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 § 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.
Claim(s) 1, 3, 4, 6, 7, 13 and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lau et al. (US 2020/0143349).
In regard to claims 1 and 18, Lau et al. teach an information processing system comprising: circuitry configured to: acquire a character recognition result that is a result of character recognition performed on a target image (fig. 1B element 112); extract, from the character recognition result of the target image, a plurality of candidate character strings that are candidates of an item value of an extraction target item (fig. 14 element 1404 and paragraph 192); generate, for each of the plurality of candidate character strings, a feature quantity based on positional relationships between the candidate character string and a plurality of item keywords in the target image (element 1404 and paragraphs 193-196), the plurality of item keywords being keyword word strings for use in extraction of the item value of the extraction target item (element 1402, 1510a and 1510b); store a trained model in a memory, the trained model being generated through machine learning such that (fig. 11 and paragraph 139), in response to input of a feature quantity based on positional relationships between a character string and the plurality of item keywords in an image, information indicating appropriateness of the character string being the item value of the extraction target item is output (paragraph 142); and input the feature quantity of each of the plurality of candidate character strings in the target image to the trained model so as to extract the item value of the extraction target item from among the plurality of candidate character strings (paragraph 186, after the model is trained it is used as a matcher).
In regard to claim 3, Lau et al. teach wherein the feature quantity includes, for each of the plurality of item keywords, a feature quantity based on information indicating a distance between the target character string and the item keyword, and a feature quantity based on information indicating a direction from one of the target character string and the item keyword toward another one of the target character string and the item keyword (fig. 15, paragraphs 193 and 194. The items and amounts are scored based on the angle and distance).
In regard to claim 4, Lau et al. teach wherein the feature quantity based on the information indicating the distance increases or decreases according to the distance between the target character string and the item keyword (see equations in paragraphs 193 and 194).
In regard to claim 6, Lau et al. teach wherein the circuitry is configured to extract the item value of the extraction target item, based on information indicating a probability of each of the plurality of candidate character strings being the item value of the extraction target item, the information indicating the probability being output from the trained model in response to input of the feature quantity of each of the plurality of candidate character strings in the target image (figs. 14, 15 and paragraphs 192-196).
In regard to claim 7, Lau et al. teach wherein the plurality of item keywords are word strings determined from among word strings included in at least one training image among a plurality of training images used in the machine learning, based on an attribute of each of the word strings (paragraph 191, primary and secondary labels are pre-determined).
In regard to claim 13, Lau et al. teach wherein the circuitry is configured to: store a format definition of the extraction target item in a memory; and extract character strings that match the format definition of the extraction target item, as the plurality of candidate character strings (paragraph 191, primary and secondary labels are pre-determined).
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(s) 2, 14-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lau et al. in view of Yebes Torres et al. (US 2022/0414630).
In regard to claim 2, Lau et al. teach, a feature quantity based on positional relationships between a candidate character string and a plurality of item keywords in the training image with information indicating whether the candidate character string is the item value of the extraction target item (paragraphs 192-196) but does not teach wherein the trained model is generated through machine learning using training data, the training data associating, for each of a plurality of training images of documents having layouts different from one another.
Yebes Torres et al. teach wherein the trained model is generated through machine learning using training data, the training data associating, for each of a plurality of training images of documents having layouts different from one another (figs. 4, 5 and 26. Yebes Torres et al. teach training the machine learning algorithm on multiple types of receipts).
The two are analogous art because they both deal with the same field of invention of image recognition.
Before the effective filing date it would have been obvious to one of ordinary skill in the art to provide the apparatus of Lau et al. with the training date of Yebes Torres et al. The rationale is as follows: Before the effective filing date it would have been obvious to provide the apparatus of Lau et al. with the training date of Yebes Torres et al. because the training date of Yebes Torres et al. would improve the accuracy of the image recognition.
In regard to claims 14 and 19, Lau et al. teach an information processing system comprising: circuitry configured to: acquire a character recognition result that is a result of character recognition (fig. 1B element 112); generate, for each of character strings included in each of the plurality of training images, the character strings including a character string that is an item value of an extraction target item and other character strings (fig. 14 element 1404 and paragraph 192), a feature quantity based on positional relationships between the character string and a plurality of item keywords in the training image (paragraphs 192-195), the plurality of item keywords being keyword word strings for use in extraction of the item value of the extraction target item (element 1402, 1510a and 1510b); and generate a trained model through machine learning (fig. 11 and paragraph 139), the machine learning being performed using training data, the training data associating, for each of the character strings included in each of the plurality of training images, the feature quantity of the character string with information indicating whether the character string is the item value of the extraction target item (paragraph 142) but does not teach performed on a plurality of training images of documents having layouts different from one another.
Yebes Torres et al. teach performing machine learning on a plurality of training images of documents having layouts different from one another (figs. 4, 5 and 26. Yebes Torres et al. teach training the machine learning algorithm on multiple types of receipts).
In regard to claim 15, Lau et al. teach wherein the circuitry is configured to extract, from the character recognition result of each of the plurality of training images, a plurality of candidate character strings that are candidates of the item value of the extraction target item, the plurality of candidate character strings that are extracted being the character string that is the item value of the extraction target item and the other character strings (figs. 14, 15 and paragraphs 192-196).
In regard to claim 16, Yebes Torres et al. teach wherein the circuitry is configured to: acquire a ground truth definition, the ground truth definition associating the extraction target item with the item value of the extraction target item in each of the plurality of training images; and acquire information indicating whether the character string is the item value of the extraction target item, based on the ground truth definition (fig. 10 element 1006 and paragraph 168).
Allowable Subject Matter
Claims 5, 8-12 and 17 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.
The following is an examiner’s statement of reasons for allowance: In regard to claim 5, the prior art fails to teach or make obvious “wherein the feature quantity based on the information indicating the direction increases or decreases according to a degree at which the item keyword is located in a horizontal left direction of the target character string and a degree at which the item keyword is located in a vertical above direction of the target character string” in combination with the claim’s other features.
In regard to claims 8-10, the prior art fails to teach or make obvious the claimed attributes in combination with the claim’s other features.
In regard to claim 12, the prior art fails to teach or make obvious “wherein the word strings are determined as the plurality of item keywords in descending order of effectiveness scores each indicating effectiveness of the word string based on the attribute of the word string” in combination with the claim’s other features.
In regard to claim 17, the prior art fails to teach or make obvious “wherein the circuitry is configured to: display the plurality of candidate character strings extracted for the extraction target item from the character recognition result of each of the plurality of training images, in a method that allows a user to visually recognize that the candidate character strings are candidates for the item value; receive designation of one candidate character string from among the displayed candidate character strings by the user; generate the ground truth definition using the designated one candidate character string as the item value of the extraction target item in the training image; and acquire the generated ground truth definition” in combination with the claim’s other features.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH R HALEY whose telephone number is (571)272-0574. The examiner can normally be reached 7:30am-5pm.
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/JOSEPH R HALEY/ Primary Examiner, Art Unit 2621