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 INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “processing device to” in claim(s) 8-10, 14-17, and 20. See at least ¶¶ [0087], [0098] of the pre-grant publication of the instant application (US 20240037969 A1) for support.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 11,790,675 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the parent recites a method of generating a first and second hypothesis, scoring both, and determining the first score is greater. The instant claims recites a broader method of generating “one or more” hypothesis and “selecting a winning hypothesis.” The instant claim 1 is generic to the species of comparison claim in the ‘675 patent. It would have been obvious to a person of ordinary skill in the art at to broaden the parent patent to the winner selection of the instant application. Therefore, the claims are obvious variations of the patented invention.
18/484110 claims
US 11,970,675 claims
(method) 1, (system) 8, (CRM) 15
1, 9, 15 (broader/obvious variation)
2, 9, 16 (determine language and select corpus)
3, 11, 17 (apply language detection model)
3, 10, 17 (structural classification/similarity)
4, 18 (structural classification/similarity) obvious variation
4, 11 (CEL, CL, CCPL)
8, 14 (same)
5, 12, 18 (input 1: fragment, input 2: geometric)
5, 13 (input 1: grapheme, input 2: geometric)
6, 13, 19 (aspect ratio)
6 (same)
7, 14, 20 (morphological/dictionary/syntactical)
7 (same)
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 4 and 11 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 4 and 11 recite “the loss function comprises one or more of: a cross entropy loss function, a center loss function, or a close-to-center penalty loss function.” The specification (US 2024/0037969 A1) provides support for a cross entropy loss (CEL) with center (CL) [¶54] and CEL with Close-to-Center Penalty Loss (CCPL). However, there is no written description of a CL or a CCPL being used alone.
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 3, 5-6, 10, 12-13, 17-19 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 3 recites “a respective fragment image” however, it is unclear which fragment image, of the plurality, the claim is referring to.
Claim 5, disclose “the one or more fragment images” however, independent claim 1 recites “a plurality of fragment images.” This lacks proper antecedent basis.
Claim 10 mirrors the issue of claim 3, mutatis mutandis.
Claim 12 mirrors the issue of claim 5, mutatis mutandis.
Claim 17 mirrors the issue of claim 3, mutatis mutandis.
Claim 18 mirrors the issue of claim 5, mutatis mutandis.
Claims 6, 13, and 19 depend either directly or indirectly from the objection of claim 5, 12, and 18, therefore they are also objected.
Appropriate correction is required.
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.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claim(s) recite(s) mathematical concepts (confidence score calculations) and mental processes (hypothesis generation, selection). This judicial exception is not integrated into a practical application because claims recite generic "recognition model" and "confidence scores" without the specific implementations (CEL + CL + CCPL loss functions, distance-based confidence from class centers). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because applying neural network recognition models to images for character recognition is well-understood, routine, and conventional.
STEP 1: Statutory Category?
YES - Claim 1 recites a method (process).
STEP 2A, PRONG 1: Judicial Exception?
Limitation
Abstract idea
Grouping
“generating one or more hypotheses … segmenting an
image of a text into a plurality of fragment images”
Mental Process
Could be performed mentally by observing an image and mentally dividing it into segments.
“applying a recognition model to … identify (i) a plurality of symbols … and (ii) a plurality of classification confidence scores”
Mathematical Concept
Confidence scores are mathematical
calculations; neural network
classification involves mathematical
operations.
"determining, using the plurality of classification confidence scores, the fragmentation confidence score"
Mathematical Concept
Mathematical aggregation/calculation
of scores.
“selecting the plurality of
symbols, identified for a winning hypothesis … as a recognized text”
Mental Process
Comparison and selection based on scores could be performed mentally.
The claim recites generating hypotheses segmenting an image into fragments, applying a recognition model to identify symbols and classification confidence scores, determining a fragmentation confidence score using the classification confidence scores, and selecting symbols based on the fragmentation confidence scores. These limitations, under their broadest reasonable interpretation, cover mathematical concepts (calculating confidence scores, aggregating scores to determine fragmentation confidence) and mental processes (generating segmentation hypotheses by mentally dividing an image, selecting a winning hypothesis through mental comparison). These are treated together as a single abstract idea for analysis.
STEP 2A, PRONG 2: Practical Application?
