contrast form (Gokturk, ¶[0149] “Context and meaning for detected and recognized words may play an important part in a search algorithm. The meaning of the text in the image can be derived from the text tag” this represents image text contrast and ¶[0277] “Accordingly, one embodiment provides for images to be displayed to a user in which individual images can be objectified so that recognized objects appearing in the image are capable of being interactive.” Represents being displayed).
As per claims 6 and 15, Gokturk teaches, the method according to claim 5, wherein the recalling a plurality of sub-sign object images corresponding to the plurality of sub-sign objects on the one-to-one basis comprises: recalling, for each sub-sign object in the plurality of sub-sign objects, a sub-sign object image corresponding to the sub-sign object from a preset sign image set in response to determining that the preset sign image set contains the sub-sign object image; and recalling the sub-sign object image corresponding to the sub-sign object from a network image resource in response to determining that the preset sign image set does not contain the sub-sign object image corresponding to the sub-sign object (Gokturk, fig.3, 210 by training there would be re-calling, as well as fig.2 there is correlation which means there is recalling of the sub-sign object image since the image is being correlated).
As per claims 7 and 16, Gokturk teaches, the method according to claim 2, further comprising: displaying, through generating, through a pre-trained artificial intelligence large model summative data for the sign object according to the plurality of sub-sign objects and the plurality of meanings corresponding to the plurality of sub-sign objects on a one-to-one basis (Gokturk, ¶[0304] “ Machine learning may be used to train…” this means that artificial intelligence large model is being used, as well as ¶[0168] “[0168] In one embodiment, a dictionary assist can be used. The words that are not in a dictionary can be eliminated/corrected using the dictionary. A finite automate state machine can be used in order to implement the dictionary.” To define the meanings and further ¶[0067] “In step 210, a training phase is applied where a training set of face and non-face images are collected, and a classification algorithm, such as Support Vector Machines, Neural Networks, or Hidden Markov Models, Adaboost classifiers are trained. The training faces used may accommodate various types of faces or facial markers, including eyes (eyebrows and socket), nose or mouth.” The neural networks represent learning models and the way the claims are written read even on the other type of sign recognition of Gokturk).
As per claims 8 and 17, Gokturk in view of Prabhaker teaches the method according to claim 7, wherein the generating and displaying, through a pre-trained artificial intelligence large model, summative data for the sign object according to the plurality of sub-sign objects and the plurality of meanings corresponding to the plurality of sub-sign objects on the one-to-one basis comprises: determining a prompt word of the artificial intelligence large model according to a scenario represented by the sign object; and generating and displaying, through the artificial intelligence large model, the summative data according to the prompt word, the plurality of sub-sign objects and the plurality of meanings (Gokturk, ¶[0167] “In step 930, the text is interpreted, so as to provide context or meaning. For example, when recognition yields a string of characters, step 930 may interpret the string as a word or set of words. In performing this step, one embodiment may utilize confidence value generated by an OCR algorithm or application. In one embodiment, the letter with the highest confidence is chosen as the final letter. However, such a method may be prone to errors, since some letters look similar to each other. In order to deal with this issue, other context information can be used for word recognition.” This represents summative data according to the prompt word, the plurality of sub-sign objects and the plurality of meanings).
As per claims 9 and 18, Gokturk teaches, the method according to claim 1, further comprising: determining another object in the target image, in response to determining that the target image does not contain the sign object; and displaying the other object and processed data for the other object in a double-column form (Gokturk, ¶[0084] “Under an embodiment, the step of detecting a person or face may be performed as an additional step of recognition. If steps 310-330 are performed and the result of the recognition is a bad signature or recognition (e.g. a signature that does not map to a typical recognition value for a person or face), then the result returned as a result of the recognition may be that no face was detected” there is no definition of sign object, therefore this would represent in response to determining that the target image does not contain the sign object; and displaying the other object and processed data for the other object in a double-column form as there is detected and non-detected and ¶[0067] “In step 210, a training phase is applied where a training set of face and non-face images are collected, and a classification algorithm, such as Support Vector Machines, Neural Networks, or Hidden Markov Models, Adaboost classifiers are trained. The training faces used may accommodate various types of faces or facial markers, including eyes (eyebrows and socket), nose or mouth.” The neural networks represent learning models and the way the claims are written read even on the other type of sign recognition of Gokturk).
Notes: Particular attention should be paid to Prabhaker (US 2023/0022364) Abstract: “A method for extracting sentiments or mood from art images includes: receiving at least one of the art images as an input image; preprocessing the input image; extracting features from the preprocessed input image, the extracting including predicting a color label corresponding to a dominant perceptual color detected from the preprocessed input image a dominant subject from the preprocessed input image, detecting low-level image features from the preprocessed input image, and extracting mood feature information based on a description information included in the input image; classifying the extracted features into a plurality of mood/sentiments classes, using an artificial neural network; and predicting at least one of a mood or a sentiment that is present in the input image based on the dominant perceptual color and the plurality of mood/sentiments classes.” This reference also reads on the claimed limitation. And further deeper and similar usage of the neural networks.
Further Yuan (US 2023/0304823) ¶[0134] “the server 12 obtains images shot by the road cameras 111 at a current moment, then identifies the target vehicle from the images based on the vehicle information of the target vehicle, identifies another sign or object in the images, and compares another sign or object with the high-definition map of the parking lot, to determine a location of the target vehicle relative to the parking lot.” Has to do with vehicles and signs on the road however also reads on the claimed limitations.
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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