DFETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Preliminary Amendment
The preliminary amendment filed on January 13th, 2025 has been acknowledged and entered.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record on file.
Information Disclosure Statement(s)
The Information disclosure statements (IDS) filed on January 13th, 2025 and July 09th, 2026 have been acknowledged and considered by the examiner.
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 limitation(s) 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 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, materials, or acts to entirely perform the recited function.
Claim(s) 1, recite(s) limitation(s) that use words like “means” (or “step”) or similar terms with functional language but do not invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “storage device for…,” [Line 4].
Such claim limitation(s) is/are:
(i) “storage device” have a structure associated with it a storage memory of a computer.
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.
Claims 1, 3-9 and 11-15, recite(s) limitation(s) that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claims 1 and 3-7; recites the limitation, “an arithmetic operation device” [Lines 2-3 in claim 1], [line 2 in claim 3], [line 2 in claim 4], [line 2 in claim 5], [line 2 in claim 6] and [line 2 in claim 7].
Claim 8; recites the limitation, “an image pickup device for…” [Line 3 in claim 8].
Claim 8; recites the limitation, “a quantitative arithmetic operation device for…” [Line 5 in claim 8].
Claims 9, 11-15; recites the limitation, “a device…” [Line 3, lines 4, 6, 8 and 10 in claim 9], [line 2 in claim 11], [line 2 in claim 12], [line 2 in claim 13], [line 2 in claim 14] and [line 2 in claim 15].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 3-9 and 11-15;
(i) “arithmetic operation device” (Fig. 1, #111. Paragraph [0013]- the instant specification, filed on January 13th, 2025 provides sufficient written support for a structure, material and/or act for the recited arithmetic operation device to perform its recited functions, in Par. [0013] being described as the arithmetic operation device represents the device that executes various types of processing in the object classification device, which includes the CPU (Central Processing Unit), FPGA (Field-Programmable Gate Array), and the like, thus have sufficient structure or material wherein is CPU/Central Processing Unit.).
(ii) “image pickup device” (Fig. 1, #120. Paragraph [0091]- the instant specification, filed on January 13th, 2025 provides sufficient written support for a structure, material and/or act for the recited image pickup device to perform its recited functions, in Par. [0091] being described as a camera and a microscope, thus have sufficient structure or material wherein is a camera and a microscope.).
(iii) “quantitative arithmetic operation device” the instant specification, filed on January 13th, 2025, has no sufficient written support for the recited arithmetic operation device to perform the recited function, the closest support can be found in the instant specification, [0091] of “the object classification system further includes a quantitative arithmetic operation device” however, still doesn’t provide specific sufficient structure, material and/or act, thus have insufficient structure, material and/or act.
(iv) “device” (Fig. 1, #111. Paragraph [0013]- the instant specification, filed on January 13th, 2025 provides sufficient written support for a structure, material and/or act for the recited arithmetic operation device to perform its recited functions, in Par. [0013] being described as the arithmetic operation device represents the device that executes various types of processing in the object classification device, which includes the CPU (Central Processing Unit), FPGA (Field-Programmable Gate Array), and the like, thus have sufficient structure or material wherein is CPU/Central Processing Unit.).
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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 8’s limitation:
Claim 8; recites the limitation, “a quantitative arithmetic operation device for…” [Line 5].
Claim 8 respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 8 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation.
Claim 8; recites the limitation, “a quantitative arithmetic operation device for…” [Line 5].
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more nor an integration of the judicial exceptions into a practical application. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion). The claimed invention merely performs image classification basing on a region of an object in the image and its relevant information/data. See analysis below for more details.