The claim recites the following additional elements beyond the judicial exception:
"an image of a text" – field of use limitation (images)
"fragment images" depicting "one or more words" – data characterization
"a recognition model" – generic ML model recited at high level
The "image of a text" and “fragment images” merely limits the field of use to image processing of handwriting that is not an integration into a practical application. See MPEP 2106.05 (g)&(h). See also Parker v. Flook, 437 U.S. 584 (1978). The "recognition model" is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic machine learning component. See MPEP 2106.05(f) – mere instructions to apply. The claim does not recite the specific technical improvements described in the specification (specialized loss functions, distance-based confidence functions using class centers in feature space) . The additional elements do not integrate the abstract idea into a practical application.
Consideration
Analysis
Improvement to technology? (MPEP 2106.05(a))
Specification describes improved handwriting
recognition, but claim does not reflect the specific technical improvements (specialized loss functions, distance-based confidence function, class center computations). Claim recites generic "recognition model" and generic "confidence scores" without the
technical details that provide the improvement.
Particular machine? (MPEP 2106.05(b))
No particular machine recited; "recognition model" is generic.
Transformation? (MPEP 2106.05(c))
Image data manipulation, but this is data-to-data transformation without physical transformation.
Mere instructions to apply? (MPEP 2106.05(f))
"Applying a recognition model" amounts to generic instruction to use ML to perform the abstract idea.
Insignificant extra-solution activity? (MPEP 2106.05(g))
Yes, receiving image is data gathering; outputting recognized text is data output.
Field of use limitation? (MPEP 2106.05(h))
"Image of a text" and "handwritten" limit to particular field but don't integrate exception.
The additional elements do NOT integrate the judicial exception into a practical application. The claim recites the abstract idea with generic ML components at a high level of generality.
STEP 2B: Significantly More/ Inventive Concept?
The additional elements, considered individually and in combination, do not amount to significantly more than the abstract idea. Applying neural network recognition models to images for character recognition is well-understood, routine, and conventional, as acknowledged by the specification at ¶[0003] and ¶[0017]-[0019]. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018). The claim is not patent eligible.
CLAIM 8 (System):
STEP 1: YES - System (machine) is statutory category.
STEP 2A, Prong 1: Same abstract ideas as claim 1 (mathematical concepts+ mental processes).
STEP 2A, Prong 2: Additional elements:
"a memory" - generic computer component
"a processing device, communicatively coupled to the memory" - generic computer component
"handwritten text" - field of use.
These generic computer components do not integrate the abstract idea into a practical
application. The claim amounts to "apply it" on a generic computer. See MPEP 2106.05(f).
STEP 2B: Generic computer components (memory, processor) are well-understood ,
routine, and conventional. See MPEP 2106.05(d)(II) - "Performing repetitive calculations."
RESULT: Claim 8 is INELIGIBLE.
CLAIM 15 (CRM).
STEP 1: YES - Non-transitory CRM (manufacture) is statutory category.
STEP 2A-2B: Same analysis as claims 1 and 8. The CRM with instructions to perform the abstract idea on a processor does not integrate the exception or provide significantly more.
RESULT: Claim 15 is INELIGIBLE.
DEPENDENT CLAIMS.
Claim(s)
Additional Limitation(s)
Integrates/Significantly More?
Result
2, 9, 16
Language detection model to determine language; select symbols from corpus of determined language
No - additional ML model and data selection; still abstract.
INELIGIBLE
3, 10, 17
Structural classification model for additional confidence scores based on structural similarity.
No - additional mathematical
calculations (confidence scores)
INELIGIBLE
4, 11
Recognition model trained
using cross entropy loss, center loss, or close-to-center penalty loss.
No - describes training method (mathematical optimization) but doesn't change what the claim does at runtime.
INELIGIBLE
5, 12, 18
Input includes fragment images AND geometric features.
No - specifies input data types; insignificant extra-solution activity.
INELIGIBLE
6, 13, 19
Geometric features include aspect ratio for graphemes.
No - specifies particular data (aspect ratio is mathematical ratio).
INELIGIBLE
7, 14, 20
Validating using morphological, dictionary, or syntactical model.
No - additional abstract verification step.
INELIGIBLE
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, 3-5, 7-8, 10-12, 14-15, 17-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Koerich et al. (Recognition and Verification of Unconstrained Handwritten Words – hereinafter “Koerich”).
