DETAILED ACTION
Claims 14,16 and 6-13 and 15 objected to because of the following informalities:
Claims 1-16 not rejected under 35 U.S.C. 101 because the claimed invention is directed to improving the computing field not without significantly more (streamlined analysis) in view of applicant’s disclosure of information processing at [0103]:
Claim(s) 1,5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation.
Claim(s) 1,5 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 further in view of Dai et al. (US 8,105,777 B1):
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 further in view of FAUDEMAY (WO 99/40539 A1) with machine translation:
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation:
Claim(s) 14,16 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 below, further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation as applied in claim 4, above:
Claim(s) 6,12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation:
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claim 6,12,15 further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation:
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Bequet et al. (US 2020/0133977 A1):
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Bequet et al. (US 2020/0133977 A1) as applied in claim 8 further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation as applied in claim 4:
Claim(s) 10,11 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Bequet et al. (US 2020/0133977 A1) as applied in claim 8 further in view of MASAMITSU et al (WO 2020/138479 A1) with machine translation:
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Brun et al. (US 2013/0230257 A1):
PNG
media_image1.png
770
962
media_image1.png
Greyscale
Claim Objections
Claims 14,16 and 6-13 and 15 objected to because of the following informalities:
PNG
media_image2.png
770
373
media_image2.png
Greyscale
Claim 14 is objected like claim 6.
Claim 16 is objected like claim 6.
Claim 6’s last limitation is objected for not having “plural indentations to further segregate subcombinations or related steps” (see (k) CLAIM OR CLAIMS below):
PNG
media_image3.png
453
843
media_image3.png
Greyscale
Thus, claims 7,8,9,10,11,12,13 objected
Claim 15 objected like claim 6.
Appropriate correction is required (see (k) CLAIM OR CLAIMS below):
Content of Specification
(a) TITLE OF THE INVENTION: See 37 CFR 1.72(a) and MPEP § 606. The title of the invention should be placed at the top of the first page of the specification unless the title is provided in an application data sheet. The title of the invention should be brief but technically accurate and descriptive, preferably from two to seven words. It may not contain more than 500 characters.
(b) CROSS-REFERENCES TO RELATED APPLICATIONS: See 37 CFR 1.78 and MPEP § 211 et seq.
(c) STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT: See MPEP § 310.
(d) THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT. See 37 CFR 1.71(g).
(e) INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A READ-ONLY OPTICAL DISC, AS A TEXT FILE OR AN XML FILE VIA THE PATENT ELECTRONIC SYSTEM: The specification is required to include an incorporation-by-reference of electronic documents that are to become part of the permanent United States Patent and Trademark Office records in the file of a patent application. See 37 CFR 1.77(b)(5) and MPEP § 608.05. See also the Legal Framework for Patent Electronic System posted on the USPTO website (https://www.uspto.gov/sites/default/files/documents/2019LegalFrameworkPES.pdf) and MPEP § 502.05
(f) STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT INVENTOR. See 35 U.S.C. 102(b) and 37 CFR 1.77.
(g) BACKGROUND OF THE INVENTION: See MPEP § 608.01(c). The specification should set forth the Background of the Invention in two parts:
(1) Field of the Invention: A statement of the field of art to which the invention pertains. This statement may include a paraphrasing of the applicable U.S. patent classification definitions of the subject matter of the claimed invention. This item may also be titled “Technical Field.”
(2) Description of the Related Art including information disclosed under 37 CFR 1.97 and 37 CFR 1.98: A description of the related art known to the applicant and including, if applicable, references to specific related art and problems involved in the prior art which are solved by the applicant’s invention. This item may also be titled “Background Art.”
(h) BRIEF SUMMARY OF THE INVENTION: See MPEP § 608.01(d). A brief summary or general statement of the invention as set forth in 37 CFR 1.73. The summary is separate and distinct from the abstract and is directed toward the invention rather than the disclosure as a whole. The summary may point out the advantages of the invention or how it solves problems previously existent in the prior art (and preferably indicated in the Background of the Invention). In chemical cases it should point out in general terms the utility of the invention. If possible, the nature and gist of the invention or the inventive concept should be set forth. Objects of the invention should be treated briefly and only to the extent that they contribute to an understanding of the invention.
(i) BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S): See MPEP § 608.01(f). A reference to and brief description of the drawing(s) as set forth in 37 CFR 1.74.
(j) DETAILED DESCRIPTION OF THE INVENTION: See MPEP § 608.01(g). A description of the preferred embodiment(s) of the invention as required in 37 CFR 1.71. The description should be as short and specific as is necessary to describe the invention adequately and accurately. Where elements or groups of elements, compounds, and processes, which are conventional and generally widely known in the field of the invention described, and their exact nature or type is not necessary for an understanding and use of the invention by a person skilled in the art, they should not be described in detail. However, where particularly complicated subject matter is involved or where the elements, compounds, or processes may not be commonly or widely known in the field, the specification should refer to another patent or readily available publication which adequately describes the subject matter.
(k) CLAIM OR CLAIMS: See 37 CFR 1.75 and MPEP § 608.01(m). The claim or claims must commence on a separate sheet or electronic page (37 CFR 1.52(b)(3)). Where a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation. There may be plural indentations to further segregate subcombinations or related steps. See 37 CFR 1.75 and MPEP 608.01(i) - (p).
(l) ABSTRACT OF THE DISCLOSURE: See 37 CFR 1.72 (b) and MPEP § 608.01(b). The abstract is a brief narrative of the disclosure as a whole, as concise as the disclosure permits, in a single paragraph preferably not exceeding 150 words, commencing on a separate sheet following the claims. In an international application which has entered the national stage (37 CFR 1.491(b)), the applicant need not submit an abstract commencing on a separate sheet if an abstract was published with the international application under PCT Article 21. The abstract that appears on the cover page of the pamphlet published by the International Bureau (IB) of the World Intellectual Property Organization (WIPO) is the abstract that will be used by the USPTO. See MPEP § 1893.03(e).
(m) SEQUENCE LISTING: See 37 CFR 1.821 - 1.825 and MPEP §§ 2421 - 2431. The requirement for a sequence listing applies to all sequences disclosed in a given application, whether the sequences are claimed or not. See MPEP § 2422.01.
I see claim 6’s (14,15,16’s) last limitation as this:
until the temporary lowermost-layer classifier satisfies the predetermined condition, repeat12
determination3 of a certain criterion that enables classification of all target objects each determined to belong to a certain category classified by a classifier in a layer immediately above the temporary lowermost-layer classifier,4
determination5 of lower-order categories to which all the target objects respectively belong based on the certain (reference) criterion,6 and
replacement7 of the temporary lowermost-layer classifier with an intermediate-layer classifier constructed based on an image of each of all the target objects belonging to the determined lower-order categories and the lower-order categories;8 and
construction9 of a temporary lowermost-layer classifier that classifies all the target objects belonging to the respective categories into respective pieces of the identification information, based on the image and the identification information of each of all the target objects belonging to the respective categories classified by the intermediate-layer classifier.
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:
“an acquisition unit configured to acquire” in claim 1;
“an output10 device configured to report” in claim 5
“an acquisition unit configured to acquire” in claims 6,13; and
“an acquisition unit configured to acquire” in claim 14.
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 (“acquire”; “report”), and equivalents thereof:
PNG
media_image4.png
239
870
media_image4.png
Greyscale
PNG
media_image5.png
573
981
media_image5.png
Greyscale
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.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure (“circuitry”), materials, or acts (“provide & acquire”) to entirely perform the recited function (“configured to function”; “configured to provide”). Such claim limitation(s) is/are:
“a controller11 configured to function” in claim 1;
“a communication unit configured to provide…and acquire”: acts:
provide & acquire unit in claim 5:
PNG
media_image6.png
301
841
media_image6.png
Greyscale
“a controller12 configured to function” in claims 6,9,10,11,12,13; and
“a controller configured to modify” in claim 14.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-16 not rejected under 35 U.S.C. 101 because the claimed invention is directed to improving the computing13 field not without significantly more (streamlined analysis) in view of applicant’s disclosure of information processing14 at [0103]:
PNG
media_image7.png
1227
1051
media_image7.png
Greyscale
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.
(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.
Claim(s) 1,5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation.
Claim(s) 1,5 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation.
