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 § 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.
Claim 3 recites the limitation "the data structure" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claims 6 and 9 recites the limitation "the one or more properties" in line 1 respectively. There is insufficient antecedent basis for this limitation in the claims.
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.
Claim 20 is rejected under 35 U.S.C 101. The claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of “One or more computer storage media” encompasses a signal and signals are non-statutory. See MPEP 2106.03 I. Applicant is advised to amend the claim as “One or more non-transitory computer storage media-------” in-order to make the claim statutory.
Allowable Subject Matter
Claim 1 is allowed. Regarding independent claim 1, NPL1 (Flamingo: a Visual language Model for Few-Shot Learning, Jean-Baptiste Alayrac et al., arXiv, Nov 2021, Pages 1-54) hereafter NPL1 discloses “ A method performed by one or more computers and for training a visual language model to identify locations of image elements within a graphical image, the method comprising: generating a plurality of training data items, each training data item including (i) a graphical image rendered according to a corresponding set of instructions (fig 1 shows the graphical images in the input prompt section) (ii) a natural language query for identifying at least one image element of the graphical image (fig 1 shows the text description (natural language query) in the input prompt identifying at least one image element of the graphical image (i.e “This is a Chinchilla--- This is a Shiba. They are very popular in Japan etc meeting the claim limitations), and (iii) a target location for the at least one image element, the target location being determined from the set of instructions (fig 1 shows the third image on the top with the text query “This is” in the input prompt section and the arrow to Completion “a flamingo. They are found in the Caribbean and South America (i.e a target location for the at least one image element in the third image in the input prompt, examiner notes that the specifics of a target location/place are not required by the current claim) meeting the claim limitations); for each of the training data items, processing the corresponding graphical image and natural language query using a visual language model to generate a corresponding model output comprising a predicted location of an image element identified from the natural language query (fig 1 shows the bottom row 4th image on the top (i.e the Graphical image) based on the context “This is cityscape. It looks like Chicago” and with the natural language query “what makes you think this is Chicago?” as the natural language query and the answer “I think it is Chicago because of the Shedd Aquarium in the background (i.e a predicted location for the at least one image element identified from the natural language query), examiner notes that the specifics of a predicted location/place are not required by the current claim) meeting the claim limitations). NPL1 and the other cited arts alone or in combination however fail to disclose “adjusting parameters of the visual language model to optimize, for each of the training data items, an objective function that depends on a comparison between the predicted location of the model output corresponding to the training data item and the target location of the training data item.”, therefore claim 1 is allowed. Dependent claims 2, 4-5, 7-8 and 10-18 depending directly or indirectly on claim 1 are also allowed.
Claim 19 is allowed. Regarding independent claim 19, NPL1 (Flamingo: a Visual language Model for Few-Shot Learning, Jean-Baptiste Alayrac et al., arXiv, Nov 2021, Pages 1-54) hereafter NPL1 discloses “ A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one more computers to perform operations for training a visual language model to identify locations of image elements within a graphical image, the operations comprising: generating a plurality of training data items, each training data item including (i) a graphical image rendered according to a corresponding set of instructions (fig 1 shows the graphical images in the input prompt section) (ii) a natural language query for identifying at least one image element of the graphical image (fig 1 shows the text description (natural language query) in the input prompt identifying at least one image element of the graphical image (i.e “This is a Chinchilla--- This is a Shiba. They are very popular in Japan etc meeting the claim limitations), and (iii) a target location for the at least one image element, the target location being determined from the set of instructions (fig 1 shows the third image on the top with the text query “This is” in the input prompt section and the arrow to Completion “a flamingo. They are found in the Caribbean and South America (i.e a target location for the at least one image element in the third image in the input prompt, examiner notes that the specifics of a target location/place are not required by the current claim) meeting the claim limitations); for each of the training data items, processing the corresponding graphical image and natural language query using a visual language model to generate a corresponding model output comprising a predicted location of an image element identified from the natural language query (fig 1 shows the bottom row 4th image on the top (i.e the Graphical image) based on the context “This is cityscape. It looks like Chicago” and with the natural language query “what makes you think this is Chicago?” as the natural language query and the answer “I think it is Chicago because of the Shedd Aquarium in the background (i.e a predicted location for the at least one image element identified from the natural language query), examiner notes that the specifics of a predicted location/place are not required by the current claim) meeting the claim limitations). NPL1 and the other cited arts alone or in combination however fail to disclose “adjusting parameters of the visual language model to optimize, for each of the training data items, an objective function that depends on a comparison between the predicted location of the model output corresponding to the training data item and the target location of the training data item.”, therefore claim 19 is allowed.
NOTE: Claims 3, 6, 9 and 20 will be allowed after overcoming the 35 U.S.C 112 and 35 U.S.C 101 rejections.
Conclusion
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/JAYESH A PATEL/Primary Examiner, Art Unit 2677
/JAYESH PATEL/
Primary Examiner
Art Unit 2677
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