Claims 1, 8, and 15.
Koerich discloses a system, comprising:
a memory;
a processing device, communicatively coupled to the memory, the processing device to (Koerich disclose a computerized system for handwriting recognition. “The recognition time … was obtained on a PC Athlon 1.1GHz with 512MB of RAM memory” (p. 8, § 5, right column).):
generate one or more hypotheses, each of the one or more hypotheses segmenting
an image of a handwritten text into a plurality of fragment images (Koerich generates multiple (“N-best”) hypothesis where each hypothesis is defined by “segmentation boundaries” that divide the image into segments (“fragment images”). “Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of … segmentation boundaries” (p. 1, Abstract). “Having the segmentation boundaries of each word hypothesis …” (p. 9, § 5.3 , left column)), wherein a fragment image of the plurality of fragment images depicts one or more words of the text (The “text” in Koerich is the word, and the “fragment images” are the character segments that make up that text. “Given a word image … segmentation boundaries of the word hypotheses into characters” (p. 1, Abstract).);
obtain one or more fragmentation confidence scores, each fragmentation confidence score obtained for a respective hypothesis of the one or more hypotheses (The “word confidence score” in Koerich is the “fragmentation confidence score” for the entire hypothesis. “The
verification consists of an estimation of the probability of each … character probabilities are combined to produce word confidence scores … The N-best recognition hypothesis list is reranked based on such composite scores” (p. 1, Abstract).), by:
applying a recognition model to the respective plurality of fragment images to identify (i) a plurality of symbols corresponding to the respective plurality of fragment images (The “a posterior probability” is the classification score for each individual segment (fragment). “segmental neural network (SNN) … The task of the SNN is to assign an a posteriori probability to each segment … The output of the SNN is P(cl|xl), which is the a posteriori probability of the character class cl given the feature vector xl.” (p. 4, § 3.2, right column). “Each recognition hypothesis consists of: a text transcript … given as a sequence of characters” (p. 3, § 2.2, right column).), and (ii) a plurality of classification confidence scores associated with the respective plurality of fragment images (“The verification consists of an estimation of the probability of each segment representing a known class of character.” (p. 1, Abstract). “assign an a posteriori probability to each segment” (p. 4, § 3.2, right column). ); and
determining, using the plurality of classification confidence scores, the fragmentation confidence score for the respective hypothesis (Koerich combines the individual “character probabilities” (classification score) to create the “word confidence score” (fragmentation score). “character probabilities are combined to produce word confidence scores” (p. 1, Abstract).); and
select, using the one or more fragmentation confidence scores, the plurality of symbols, identified for a winning hypothesis of the one or more hypotheses, as a recognized text (Koerich uses the top scores to re-rank and select “TOP 1” (winning) hypothesis as the output text. “The N-best word hypotheses are reranked based on such composite scores … to either accept or reject the best word hypothesis” (p. 2, § 1 , right column).).
Claims 3, 10, and 17.
Koerich teaches the system of claim 8, wherein the processing device is further to:
for each of the one or more hypotheses:
apply a structural classification model to the respective plurality of fragment
images to identify an additional plurality of classification confidence scores
characterizing structural similarity of a respective fragment image to one or more
reference images (Koerich's SNN (Segmental Neural Network) is a structural model. "The isolated characters are represented ... by combining three different types of features: projection histogram ... profiles ... and directional histogram ... " (p. 5, § 3.2, left column). These histograms/profiles characterize the structure of the character shape.); and
wherein the fragmentation confidence score for the respective hypothesis is
further determined using the additional plurality of classification confidence scores (Koerich teaches the final score used to pick the winner is a composite score calculated by taking the initial hypothesis score and updating it with the output by the structural verification model. “Character probabilities are combined to produce word confidence scores which are further integrated with the recognition scores produced by the recognition system. The N-best recognition hypothesis list is reranked based on such composite scores” (p. 1, Abstract).).
Claims 4 and 11.
Koerich teaches the system of claim 8, wherein the recognition model is trained using a loss function, wherein the loss function comprises one or more of:
a cross entropy loss function (Koerich uses a multilayer perceptron (MLP) trained to estimate “a posteriori probabilities” for each character segment. The examiner takes official notice that it is well known in the art that training a neural network to estimate posterior probability utilizes a cross-entropy loss function to minimize error between the predicted probability distribution and the actual target loss. “a multilayer perceptron (MLP) neural network which is called segmental neural network (SNN) … The task of the SNN is to assign an a posteriori probability to each segment” (p. 4, § 3.2).),
a center loss function, or
a close-to-center penalty loss function.