Regarding Independent Claim 1 and its dependent claims 2-8,
Step 1 Analysis: Claim 1 is directed to a device, which falls within one of the four statutory categories (process, machine, manufacture or composition of matter). Please see MPEP §2106.04.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“determine an object region of an entire object in the image,
generate information per unit of the object region based on the object region,
extracts an object region unit feature value from the information per unit of the object region,
extracts a partial region unit feature value from each of one or more partial regions in the object region, and
classifies the object based on the object region unit feature value and the partial region unit feature value,”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes/Mathematical Concept” grouping of abstract ideas. Please see MPEP §2106.04. The limitations of:
the limitation is a series of steps a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, etc.) and evaluate them to make a determination/identification/classifying some information/data. Furthermore, the human mind can use data/information and mathematical concepts, operations to generate values, extract values, calculate and determine resulting values.
Notes: under MPEP 2106.04(a)(2)(III), mental process (thinking) “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011): "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all." (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 [1972]). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, generic circuit or device, or the likes. See " Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’).
Because both product/device and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
“An object classification device for classifying an object in an image, comprising: an arithmetic operation device; and a storage device for storing a program to be executed by the arithmetic operation device, wherein the arithmetic operation device”
The additional elements are recited at a high level of generality (i.e. as a processor performing executing instructions stored, a memory storing instruction program, a computer to have computer components executing the instructions of the invention, a non-transitory computer readable medium performing storing instructions, generic devices [interface, screen, camera, sensor, etc.] performing well-known generic functions, etc.).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. Please see MPEP §2106.05
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-8 do not provide elements that overcome the deficiencies of the independent claim 1.
Moreover, claim 2 recites, in part,
“the information per unit of the object region…a feature of the object region”
Wherein clause that merely provides unlimiting further specification that provide well-known generality, high level of generality further specification limitation to the limitation each depends on, such as the information per unit…is formed according to certain condition, data.
Moreover, claim 3 recites, in part,
“the arithmetic operation device is configured to set a point in the object region, and to generate the information…as an original point”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing setting a point and generating value/information, which are mere abstract idea activities recited in a mere attempt to implement the abstract ideas using a generic CPU device.
Moreover, claim 4 recites, in part,
“the arithmetic operation device generates the information…pixel of the image”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing generating data/value using mathematical concept, operation in a mere attempt to implement the abstract idea using generic CPU.
Moreover, claim 5 recites, in part,
“the arithmetic operation device determines…on a shape of the object region”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing determination a starting point by observing some data/information of the image in a mere attempt to implement the abstract idea using generic CPU.
Moreover, claim 6 recites, in part,
“the arithmetic operation device analyzes…determines an evaluation value…the classification result”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing analyzing some observable data/information and make determination according to some observable data and condition to be met of the data.
“displays the evaluation value of a user” is a post-solution activity of displaying data/information being an insignificant additional element, under Step 2A Prong 2 and Step 2B of the 101 analysis.
Moreover, claim 7 recites, in part,
“the arithmetic operation device determines a classification…classifies the object…the partial region”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing observing the data/information of the image and determines some value and classify the object according to some observable condition to be met of the data, in a mere attempt to implement the abstract ideas using generic CPU.
Moreover, claim 8 recites, in part,
“an image pickup device for picking up…a quantitative arithmetic operation device for calculating…the one or more images are classified based on the quantitative value”
Which includes generic devices and additional elements recited at high level of generality to perform generic functions.
Accordingly, the dependent claims 2-8 are not patent eligible under 101.
Regarding Independent Claim 9 and its dependent claims 10-15,
Step 1 Analysis: Claim 9 is directed to a method/process, which falls within one of the four statutory categories (process, machine, manufacture or composition of matter). Please see MPEP §2106.04.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“determine an object region of an entire object in the image,
generates information per unit of the object region based on the object region,
extracts an object region unit feature value from the information per unit of the object region,
extracts a partial region unit feature value from each of one or more partial regions in the object region, and
classifies the object based on the object region unit feature value and the partial region unit feature value,”
The limitations as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes/Mathematical Concept” grouping of abstract ideas. Please see MPEP §2106.04. The limitations of:
the limitation is a series of steps a human mind can perform, under BRI, using pen and paper through a process of observation and evaluation such as, the human mind can observe some data/information (images, video, already given or resulted outcome/output of data/information, etc.) and evaluate them to make a determination/identification/classifying some information/data. Furthermore, the human mind can use data/information and mathematical concepts, operations to generate values, extract values, calculate and determine resulting values.