PNG
media_image8.png
768
380
media_image8.png
Greyscale
Re 1. (Original), Hidetoshi discloses A recognition device comprising:
an acquisition unit (“having a radar15 wave”, pg. 7, last txt blk) configured to acquire an (“optical”16, pg. 7, 4th txt blk) image (as for use on weather maps); and
a controller (“in parallel”, pg. 12, 9th txt blk) configured to function as an object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) that estimates a target object (via “extracts, as a case system estimation result, target17 information”, pg. 4, 6th txt blk) appearing in the (radar-weather map) image by causing multiple classifiers18 hierarchized (i.e., hierarchized classifying via “hierarchized” “concepts”19, pg. 4, 3rd txt blk) in multiple layers (forming the “target/ target layer”, pg. 11, 4th txt blk) to classify the target object in order (from 1st to 3rd layers), wherein
the multiple (distinguishing-classifying) classifiers comprise:
an uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies the target object appearing in the (radar) image into any one of multiple (“classification node”, pg. 11, 4th txt blk) categories; and
multiple lower-layer classifiers (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) each of which performs (node) (conceptual-)classification20 of a (“helicopter”-)category21 classified22 by an upper-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) into a lower-order (“reconnaissance helicopter”-)category,
the lower-layer classifiers (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) comprise one or more lowermost-layer (conceptual-nodal) classifiers each of which classifies the target object (via “extracts, as a case system estimation result, target23 information”, pg. 4, 6th txt blk) into the lower-order (“reconnaissance helicopter”-)category which is identification (“result”, pg. 3 [0014], last S) information of the target object, and
a number (three: fig. 7) of layers from the uppermost-layer classifier to the one or more lowermost- layer classifiers is24 different (as shown in fig. 7) between at least two target objects (fig. 7: “Target Purpose”25 twice) having different26 (or “unique”27 having no equal) identification information (“to each model”, pg. 2, 5th txt blk) among (“simulated”, pg. 12, 9th txt blk) target objects (30) estimated (via “extracts, as a case system estimation result, target28 information”, pg. 4, 6th txt blk) by the object recognizer (via “A target identifying apparatus”, pg. 3, [0014] via:
PNG
media_image9.png
1167
1124
media_image9.png
Greyscale
Re 5. (Currently Amended), Hidetoshi discloses A terminal apparatus comprising:
an image capturing (“storage”, pg. 9) unit (2);
a communication unit (or “output”29 “computer”, pg. 5, 3rd txt blk) configured to provide an image generated (“17 is generated. That is…summarized”, pg. 10, 8th txt blk, via fig. 5:17: fig. 6B: integrated summarized/generated image) by the image capturing unit (2) to the recognition device according to claim 1 and acquire the identification information of a target (F-16) object appearing in the (weather-radar) image; and
an output (computer) device configured to report (via transmission) the identification information (as reported)
PNG
media_image10.png
658
1083
media_image10.png
Greyscale
PNG
media_image11.png
1380
862
media_image11.png
Greyscale
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 further in view of Dai et al. (US 8,105,777 B1):
PNG
media_image12.png
768
438
media_image12.png
Greyscale
Re 2. (Original), Hidetoshi teaches The recognition device according to claim 1, wherein a number (as shown in fig. 7) of (airplane) categories (conceptually) classified by at least one or some of the multiple (conceptual) classifiers is (via looking to fig. 7) equal (via “is”) to or less than (relative to each other as shown in fig. 7) a first threshold (“predetermined”, pg. 3 [0014], 1st text blk) value.
Hidetoshi does not teach “first threshold”. Dai teaches “first threshold”, c. 37,ll. 37-45. Since Hidetoshi teaches similarity, one of skill in the art of similarity can make Hidetoshi’s be as Dai’s predictably recognizing the change “that results in the fewest misclassifications”, Dai, c. 37,ll. 37-45.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 further in view of FAUDEMAY (WO 99/40539 A1) with machine translation:
PNG
media_image13.png
768
540
media_image13.png
Greyscale
Re 3. (Currently Amended), Hidetoshi teaches The recognition device according to claim 1 st txt blk: fig. 3:ST2: “Perform time series/integration processing on the monitoring records to generate unconfirmed target records”) in a classified category (via fig. 3:ST3: a classification step) of a (conceptual) feature quantity used for classification by at least one or some of the multiple (conceptual-node) classifiers is equal to or less than (relative to each other as shown in fig. 7) a second threshold (“predetermined”, pg. 3 [0014], 1st text blk) value (via:
PNG
media_image14.png
1436
833
media_image14.png
Greyscale
Hidetoshi does not teach “second threshold”.
FAUDEMAY teaches a second threshold (“with a neighboring region”, pg. 3, 8th txt blk, as shown in figure 1: the sky).
Since Hidetoshi teaches similarity and airplanes, one of skill in the art of similarity and airplanes can make Hidetoshi’s be as FAUDEMAY’s (“airplane in the sky”, pg. 10, 5th txt blk) predictably recognizing the change “to segment the images into significant objects” “with significant semantic value”, pg. 3, 4th txt blk, providing significant semantic meaning (via a radar “image” “label”, FAUDEMAY, pg. 14, 9th txt blk) to different types of aircraft in radar images like a weather map:
PNG
media_image15.png
2758
862
media_image15.png
Greyscale
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation:
PNG
media_image16.png
768
540
media_image16.png
Greyscale
Re 4. (Currently Amended), Hidetoshi teaches The recognition device according to claim 1, wherein a correct answer (“improved” “identification”, pg. 2, 4th txt blk) rate (“of the characteristic values”, pg. 11, 2nd txt blk) of all target (purpose) objects (figs. 5,8: D5a,30) classified by at least one or some of the one or more lowermost-layer classifiers (i.e., six hierarchized classifying via “hierarchized” “concepts”30, pg. 4, 3rd txt blk) is equal to or more than (relative to four upper conceptual classifications in fig. 7) a third threshold (“predetermined”, pg. 3 [0014], 1st text blk) value (via:
PNG
media_image17.png
1206
1082
media_image17.png
Greyscale
PNG
media_image9.png
1167
1124
media_image9.png
Greyscale
Hidetoshi does not teach “correct answer…third threshold”. Mamoru teaches:
correct answer (“rate from 100%”, pg. 4, 4th txt blk)…third threshold (“value of the classification probability”, pg., 16, last txt blk). Since Hidetoshi teaches an improved identification rate (represented in fig. 7 as target purpose) via classification nodes, one of skill in the art of classification can make Hidetoshi’s classification be as Mamoru’s predictably recognizing the change “setting…a highly accurate classification result”, Mamoru, pg. 17, 5th txt blk, due to advancement of technology creating new airplanes and thus new document31 label-names (like F-16) of airplanes in addition to said “F15” and “B747”:
PNG
media_image18.png
1167
1123
media_image18.png
Greyscale
Claim(s) 14,16 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 below, further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation as applied in claim 4, above:
PNG
media_image19.png
770
794
media_image19.png
Greyscale
Claim 14 is rejected like claim 6 (reproduced below) with the exception of “new… modify… new… new…new”:
Re 14. (Currently Amended), Hidetoshi of the combination of HIDETOSHI, ZHU, JEAN-PIERRE teaches A recognizer modifying apparatus comprising:
An (optical-radar) acquisition unit configured to acquire at least an (optical) image and identification information (via said ID rate) of a new target (airplane) object; and
a controller (in parallel) configured to modify the object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) in the recognition device according to claim 1 by using the new target (airplane) object, wherein
the (parallel) controller is configured to:
cause the identification information (via said ID rate) of the new target (airplane) object to be estimated (via “extracts, as a case system estimation result, target32 information”, pg. 4, 6th txt blk) based on the (optical-radar) image by using the object recognizer (via “A target identifying apparatus”, pg. 3, [0014]);
specify a lowermost-layer (F-15) classifier that has classified (node-clustered) the identification information (ID as a tree);
replace a temporary lowermost-layer (F-15) classifier with the lowermost-layer (F-16) classifier, the temporary lowermost-layer (F-15) classifier being constructed based on the (Computer-Vision: CV) image and the identification (rate) information of each of all target (airplane) objects (in the tree) and the new target (airplane) object that are (node) classified by the lowermost-layer (F-17) classifier;
fix the temporary lowermost-layer (B747) classifier as a lowermost-layer (B-747) classifier when the temporary lowermost-layer (F-15) classifier satisfies (“shape characteristics”, pg. 11, 2nd txt blk) a predetermined condition; and
until the temporary lowermost-layer (F-17) classifier satisfies (“shape characteristics”, pg. 11, 2nd txt blk) the predetermined condition, repeat
determination of a certain criterion (said “predetermined reference”, pg. 11, 2nd txt blk) that enables classification of all target objects each determined to belong to a certain category classified by a classifier in a layer immediately above the temporary lowermost-layer classifier,
determination of (third) lower-order categories to which all the target objects respectively belong based on the certain criterion, and
replacement of the temporary lowermost-layer classifier with an (second) intermediate-layer classifier constructed based on an image of each of all the target objects belonging to the determined lower-order categories and the lower-order categories; and
construction of a temporary lowermost-layer (quality) classifier that classifies all the target objects belonging to the respective categories into respective pieces of the identification information, based on the image and the identification information of each of all the target objects belonging to the respective categories classified by the intermediate-layer classifier (via:
Re 6. (Original), HIDETOSHI teaches An information processing apparatus (as indicated by the information connections in fig. 7) that constructs an object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) that estimates identification information (via “extracts, as a case system estimation result, target information”, pg. 4, 6th txt blk) identifying a target object appearing in an image by causing multiple classifiers hierarchized (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) in multiple layers (forming the “target/ target layer”, pg. 11, 4th txt blk) to classify the target object in order (from 1st to 3rd layers), the recognizer constructing apparatus (as indicated by the information connections in fig. 7) comprising:
an acquisition (“processor”, pg. 12, 9th txt blk) unit configured to acquire at least an image and identification information of each of multiple target objects; and
a controller (“processor”, pg. 12, 9th txt blk) configured to construct multiple classifiers (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk), based on the image and the identification (rate) information of each of the multiple (“simulated”, pg. 12, 9th txt blk) target objects (30), wherein
the multiple (distinguishing-classifying) classifiers comprise:
an uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies, based on the (optical-radar) image acquired by the acquisition unit, the (“simulated”, pg. 12, 9th txt blk) target object in the (optical-radar) image into a (“classification node”, pg. 11, 4th txt blk) category; and
a lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies the (“simulated”, pg. 12, 9th txt blk) target object (via “extracts, as a case system estimation result, target information”, pg. 4, 6th txt blk) belonging to a (“helicopter”-)category classified by an upper-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) into any piece (or parts as shown in fig. 7) of the identification (rate) information, and
the controller (“processor”, pg. 12, 9th txt blk) is configured to:
construct the uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that determines, based on an initial (“reference”, pg. 11, 2nd txt blk) criterion, categories to which the multiple (“simulated”, pg. 12, 9th txt blk) target objects (30) respectively belong, and that classifies the (“simulated”, pg. 12, 9th txt blk) target objects (30) to the determined categories;
construct, based on the image and the identification (rate) information of each of all the (“simulated”, pg. 12, 9th txt blk) target objects (30) belonging to the respective categories classified by the uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk), a temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies all the (“simulated”, pg. 12, 9th txt blk) target objects belonging to the respective categories into respective pieces (or parts as shown in fig. 7) of the identification (rate) information;
fix the temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) as a lowermost-layer classifier when the temporary lowermost-layer classifier satisfies (“shape characteristics”, pg. 11, 2nd txt blk) a predetermined condition; and
until the temporary lowermost-layer classifier (shape-feature-)satisfies the predetermined condition, repeat
determination of a certain (said “predetermined reference”, pg. 11, 2nd txt blk) criterion that enables classification of all (airplane) target objects each determined to belong to a certain (node) category classified by a (quality) classifier in a (top) layer immediately above the temporary (third) lowermost-layer (quality) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk),
determination of (third) lower-order categories to which all the target (airplane) objects respectively belong based on the certain (reference) criterion, and
replacement of the temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) with an (second) intermediate-layer classifier (between the 1st and 2nd quality classifiers: fig. 7) constructed based on an (airplane) image of each of all the target objects belonging to the determined lower-order (node) categories and the lower-order categories; and
construction of a temporary lowermost-layer (quality) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies all the target (airplane) objects (by quality-features) belonging to the respective categories into respective pieces (or parts as shown in fig. 7) of the identification (rate) information, based on the (airplane) image and the identification (rate) information of each of all the target (airplane) objects belonging to the respective (node) categories classified by the (second) intermediate-layer (quality-feature) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk).
Hidetoshi of the combination of HIDETOSHI, ZHU, JEAN-PIERRE does not teach the additional differences relative to claim 6 of:
new (target object)…
modify (the object recognizer)…
new (target object)…
new (target object)…
new (target object).
Mamoru teaches the additional difference of claim 14:
new (“technology”, pg. 7, 4th txt blk; “terms”, pg. 13, last txt blk; “term…new group of the ‘New Technology Information’ category”, pg. 17, 5th txt blk) (target object)…
modify (“By first setting33 a flag”, pg. 17, 5th txt blk) (the object recognizer)…
new (technology) (target object)…
new (category) (target object)…
new (term) (target object).
Similar to claim 4, Since Hidetoshi teaches an improved identification rate (represented in fig. 7 as target purpose) via classification nodes, one of skill in the art of classification can make Hidetoshi’s classification be as Mamoru’s predictably recognizing the change “setting…a highly accurate classification result”, Mamoru, pg. 17, 5th txt blk, due to advancement of technology creating new airplanes and thus new document34 label-names (like F-16) of airplanes in addition to said “F15” and “B747”:
PNG
media_image18.png
1167
1123
media_image18.png
Greyscale
Claim 16 is rejected like claim 6 except for the additional differences of “new… modifying…new…modifying …new…new”:
16. (Currently Amended) A modification method comprising:
acquiring at least an (Computer Vision) image and (improved-rate) identification information of a new target (airplane) object; and
modifying the object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) in the recognition device according to claim 1 by using the new target object, wherein
the modifying of the (F-16) object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) comprises:
causing the identification (rate) information of the new target (F-16) object to be estimated (via “extracts, as a case system estimation result, target35 information”, pg. 4, 6th txt blk) based on the (camera) image by using the object recognizer (via “A target identifying apparatus”, pg. 3, [0014]);
specifying a lowermost-layer (F-15) classifier that has classified the identification (rate) information;
replacing a temporary lowermost-layer (F-15) classifier with the lowermost-layer (F-16) classifier, the temporary lowermost-layer (F-15) classifier being constructed based on the (CV) image and the identification (rate) information of each of all target (tree) objects (fig. 7) and the new target (F-16) object that are (node) classified by the lowermost-layer (F-15) classifier;
fixing the temporary lowermost-layer (quality) classifier as a lowermost-layer (F-14) classifier when36 the temporary lowermost-layer (F-15) classifier satisfies (“shape characteristics”, pg. 11, 2nd txt blk) a predetermined condition; and until37 the temporary lowermost-layer classifier satisfies the predetermined condition, repeating3839
determination of a certain criterion that enables classification of all target objects each determined to belong to a certain category classified by a classifier in a layer immediately above the temporary lowermost-layer classifier,
determination of lower-order categories to which all the target objects respectively belong based on the certain criterion, and
replacement of the temporary lowermost-layer classifier with an intermediate-layer classifier constructed based on an image of each of all the target objects belonging to the determined lower-order categories and the lower-order categories; and
construction of a temporary lowermost-layer classifier that classifies all the target objects belonging to the respective categories into respective pieces of the identification information, based on the image and the identification information of each of all the target objects belonging to the respective categories classified by the intermediate-layer classifier (via the rejection of claim 6, re-reproduced below:
Re 6. (Original), HIDETOSHI teaches An information processing apparatus (as indicated by the information connections in fig. 7) that constructs an object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) that estimates identification information (via “extracts, as a case system estimation result, target information”, pg. 4, 6th txt blk) identifying a target object appearing in an image by causing multiple classifiers hierarchized (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) in multiple layers (forming the “target/ target layer”, pg. 11, 4th txt blk) to classify the target object in order (from 1st to 3rd layers), the recognizer constructing apparatus (as indicated by the information connections in fig. 7) comprising:
an acquisition (“processor”, pg. 12, 9th txt blk) unit configured to acquire at least an image and identification information of each of multiple target objects; and
a controller (“processor”, pg. 12, 9th txt blk) configured to construct multiple classifiers (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk), based on the image and the identification (rate) information of each of the multiple (“simulated”, pg. 12, 9th txt blk) target objects (30), wherein
the multiple (distinguishing-classifying) classifiers comprise:
an uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies, based on the (optical-radar) image acquired by the acquisition unit, the (“simulated”, pg. 12, 9th txt blk) target object in the (optical-radar) image into a (“classification node”, pg. 11, 4th txt blk) category; and
a lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies the (“simulated”, pg. 12, 9th txt blk) target object (via “extracts, as a case system estimation result, target information”, pg. 4, 6th txt blk) belonging to a (“helicopter”-)category classified by an upper-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) into any piece (or parts as shown in fig. 7) of the identification (rate) information, and
the controller (“processor”, pg. 12, 9th txt blk) is configured to:
construct the uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that determines, based on an initial (“reference”, pg. 11, 2nd txt blk) criterion, categories to which the multiple (“simulated”, pg. 12, 9th txt blk) target objects (30) respectively belong, and that classifies the (“simulated”, pg. 12, 9th txt blk) target objects (30) to the determined categories;
construct, based on the image and the identification (rate) information of each of all the (“simulated”, pg. 12, 9th txt blk) target objects (30) belonging to the respective categories classified by the uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk), a temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies all the (“simulated”, pg. 12, 9th txt blk) target objects belonging to the respective categories into respective pieces (or parts as shown in fig. 7) of the identification (rate) information;
fix the temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) as a lowermost-layer classifier when the temporary lowermost-layer classifier satisfies (“shape characteristics”, pg. 11, 2nd txt blk) a predetermined condition; and
until the temporary lowermost-layer classifier (shape-feature-)satisfies the predetermined condition, repeat
determination of a certain (said “predetermined reference”, pg. 11, 2nd txt blk) criterion that enables classification of all (airplane) target objects each determined to belong to a certain (node) category classified by a (quality) classifier in a (top) layer immediately above the temporary (third) lowermost-layer (quality) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk),
determination of (third) lower-order categories to which all the target (airplane) objects respectively belong based on the certain (reference) criterion, and
replacement of the temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) with an (second) intermediate-layer classifier (between the 1st and 2nd quality classifiers: fig. 7) constructed based on an (airplane) image of each of all the target objects belonging to the determined lower-order (node) categories and the lower-order categories; and
construction of a temporary lowermost-layer (quality) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies all the target (airplane) objects (by quality-features) belonging to the respective categories into respective pieces (or parts as shown in fig. 7) of the identification (rate) information, based on the (airplane) image and the identification (rate) information of each of all the target (airplane) objects belonging to the respective (node) categories classified by the (second) intermediate-layer (quality-feature) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk).