Claims 5, 12, and 18.
Koerich teaches the system of claim 8, wherein an input into the recognition model comprises:
a first input comprising one or more fragment images of the respective plurality of
fragment images (The “text” in Koerich is the word, and the “fragment images” are the character segments that make up that text. “Given a word image … segmentation boundaries of the word hypotheses into characters” (p. 1, Abstract).), and a second input comprising one or more geometric features of the one or more fragment images (In the art of OCR, “profiles” (the contour shape of the character) and “projection histograms” (distribution of pixel density) are geometric features. "The isolated characters are represented ... by combining three different types of features: projection histogram ... profiles ... and directional histogram ... " (p. 5, § 3.2, left column).).
Claims 7, 14, and 20.
Koerich teaches the system of claim 8, wherein the processing device is further to:
validate the plurality of symbols, identified for the winning hypothesis, using one or more
of a morphological model, a dictionary model, or a syntactical model (Koerich’s system validates the hypothesis against the "Lexicon" (Dictionary)."Lexicon-driven word recognition approaches ... " [Section 2, p. 3]. "Verification lexicon containing ASCII strings ... " [Section 1, p. 2].).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 6, 9, 13, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koerich in view of Ghosh et al. (Script Recognition – A Review, hereinafter “Ghosh”).
Claims 2, 9, and 16.
Koerich teaches the system of claim 8, wherein the processing device is further to:
determine, using a language detection model, a language associated with the image; and
select the plurality of symbols corresponding to the respective plurality of fragment images from a corpus of symbols of the determined language (Koerich uses a "Lexicon L" (corpus) to limit recognition (p. 1 & 3, §§ 1 &2). While Koerich typically assumes one language (e.g., French/English), identifying the language (or alphabet) to select the correct Lexicon is inherent in any multi-lingual OCR system or obvious to add (e.g., selecting the "French Dictionary" vs. "English Dictionary").).
Koerich discloses all of the subject matter as described above except for specifically teaching “determine … a language associated with the image; and select … a corpus of symbols of the determined language.” However, Ghosh in the same field of endeavor teaches how to determine … a language associated with the image; and select … a corpus of symbols of the determined language (“In a multi-script, multilingual environment, it is essential to know the script used in writing a document before an appropriate character recognition and document analysis algorithm can be chosen.” (Abstract)).
It would have been obvious to one of ordinary skill in the art to Koerich and Ghosh before the effective filing date of the claimed invention. Koerich teaches using a specific lexicon (corpus) but does not explicitly teach automatically determining the language to select that corpus. Ghosh teaches that in multilingual environments it is standard practice to first determine the “script used” (language detection) in a document image to select the “appropriate character recognition algorithm” or lexicon. It would have been obvious to combine Koerich with Ghosh’s language detection to enable the system to automatically handle multilingual document by dynamically selecting the correct lexicon based on the detected script.
Claims 6, 13, and 19.
The combination of to Koerich and Ghosh discloses the system of claim 12, wherein the one or more geometric features comprise at least one aspect ratio for one or more graphemes in the one or more fragment images (Koerich “horizontal and vertical scalings” (p. 5, § 3.2, right column); Ghosh p. 6 teaches an “aspect ratio”).
It would have been obvious to one of ordinary skill in the art to Koerich and Ghosh before the effective filing date of the claimed invention. Koerich teaches using geometric features such as “horizontal and vertical scalings” (size measurements) but does not explicitly name “aspect ratio.” Ghosh teaches “aspect ratio” is a standard structural feature used in text recognition to discriminate between characters with similar shapes but different proportion. It would have been obvious to include the aspect ratio feature of Ghosh into the feature vector of Koerich to improve the model’s ability to distinguish between wide and narrow character classes.
Conclusion
The prior art made of record but not relied, yet considered pertinent to the applicant’s disclosure, is listed on the PTO-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ross Varndell whose telephone number is (571)270-1922. The examiner can normally be reached M-F, 9-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, O’Neal Mistry can be reached at (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Ross Varndell/Primary Examiner, Art Unit 2674