Hidetoshi of the combination of HIDETOSHI, ZHU, JEAN-PIERRE does not teach the additional differences in claim 16 relative to claim 6:
“new (target object)…
modifying (the object recognizer)…
new (target object)…
modifying (of the (F-16) object recognizer) …
new (target object)…
new (target object)”.
Mamoru teaches the additional difference of claim 16:
new (“technology”, pg. 7, 4th txt blk; “terms”, pg. 13, last txt blk; “term…new group of the ‘New Technology Information’ category”, pg. 17, 5th txt blk) (target object)…
modifying (“By first setting40 a flag”, pg. 17, 5th txt blk) (the object recognizer)…
new (target object)…
modifying (“By first setting41 a flag”, pg. 17, 5th txt blk) (of the (F-16) object recognizer) …
new (target object)…
new (target object).
Similar to claim 4, Since Hidetoshi teaches an improved identification rate (represented in fig. 7 as target purpose) via classification nodes, one of skill in the art of classification can make Hidetoshi’s classification be as Mamoru’s predictably recognizing the change “setting…a highly accurate classification result”, Mamoru, pg. 17, 5th txt blk, due to advancement of technology creating new airplanes and thus new document42 label-names (like F-16) of airplanes in addition to said “F15” and “B747”:
Claim(s) 6,12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation:
PNG
media_image20.png
770
795
media_image20.png
Greyscale
Claim 6 is rejected like claim 1:
Re 6. (Original), HIDETOSHI teaches An information processing apparatus (as indicated by the information connections in fig. 7) that constructs43 an object recognizer (via “A target identifying apparatus”, pg. 3, [0014]) that estimates identification information (via “extracts, as a case system estimation result, target44 information”, pg. 4, 6th txt blk) identifying a target object appearing in an image by causing multiple classifiers hierarchized (i.e., hierarchized classifying via “hierarchized” “concepts”45, pg. 4, 3rd txt blk) in multiple layers (forming the “target/ target layer”, pg. 11, 4th txt blk) to classify the target object in order (from 1st to 3rd layers), the recognizer constructing46 apparatus (as indicated by the information connections in fig. 7) comprising:
an acquisition (“processor”, pg. 12, 9th txt blk) unit configured to acquire at least an image and identification information of each of multiple target objects; and
a controller (“processor”, pg. 12, 9th txt blk) configured to construct47 multiple classifiers (i.e., hierarchized classifying via “hierarchized” “concepts”48, pg. 4, 3rd txt blk), based on the image and the identification (rate) information of each of the multiple (“simulated”, pg. 12, 9th txt blk) target objects (30), wherein
the multiple (distinguishing-classifying) classifiers comprise:
an uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies, based on the (optical-radar) image acquired by the acquisition unit, the (“simulated”, pg. 12, 9th txt blk) target object in the (optical-radar) image into a (“classification node”, pg. 11, 4th txt blk) category; and
a lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies the (“simulated”, pg. 12, 9th txt blk) target object (via “extracts, as a case system estimation result, target49 information”, pg. 4, 6th txt blk) belonging to a (“helicopter”-)category classified by an upper-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) into any piece (or parts as shown in fig. 7) of the identification (rate) information, and
the controller (“processor”, pg. 12, 9th txt blk) is configured to:
construct the uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that determines, based on an50 initial (“reference”, pg. 11, 2nd txt blk) criterion, categories to which the multiple (“simulated”, pg. 12, 9th txt blk) target objects (30) respectively belong, and that classifies the (“simulated”, pg. 12, 9th txt blk) target objects (30) to the determined categories;
construct, based on the image and the identification (rate) information of each of all the (“simulated”, pg. 12, 9th txt blk) target objects (30) belonging to the respective categories classified by the uppermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk), a temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies all the (“simulated”, pg. 12, 9th txt blk) target objects belonging to the respective categories into respective pieces (or parts as shown in fig. 7) of the identification (rate) information;
fix the temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) as a lowermost-layer classifier when the temporary lowermost-layer classifier satisfies (“shape characteristics”, pg. 11, 2nd txt blk) a predetermined condition; and
until the temporary lowermost-layer classifier (shape-feature-)satisfies the predetermined condition, repeat
determination of a51 certain (said “predetermined52 reference”, pg. 11, 2nd txt blk) criterion that enables classification of all (airplane) target objects each determined to belong to a certain (node) category classified by a (quality53) classifier in a (top) layer immediately above the temporary (third) lowermost-layer (quality) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”54, pg. 4, 3rd txt blk),
determination of (third) lower-order categories to which all the target (airplane) objects respectively belong based on the certain (reference) criterion, and
replacement of the temporary lowermost-layer classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) with an (second) intermediate-layer classifier (between the 1st and 2nd quality classifiers: fig. 7) constructed based on an (airplane) image of each of all the target objects belonging to the determined lower-order (node) categories and the lower-order categories; and
construction of a temporary lowermost-layer (quality) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”, pg. 4, 3rd txt blk) that classifies all the target (airplane) objects (by quality-features) belonging to the respective categories into respective pieces (or parts as shown in fig. 7) of the identification (rate) information, based on the (airplane) image and the identification (rate) information of each of all the target (airplane) objects belonging to the respective (node) categories classified by the (second) intermediate-layer (quality-feature) classifier (i.e., hierarchized classifying via “hierarchized” “concepts”55, pg. 4, 3rd txt blk).
HIDETOSHI does not teach the difference56 of claim 6:
“constructs …
constructing …
construct …
construct …
construct…
temporary …
fix …
temporary …
as …
when …
temporary …
a predetermined condition…
until …
temporary…
the predetermined condition, repeat …
temporary …
replacement …
temporary …
constructed …
construction of a temporary”.
ZHU teaches the additional difference of claim 15:
constructs …
(“classifier”) constructing (“method”, pg. 2 [0007], in fig. 1, below) …
(“continuing to”) construct (“the next one lower stage of node classifier”, abstract) …
construct (“the lower stage of node classifier” [0009]) …
construct (“the next one lower stage node classifier”, pg. 9, 1st txt blk)…
(“the”) temporary (“cascade classifier”, abstract) …
fix …
(“the”) temporary (“cascade classifier”, abstract) …
as …
when …
(“form new”) temporary (“cascade classifier”, abstract) …
satisfies a predetermined condition; and until …
(“the”) temporary (“cascade classifier”, abstract)…
satisfies the predetermined condition, repeat …
(“the final”) temporary (“cascade classifier”, abstract) …
replacement …
(“the”) temporary (“cascade classifier”, abstract) …
(“there is provided a cascade classifier” [0010]) constructed …
construction of a temporary (“node classifier”, pg. 2 [0009], “as the temporary cascade classifier” [0009]).
PNG
media_image21.png
1318
829
media_image21.png
Greyscale
Since Hidetoshi teaches node classification, one of skill in the art of node classification can make Hidetoshi’s be as ZHU’s predictable recognizing the change “improving the precision of recognition”, ZHU [0037]:
PNG
media_image22.png
1446
1682
media_image22.png
Greyscale
The combination of HIDETOSHI,ZHU does not teach the remaining difference of claim 15:
fix …
as …
when …
satisfies a predetermined condition; and until …
satisfies the predetermined condition, repeat …
replacement.
PIERRE teaches the remaining difference of claim 15:
fix (“the learning” resulting in “fixed” “learning”, pg. 4, 10th txt blk) …
as …
when (“is satisfactory”, pg. 6, 5th txt blk) …
satisfies a predetermined condition (or “not strongly non-linear (threshold57)” “input/output response”, pg. 5, 1st txt blk: understood as an output response satisfying this varies-continuously-and-not-strong-non-linear-conditional-threshold in order to stay weakly non-linear during the learning phase as indicated by the curve in fig. 3:
PNG
media_image23.png
662
661
media_image23.png
Greyscale
; and
(“repeat” “The same mechanisms58” ) until (“homogeneous classes are obtained, for example the classes A,B,C,D,E in Figure 1”, pg. 4, 7th txt blk:
PNG
media_image24.png
479
784
media_image24.png
Greyscale
…
satisfies the predetermined (weakly non-linear) condition, repeat (“until homogeneous classes are obtained, for example the classes A,B,C,D,E in Figure 1”, above)…
replacement (“by a trio”, pg. 6, 5th txt blk:
PNG
media_image25.png
1177
1421
media_image25.png
Greyscale
Since Hidetoshi of the combination of Hidetoshi, ZHU teaches an hierarchy, one of skill in hierarchies can make Hidetoshi’s (fig. 7) of the combination of Hidetoshi and ZHU be as PIERRE’s (fig. 1) predictably recognizing the change “provides a substantial improvement…in the recognition rate”, PIERRE, pg. 8, 8th txt blk:
PNG
media_image26.png
1663
1121
media_image26.png
Greyscale
Accordingly, the combination of Hidetoshi, ZHU, PIERRE coincidentally teaches the claimed (the temporary59 lowermost-layer classifier60) “as”61 (a lowermost-layer classifier62).
Re 12. (Currently Amended), HIDETOSHI of the combination of HIDETOSHI, ZHU, JEAN-PIERRE teaches The recognizer constructing apparatus according to claim 6, wherein the controller (“in parallel”, pg. 12, 9th txt blk) is configured to determine the initial (or “predetermined reference63”, pg. 11, 2nd txt blk) criterion and at least a part of the certain (or said “predetermined reference64”, pg. 11, 2nd txt blk) criterion by clustering65 (resulting in said nodes66: fig. 7).
Claim 15 is rejected like claim 6:
Re 15. (Original), Hidetoshi teaches A construction (“target identifying”, pg. 1) method for an object recognizer that estimates identification information identifying a target (airplane) object appearing in an (radar-)image by causing multiple (conceptual-node) classifiers hierarchized in multiple layers (three) to classify the target (airplane) object in order (from top-layer one to bottom-layer three), the construction (“target identifying”, pg. 1) method comprising:
acquiring (via fig. 1: 2: “Target Purpose Accumulation Department”) at least an image and identification information of each of multiple target objects; and
constructing multiple (conceptual) classifiers, based on the image and the identification information of each of the multiple target objects, wherein
the multiple (conceptual) classifiers comprise (via fig. 7):
an uppermost-layer classifier that classifies, based on the image acquired in the acquiring, the target object in the image into a (airplane) category; and
a lowermost-layer classifier that classifies the target (airplane) object belonging to a (airplane) category classified by an (top) upper-layer (1st of 3) classifier into any piece (of fig. 7) of the (airplane) identification information, and
the constructing of the multiple (conceptual) classifiers comprises:
determining, based on an initial (or “predetermined reference67”, pg. 11, 2nd txt blk) criterion, (airplane) categories to which the multiple (integrated) target objects respectively belong;
constructing the uppermost-layer classifier that (conceptually) classifies the (summarized) target (airplane) objects to the determined (airplane) categories;
constructing, based on the (radar) image and the identification information of each of all the target objects (as shown in fig. 7) belonging to the respective categories (quality) classified by the (top) uppermost-layer classifier, a temporary lowermost-layer (conceptual) classifier that classifies (via said distinguishing quality68) all the target (airplane) objects belonging to the respective (airplane) categories into respective pieces (or parts as shown in fig. 7) of the (airplane) identification information;
fixing69 the temporary lowermost-layer classifier as a lowermost-layer classifier when70 the temporary lowermost-layer classifier satisfies a predetermined condition; and until71 the temporary lowermost-layer classifier satisfies the predetermined condition, repeating72
determination of a certain criterion that enables classification of all target objects each determined to belong to a certain category classified by a classifier in a layer immediately above the temporary lowermost-layer classifier,
determination of lower-order categories to which all the target objects respectively belong based on the certain criterion, and
replacement of the temporary lowermost-layer classifier with an intermediate-layer classifier constructed based on an image of each of all the target objects belonging to the determined lower-order categories and the lower-order categories; and
construction of a temporary lowermost-layer classifier that classifies all the target objects belonging to the respective categories into respective pieces of the identification information, based on the image and the identification information of each of all the target objects belonging to the respective categories classified by the intermediate-layer classifier.
Like the rejection of claim 6, HIDETOSHI does not teach the additional difference of claim 15:
“construction…
construction…
constructing…
constructing…
constructing…
constructing…
temporary…
fixing…temporary…as…when…temporary…satisfies a predetermined condition; and until…temporary…satisfies the predetermined condition73,
repeating74…
temporary…
replacement…
temporary…
constructed…
construction…
temporary”.
Like the rejection of claim 6, ZHU teaches the additional difference of claim 15:
construction (“cost is high” [0006])…
construction…
(“classifier”) constructing (“method” [0007])…
constructing (“a cascade classifier” [0008])…
constructing (“cascade classifier” [0009])…
constructing (“first stage node classifier” [0009])…
(“constructing first stage node classifier as the”) temporary (“cascade classifier” [0009] in fig. 1, below)…
temporary…
fixing…temporary…as…when…temporary…satisfies a predetermined condition; and until…temporary…satisfies the predetermined condition,
repeating…
temporary…
replacement…
temporary…
constructed…
construction…
temporary.
PNG
media_image21.png
1318
829
media_image21.png
Greyscale
Like the rejection of claim 6, Since Hidetoshi teaches node classification, one of skill in the art of node classification can make Hidetoshi’s be as ZHU’s predictable recognizing the change “improving the precision of recognition”, ZHU [0037]:
PNG
media_image22.png
1446
1682
media_image22.png
Greyscale
Like the rejection of claim 6, The combination of HIDETOSHI,ZHU does not teach the remaining difference of claim 15:
fixing…as…when…satisfies a predetermined condition; and until…satisfies the predetermined condition,
repeating…
replacement…
As in the rejection of claim 6, PIERRE teaches the remaining difference of claim 15:
fix (“the learning” resulting in “fixed” “learning”, pg. 4, 10th txt blk) …
as …
when (“is satisfactory”, pg. 6, 5th txt blk) …
satisfies a predetermined condition (or “not strongly non-linear (threshold75)” “input/output response”, pg. 5, 1st txt blk: understood as an output response satisfying this varies-continuously-and-not-strong-non-linear-conditional-threshold in order to stay weakly non-linear during the learning phase as indicated by the curve in fig. 3:
PNG
media_image23.png
662
661
media_image23.png
Greyscale
(“repeat” “The same mechanisms76” ) until (“homogeneous classes are obtained, for example the classes A,B,C,D,E in Figure 1”, pg. 4, 7th txt blk:
PNG
media_image24.png
479
784
media_image24.png
Greyscale
satisfies the predetermined (weakly non-linear) condition, repeat (“until homogeneous classes are obtained, for example the classes A,B,C,D,E in Figure 1”, above)…
replacement (“by a trio”, pg. 6, 5th txt blk:
PNG
media_image25.png
1177
1421
media_image25.png
Greyscale
As in the rejection of claim 6, Since Hidetoshi of the combination of Hidetoshi, ZHU teaches an hierarchy, one of skill in hierarchies can make Hidetoshi’s (fig. 7) of the combination of Hidetoshi and ZHU be as PIERRE’s (fig. 1) predictably recognizing the change “provides a substantial improvement…in the recognition rate”, PIERRE, pg. 8, 8th txt blk:
PNG
media_image26.png
1663
1121
media_image26.png
Greyscale
Accordingly, the combination of Hidetoshi, ZHU, PIERRE coincidentally teaches the claimed (the temporary77 lowermost-layer classifier78) “as”79 (a lowermost-layer classifier80).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claim 6,12,15 further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation:
PNG
media_image27.png
770
795
media_image27.png
Greyscale
Claim 7 is rejected like claim 4:
Re 7. (Original), Hidetoshi of the combination of Hidetoshi, ZHU, PIERRE teaches The recognizer constructing apparatus according to claim 6, wherein the predetermined (threshold) condition is that81 a correct answer (identification) rate of a (airplane) target object classified by the (cascade) temporary lowermost (F16)-layer (quality) classifier is equal to or more than (relative to the nodes fig. 7) a third threshold (predetermined) value (via rejection of claim 4:
Re 4. (Currently Amended), Hidetoshi teaches The recognition device according to claim 1, wherein a correct answer (“improved” “identification”, pg. 2, 4th txt blk) rate (“of the characteristic values”, pg. 11, 2nd txt blk) of all target (purpose) objects (figs. 5,8: D5a,30) classified by at least one or some of the one or more lowermost-layer classifiers (i.e., six hierarchized classifying via “hierarchized” “concepts”82, pg. 4, 3rd txt blk) is equal to or more than (relative to four upper conceptual classifications in fig. 7) a third threshold (“predetermined”, pg. 3 [0014], 1st text blk) value.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Bequet et al. (US 2020/0133977 A1):
PNG
media_image28.png
770
795
media_image28.png
Greyscale
Re 8. (Original), Hidetoshi of the combination of Hidetoshi,ZHU,PIERRE teaches The recognizer constructing apparatus according to claim 6, wherein the predetermined condition (or “not strongly non-linear (threshold )” “input/output response”, pg. 5, 1st txt blk: understood as an output response satisfying this varies-continuously-and-not-strong-non-linear-conditional-threshold in order to stay weakly non-linear during the learning phase as indicated by the curve in fig. 3) is83 at least one84 of
(A) a first condition85 that86 a number of pieces (in fig. 7) of identification information classified by the temporary lowermost-layer (quality) classifier is equal to or less than (relative to itself, fig. 7) a first threshold value, and
(B) a second condition87 that88 a degree of variation (via “a predetermined difference”, pg. 11, 1st txt blk: fig. 3:ST2: “Perform time series/integration processing on the monitoring records to generate unconfirmed target records”) in a (node-)category corresponding to the temporary lowermost-layer (quality) classifier of a (airplane) feature quantity used for (node-)classification by a (quality) classifier in a (top) layer (fig. 7) immediately above the temporary lowermost-layer (F-16 quality) classifier is equal to or less than (relative to the node of fig. 7) a second threshold value (via:
Hidetoshi of the combination of Hidetoshi,ZHU,PIERRE does not teach the Markush element (A AND B) of:
first (condition)[Markush alternative A]…
first (threshold) [Markush alternative A]…
second (condition) [Markush alternative B]…
second (threshold) [Markush alternative B]…
Bequet teaches Markush alternative A:
first (condition)[Markush alternative A] (“first stage”, “neural network” “conditions” [0535] 1st S:
PNG
media_image29.png
229
868
media_image29.png
Greyscale
first (threshold) [Markush alternative A] (“a first threshold” in claim 5).
Since Pierre of the combination of Hidetoshi,ZHU,PIERRE teaches a neural network, one of skill in the art of networks can make Pierre’s of the combination of Hidetoshi,ZHU,PIERRE be as Bequet’s predictably recognizing the change “to improve accuracy”, Bequet [0264] 1st S. Thus the Markush “element”89--A AND B--is “taught”.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Bequet et al. (US 2020/0133977 A1) as applied in claim 8 further in view of MAMORU et al. (KR 10-2010-0100933 A) with SEARCH machine translation as applied in claim 4:
PNG
media_image30.png
770
920
media_image30.png
Greyscale
Re 9. (Original), Hidetoshi of the combination Hidetoshi, ZHU, PIERRE, Bequet teaches The recognizer constructing apparatus according to claim 8, wherein the controller (“in parallel”, pg. 12, 9th txt blk) is configured to apply, to determination of fixing (resulting in “the learning” resulting in “fixed” “learning”, PIERRE: pg. 4, 10th txt blk) as the lowermost layer classifier, the predetermined condition (or “not strongly non-linear (threshold90)” “input/output response”, PIERRE: pg. 5, 1st txt blk) having91
(A) a higher correct answer (identification) rate among a correct answer (identification) rate of a (airplane) target object (node) classified by the temporary lowermost-layer (quality) classifier when92 the predetermined condition (or “not strongly non-linear (threshold93)” “input/output response”, PIERRE: pg. 5, 1st txt blk) is
the first condition (“first stage”, “neural network” “conditions” [0535] 1st S) and94
(B) a correct answer (ID) rate of a (airplane) target object classified by the temporary lowermost-layer (quality) classifier when95 the predetermined condition (or “not strongly non-linear (threshold96)” “input/output response”) is the second (sky-threshold) condition (“with a neighboring region”, FUADEMAY: pg. 3, 8th txt blk, via).
Hidethoshi of the combination Hidetoshi, ZHU, PIERRE, Bequet does not teach the Markush element of A AND B of:
“higher correct answer (Markush alternative A)…
correct answer (Markush alternative A)…
correct answer (Markush alternative B)”.
MAMORU teaches Markush alternative (A) of:
higher (or “larger”, pg. 16, penult txt blk) correct answer (Markush alternative A) )…
correct answer (that is smaller “0.35” than the larger answer “0.5” pg. 16, penult txt blk) (Markush alternative A).
Similar to rejection of claim 4, since Hidetoshi of the combination Hidetoshi, ZHU, PIERRE, Bequet teaches an improved identification rate (represented in fig. 7 as target purpose) via classification nodes, one of skill in the art of classification can make Hidetoshi’s of the combination Hidetoshi, ZHU, PIERRE, Bequet be as Mamoru’s predictably recognizing the change “setting…a highly accurate classification result” due to advancement of technology creating new airplanes and thus new document97 label-names (like F-16) of airplanes in addition to said “F15” and “B747”:
PNG
media_image18.png
1167
1123
media_image18.png
Greyscale
The combination HIDETOSHI, ZHU, PIERRE,Bequet, MAMORU:
PNG
media_image31.png
3154
1682
media_image31.png
Greyscale
Claim(s) 10,11 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Bequet et al. (US 2020/0133977 A1) as applied in claim 8 further in view of MASAMITSU et al (WO 2020/138479 A1) with machine translation:
PNG
media_image32.png
770
962
media_image32.png
Greyscale
Re 10. (Original), HIDETOSHI of combination HIDETOSHI, ZHU, PIERRE,Bequet teaches The recognizer constructing apparatus according to claim 8, wherein the controller (“in parallel”, pg. 12, 9th txt blk) is configured to, when98 a correct answer (ID) rate of a (airplane) target object (node) classified by the lowermost-layer (F-16) classifier after (A) the first (training-ending) condition99100 (or “first stage”, “neural network” “conditions”, Bequet: [0535] 1st S) is satisfied (or “has been met”, Bequet: [0505], penult S) is lower than a correct answer (improved ID) rate of a target (airplane) object (node) classified by the temporary lowermost-layer (quality) classifier before (A) the first condition101 (or “first stage”, “neural network” “conditions”, Bequet: [0535] 1st S) is satisfied, stop construction of an intermediate-layer (quality-node) classifier for satisfying (A) the first (ending-training) condition102 and construction of a lowermost-layer (F-16) classifier in a layer below the intermediate-layer (quality-node) classifier.
HIDETOSHI of the combination of HIDETOSHI, ZHU, PIERRE,Bequet does not teach:
“when…
correct answer (rate) …
is lower than a correct answer (rate)…
stop construction”.
MASAMITSU teaches:
when…
correct answer (rate) (“is shown in FIG.” 9, pg. 26, 1st txt blk:
PNG
media_image33.png
969
1133
media_image33.png
Greyscale
is lower than a correct answer (rate) (as shown by the dips in figure 9)…
stop construction (such that “the construction of the discriminant model ends”, pg. 11, 2nd txt blk, last S).
Since PIERRE of the combination of HIDETOSHI, ZHU, PIERRE,Bequet teaches a neural network, one of skill in the art of neural networks can make PIERRE’s of the combination of HIDETOSHI, ZHU, PIERRE,Bequet be as MASAMITSU’s predictably recognizing the change “optimizing a non-linear optimizing processing algorithm that maximizes use of CPU memory”, MASAMITSU, pg. 17, 7th txt blk, thus optimizing PEERRE’s weak/strong non-linear threshold.
Claim 11 is rejected like claim 10:
Re 11. (Currently Amended), HIDETOSHI of the combination of HIDETOSHI,ZHU,JEAN-PIERRE, Bequet teaches The recognizer constructing apparatus according to claim 8 th txt blk) is configured to, when103 a correct answer (ID) rate of a (airplane) target object (quality) classified by the lowermost-layer (F-15) classifier after (B) the second condition104 is satisfied is lower than a correct answer (ID) rate of a target (airplane) object classified by the temporary lowermost-layer (quality) classifier before (B) the second condition105 is satisfied, stop construction of an intermediate-layer (node) classifier for satisfying (B) the second condition106 and construction of a lowermost-layer (F-16) classifier in a layer below the intermediate-layer (node) classifier107.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over HIDETOSHI (JP 2003-279647 A) with SEARCH machine translation as applied in claim 1 in view of ZHU et al. (CN 101964059 A) with machine translation further in view of JEAN-PIERRE et al. (FR 2703804 A1) with machine translation as applied in claims 6,12,15 further in view of Brun et al. (US 2013/0230257 A1):
PNG
media_image1.png
770
962
media_image1.png
Greyscale
Re 13. (Currently Amended), HIDETOSHI of the combination of HIDETOSHI, ZHU, JEAN-PIERRE teaches The recognizer constructing apparatus according to claim 6, wherein the acquisition unit (“having a radar108 wave”, pg. 7, last txt blk) is configured to further acquire an (“information109 d1”, pg. 2, 3rd txt blk) instruction (given to a computer) to determine a classification criterion, and the controller (“in parallel”, pg. 12, 9th txt blk) is configured to determine the initial (or “predetermined reference110”, pg. 11, 2nd txt blk) criterion and at least a part of the certain (or said “predetermined reference111”, pg. 11, 2nd txt blk) criterion, based on the (“information112 d1”, pg. 2, 3rd txt blk) instruction acquired (or given) by the acquisition unit (“having a radar113 wave”, pg. 7, last txt blk).
HIDETOSHI of the combination of HIDETOSHI, ZHU, JEAN-PIERRE does not teach “a classification criterion”.
Brun teaches:
a classification criterion (or “maximized”, “Akaike information”, “classification” “criterion” [0129, penult S]).
Since HIDETOSHI of the combination of HIDETOSHI, ZHU, JEAN-PIERRE teaches classification based on the integrated image, one of skill in the art of classification can make HIDETOSHI’s image classification of the combination of HIDETOSHI, ZHU, JEAN-PIERRE be as Brun’s predictably recognizing the change “to yield high quality results”, Brun [0132] 2nd S.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
AMEMURA (US 2019/0392505 A1) corresponds to (IDS 12-7-2023) JP 2020-021378 A: cited as an X-reference in the Incoming ISR, 237 and References from IB
Amemura teaches an appearance of an hierarchy (figs. 13,17) and a terminal: “[0092] An item information acquisition system 100 of a first variation is different from the item information acquisition system 100 of the above-described embodiment in that a terminal 6 includes an image capturing section 2, an electric circuit 3, and a secondary battery 4 as illustrated in FIGS. 8A to 8C.” as the closest to the claimed “hierarchized” of claim 1 and applicant’s disclosed terminal:” [0014] As illustrated in FIG. 1, an information processing system 11 including a terminal apparatus 10 including a recognition device according to an embodiment of the present disclosure may include at least one terminal apparatus 10, a network 12, and an information processing apparatus (recognizer constructing apparatus, recognizer modifying apparatus) 13. In the present embodiment, the information processing system 11 includes multiple terminal apparatuses 10. Each terminal apparatus 10 and the information processing apparatus 13 may communicate with each other via the network 12.”
SABE et al. (EP 1 967 984 B1) corresponding to Sabe et al. (US 7,630,525 B2)
SABE teaches ““Based on the fact that a region of an eye has a lower luminance value than a region of the cheek, it is possible to estimate at a certain probability whether the input image of a human face (object of interest) 138 corresponds to a face or not (positive or negative) based on an output value of the rectangular feature 154B.” and claims “a plurality of nodes hierarchically arranged in a tree structure beginning at an uppermost node among the nodes” and “An example of combiner that combines outputs of weak hypotheses with fixed weights irrespective of input is boosting.” and “Alternatively, classification may be based on a plurality of thresholds.” as the closest to the claimed “estimates a target object…hierarchized” of claim 1 and “fix the temporal lowermost-layer classifier” of claim 6 and the 1st,2nd, 3rd thresholds of claims 2,3,4.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM 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, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/ Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 “repeat” is a combination-step of sub-combination parts (parts 1-4 as shown below)
2 “repeat” suggests a colon: the sign (:) used to mark a major division in a sentence, to indicate that what follows is an elaboration, summation, implication, etc., of what precedes (i.e., the “temporary lowermost-layer classifier” limitation); or to separate groups of numbers referring to different things, as hours from minutes in 5:30; or the members of a ratio or proportion, as in 1 : 2 = 3 : 6. (Dictionary.com)
3 Part 1
4 comma: the sign (,), a mark of punctuation used for indicating a division in a sentence, as in setting off a word, phrase, or clause, especially when such a division is accompanied by a slight pause or is to be noted in order to give order to the sequential elements of the sentence. It is also used to separate items in a list, to mark off thousands in numerals, to separate types or levels of information in bibliographic and other data, and, in many European countries, as a decimal point. (Dictionary.com)
5 Part 2
6 comma: the sign (,), a mark of punctuation used for indicating a division in a sentence, as in setting off a word, phrase, or clause, especially when such a division is accompanied by a slight pause or is to be noted in order to give order to the sequential elements of the sentence. It is also used to separate items in a list, to mark off thousands in numerals, to separate types or levels of information in bibliographic and other data, and, in many European countries, as a decimal point. (Dictionary.com)
7 Part 3
8 semicolon: the punctuation mark (;) used to indicate a major division in a sentence where a more distinct separation is felt between clauses or items on a list than is indicated by a comma, as between the two clauses of a compound sentence. (Dictionary.com)
9 Part 4
10 output: the act of turning out (Dictionary.com)
11 controller: Also called control unit, processor. Computers., the key component of a device, as a terminal, printer, or external storage unit, that contains the circuitry necessary to interpret and execute instructions fed into the device.
12 controller: Also called control unit, processor. Computers., the key component of a device, as a terminal, printer, or external storage unit, that contains the circuitry necessary to interpret and execute instructions fed into the device.
13 computing: the activity of using computers and writing programs for them (Dictionary.com)
14 information processing: computing the combined processing (via the claimed ”hierarchized” “image” (information in the form of an image via the claimed “recognition”, wherein recognition is defined: the automated conversion of information, as words or images, into a form that can be processed by a machine, especially a computer or computerized device (the claimed “controller” of claim 1). (Dictionary.com)) of claim 1 and “information” “hierarchized” of claims 6 and 15) of numerical data, graphics, text, etc (Dictionary.com)
15 radar: A method of detecting distant objects and determining their position, speed, material composition, or other characteristics by causing radio waves to be reflected from them and analyzing the reflected waves. The waves can be converted into images, as for use on weather maps. (Dictionary.com)
16 optical: of or relating to sight or vision, wherein vision is defined: computer vision, wherein computer vision is defined: Digital Technology. a robot analogue of human vision in which information about the environment is received by one or more video cameras and processed by computer: used in navigation by robots, in the control of automated production lines, etc. (Dictionary.com)
17 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
18 classifier: a person or thing that classifies. (Dictionary.com)
19 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
20 classification: the act of classifying (airplanes) (Dictionary.com)
21 category: any general or comprehensive division; a (airplane-)class (Dictionary.com)
22 classified: arranged or distributed in (airplane-) classes or according to (airplane-)class (Dictionary.com)
23 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
24 “is” essentially means look at a figure (Dictionary.com)
25 purpose: a fixed design, outcome, or idea that is the object of an action or other effort (Dictionary.com)
26 different: not ordinary; unusual (Dictionary.com)
27 unique: not typical; unusual (Dictionary.com)
28 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
29 output: Computers. information in a form suitable for transmission from internal to external units of a computer, or to an outside medium, wherein transmission is defined: the act or process of transmitting, wherein transmit is defined: to communicate, as information or news, wherein as is defined: in the role, function, or status of, wherein news is defined: information reported. (Dictionary.com)
30 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
31 document: a piece of text or text and (radar) graphics stored in a computer as a file for manipulation by document processing software, wherein (radar) graphics is defined: The representation of data in a way that includes (radar) images in addition to or instead of text. Computer-aided design, typesetting, and video games, for example, involve the use of graphics. (Dictionary.com)
32 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
33 set: to adjust (a mechanism) so as to control its performance, wherein adjust is defined: to change (something) so that it fits, corresponds, or conforms; adapt; accommodate, wherein adapt is defined: to make suitable to requirements or conditions; adjust or modify fittingly. (Dictionary.com)
34 document: a piece of text or text and (radar) graphics stored in a computer as a file for manipulation by document processing software, wherein (radar) graphics is defined: The representation of data in a way that includes (radar) images in addition to or instead of text. Computer-aided design, typesetting, and video games, for example, involve the use of graphics. (Dictionary.com)
35 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
36 This is an unsatisfied contingent limitation in method claim 16 according to device (system) claim 1
37 This is a continuation of the unsatisfied contingent limitation in method claim 16 according to device (system) claim 1
38 repeating: present participle: Grammar., a participle (“repeating”), in English having the suffix -ing, that expresses repetition or duration (“until the temporary lowermost-layer classifier satisfies the predetermined condition”) of an activity or event: used as an adjective, as in the growing weeds and the setting sun, and also in forming progressive verb constructions, as in The weeds are growing and The sun was setting. (Dictionary.com)
39 Given the unsatisfied contingent limitation, repeating continues regardless of the unsatisfied contingent limitation in method claim 16.
40 set: to adjust (a mechanism) so as to control its performance, wherein adjust is defined: to change (something) so that it fits, corresponds, or conforms; adapt; accommodate, wherein adapt is defined: to make suitable to requirements or conditions; adjust or modify fittingly. (Dictionary.com)
41 set: to adjust (a mechanism) so as to control its performance, wherein adjust is defined: to change (something) so that it fits, corresponds, or conforms; adapt; accommodate, wherein adapt is defined: to make suitable to requirements or conditions; adjust or modify fittingly. (Dictionary.com)
42 document: a piece of text or text and (radar) graphics stored in a computer as a file for manipulation by document processing software, wherein (radar) graphics is defined: The representation of data in a way that includes (radar) images in addition to or instead of text. Computer-aided design, typesetting, and video games, for example, involve the use of graphics. (Dictionary.com)
43 Addressed in rejection of claim 15
44 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
45 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
46 Addressed in rejection of claim 15
47 Addressed in rejection of claim 15
48 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
49 target: electronics an object to be detected by the reflection of a radar or sonar signal, etc (Dictionary.com)
50 an: the form of a before an initial vowel sound (an arch; an honor ) and sometimes, especially in British English, before an initial unstressed syllable beginning with a silent or weakly pronounced h, wherein a is defined: any; a single (Dictionary.com)
51 a: any; a single (Dictionary.com)
52 predetermined: to determine beforehand: (tr) to ascertain or conclude, esp after observation or consideration, wherein ascertain is defined: to determine or discover definitely, wherein definitely is defined: in a definite manner, wherein definite is defined: known for certain (Dictionary.com)
53 wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
54 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
55 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
56 The ellipses (…) represent suppressed claim limitations that are already taught above in claim 6, wherein ellipsis (…) is defined: Printing., a mark or marks as ——, …, or * * *, to indicate an omission or suppression of letters or words (or already taught claim limitations) (Dictionary.com)
57 threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein point is defined: a specific condition or degree (Dictionary.com)
58 mechanism: A) routine methods or procedures; mechanics; B) a process or technique, esp of execution. (Dictionary.com)
59 As taught by ZHU
60 As taught by Hidetoshi
61 as: in the role, function, or status of (Dictionary.com)
62 As taught by Hidetoshi
63 reference: a fact forming the basis of an evaluation or assessment; criterion (Dictionary.com)
64 reference: a fact forming the basis of an evaluation or assessment; criterion (Dictionary.com)
65 cluster: to form a cluster or clusters. (Dictionary.com)
66 node: Computers. a data point or cluster within a tree or other information structure, as defined by its relationship to another data point or cluster:
The nodes in an artificial neural network are connected to each other to send and receive information, much like the neurons of the human brain.
The directory node governs several constituent files.
The nodes in an artificial neural network are connected to each other to send and receive information, much like the neurons of the human brain.(Dictionary.com)
67 reference: a fact forming the basis of an evaluation or assessment; criterion (Dictionary.com)
68 wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
69 “fixing” does not occur in method claim 15 since the required condition—“satisfies a predetermined condition”--has not been met in claim 15
70 “when” is an unsatisfied contingent limitation in method claim 15: thus evidence need not be shown for this contingency under the broadest reasonable interpretation of claim 15
71 “until” is a continuation of the unsatisfied contingent limitation in method claim 15: thus evidence need not be shown for this contingency under the broadest reasonable interpretation of claim 15
72 The claimed “repeating” occurs in claim 15 since the required condition—“satisfies a predetermined condition”--has not been met in claim 15; thus, “repeating” further limits claim 15
73 This “fixing” limitation as shown in italics does not occur in method claim 15 since the required condition—“satisfies a predetermined condition”--has not been met in claim 15
74 The claimed “repeating” occurs in claim 15 since the required condition—“satisfies a predetermined condition”--has not been met in claim 15; thus, “repeating” further limits claim 15
75 threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein point is defined: a specific condition or degree (Dictionary.com)
76 mechanism: A) routine methods or procedures; mechanics; B) a process or technique, esp of execution. (Dictionary.com)
77 As taught by ZHU
78 As taught by Hidetoshi
79 as: in the role, function, or status of (Dictionary.com)
80 As taught by Hidetoshi
81 that adverb: to a great extent or degree; very (Dictionary.com)
82 concept: an idea of something (an airplane) formed by mentally combining (as shown in fig. 7) all its (airplane) characteristics or particulars; a construct, wherein (airplane) characteristic is defined: a distinguishing feature or (airplane) quality, wherein (airplane) quality is defined: (airplane) character or nature, as belonging to or distinguishing a thing (an airplane as “reconnaissance helicopter”: fig .7), wherein distinguishing is defined: to divide into classes; classify. (Dictionary.com)
83 is: be: (copula) used as a linking verb between the subject-- the predetermined condition-- of a sentence (claim 8) and its noun or adjective complement or complementing phrase. In this case be expresses the relationship of either essential or incidental equivalence or identity ( John is a man; John is a musician ) or specifies an essential or incidental attribute ( honey is sweet; Susan is angry ). It is also used with an adverbial complement to indicate a relationship of location in space or time ( Bill is at the office; the dance is on Saturday ) (Dictionary.com)
84 Markush element follows: A AND B
85 If there is a threshold, then there is a condition since threshold comprises condition (Dictionary.com)
86 that conjunction 1. (used to introduce a subordinate clause-- a number of pieces of identification information classified by the temporary lowermost-layer classifier is equal to or less than a first threshold value -- as the subject or object of the principal verb (“is”) or as the necessary complement to a statement made, or a clause expressing cause or reason, purpose or aim, result or consequence, etc.). I'm sure that you'll like it. That he will come is certain. Hold it up so that everyone can see it. (Dictionary.com)
87 If there is a second/first threshold, then there is a second/first condition since threshold comprises “condition”
88 that conjunction 1. (used to introduce a subordinate clause-- a number of pieces of identification information classified by the temporary lowermost-layer classifier is equal to or less than a first threshold value -- as the subject or object of the principal verb (“is”) or as the necessary complement to a statement made, or a clause expressing cause or reason, purpose or aim, result or consequence, etc.). I'm sure that you'll like it. That he will come is certain. Hold it up so that everyone can see it. (Dictionary.com)
89 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two Ss:In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
90 threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein point is defined: a specific condition or degree (Dictionary.com)
91 Said Markush element follows/continues in claim 9: A AND B
92 Indicates a further modifying contingent limitation with structure (“controller”)
93 threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein point is defined: a specific condition or degree (Dictionary.com)
94 And: (used to connect alternatives). (Dictionary.com)
95 Indicates a continent limitation
96 threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein point is defined: a specific condition or degree (Dictionary.com)
97 document: a piece of text or text and (radar) graphics stored in a computer as a file for manipulation by document processing software, wherein (radar) graphics is defined: The representation of data in a way that includes (radar) images in addition to or instead of text. Computer-aided design, typesetting, and video games, for example, involve the use of graphics. (Dictionary.com)
98 A further modifying contingent limitation with structure (“controller”) in system claim 10
99 The “first condition” is of the Markush element A AND B, thus claim 10 is a continuation of Markush alternative A.
100 This is Markush alternative A of Markush element A AND B
101 This is Markush alternative A of Markush element A AND B
102 This is Markush alternative A of Markush element A AND B
103 A further modifying contingent limitation with structure (“controller”) in system claim 11
104 This is Markush alternative B of Markush element A AND B
105 This is Markush alternative B of Markush element A AND B
106 This is Markush alternative B of Markush element A AND B
107 Given that Markush alternative A is “taught” in claims 8,10 of Markush “element” [A AND B], the Markush “element” [A AND B] is “taught” (i.e., Markush “alternative” B is “taught”) via:
MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two Ss:In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
108 radar: A method of detecting distant objects and determining their position, speed, material composition, or other characteristics by causing radio waves to be reflected from them and analyzing the reflected waves. The waves can be converted into images, as for use on weather maps. (Dictionary.com)
109 information: Computers. important or useful facts obtained as output from a computer by means of processing input data with a program, wherein program is defined: A series of instructions given to a computer to direct it to carry out certain operations. (Dictionary.com)
110 reference: a fact forming the basis of an evaluation or assessment; criterion (Dictionary.com)
111 reference: a fact forming the basis of an evaluation or assessment; criterion (Dictionary.com)
112 information: Computers. important or useful facts obtained as output from a computer by means of processing input data with a program, wherein program is defined: A series of instructions given to a computer to direct it to carry out certain operations. (Dictionary.com)
113 radar: A method of detecting distant objects and determining their position, speed, material composition, or other characteristics by causing radio waves to be reflected from them and analyzing the reflected waves. The waves can be converted into images, as for use on weather maps. (Dictionary.com)
Prosecution Insights