Notes: under MPEP 2106.04(a)(2)(III), mental process (thinking) “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011): "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all." (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 [1972]). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, generic circuit or device, or the likes. See " Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’).
Because both product/device and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. particular, the claim recites the following additional element(s) –
A device….the device…..
The additional elements are recited at a high level of generality (i.e. as a processor performing executing instructions stored, a memory storing instruction program, a computer to have computer components executing the instructions of the invention, a non-transitory computer readable medium performing storing instructions, generic devices [interface, screen, camera, sensor, etc.] performing well-known generic functions, etc.).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea. Please see MPEP §2106.05
For all of the foregoing reasons, claim 9 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 10-15 do not provide elements that overcome the deficiencies of the independent claim 9.
Moreover, claim 10 recites, in part,
“the information per unit of the object region…a feature of the object region”.
Wherein clause that merely provides unlimiting further specification that provide well-known generality, high level of generality further specification limitation to the limitation each depends on, such as the information per unit…is formed according to certain condition, data.
Moreover, claim 11 recites, in part,
“the device is configured to set a point in the object region, and to generate the information…as an original point”.
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing setting a point and generating value/information, which are mere abstract idea activities recited in a mere attempt to implement the abstract ideas using a generic CPU device.
Moreover, claim 12 recites, in part,
“the device generates the information…pixel of the image”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing generating data/value using mathematical concept, operation in a mere attempt to implement the abstract idea using generic CPU.
Moreover, claim 13 recites, in part,
“the device determines…on a shape of the object region”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing determination a starting point by observing some data/information of the image in a mere attempt to implement the abstract idea using generic CPU.
Moreover, claim 14 recites, in part,
“the device analyzes…determines an evaluation value…the classification result”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing analyzing some observable data/information and make determination according to some observable data and condition to be met of the data.
“displays the evaluation value of a user” is a post-solution activity of displaying data/information being an insignificant additional element, under Step 2A Prong 2 and Step 2B of the 101 analysis.
Moreover, claim 15 recites, in part,
“the device determines a classification…classifies the object…the partial region”
Which is a mental process activity abstract idea of observation and evaluation, judgement, merely performing observing the data/information of the image and determines some value and classify the object according to some observable condition to be met of the data, in a mere attempt to implement the abstract ideas using generic CPU.
Accordingly, the dependent claims 10-15 are not patent eligible under 101.
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.
Claims 1-2, 7-10 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Anzori Georgadze et. al. (”US 2021/0003735 A1” hereinafter as “Georgadze”).
Regarding claim 1, Georgadze teaches an object classification device for classifying an object in an image (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), comprising: an arithmetic operation device (Par. [0056] discloses “certain embodiments of the invention was discussed below may be incorporated as code…residing in firmware and/or computer usable medium…on a computer system” wherein, the computer system is analogous to the recited arithmetic operation device); and a storage device for storing a program to be executed by the arithmetic operation device, wherein the arithmetic operation device (Par. [0056] discloses “computer useable medium having control logic for enabling execution on a computer system having a computer processor…from execution of the code”) determines an object region of an entire object in the image (Par. [0069] discloses “those voxels in a region of interest in which the detected material is located”; moreover, Par. [0128] discloses “object reconstruction and identification…with different filtering values and combinations being selected which maximizes gradients in volume map distributions for that specific region of the VOI or an object”), generates information per unit of the object region based on the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit), extracts an object region unit feature value from the information per unit of the object region (Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value), extracts a partial region unit feature value from each of one or more partial regions in the object region (Par. [0067] discloses “different spectral regions are treated separately to construct multiple volume density maps…a hit map (volume density map) can thereby be reconstructed to locate objects in the VOI…the volume density map can be based on…measured parameter such as counts of completed trajectories, total scattering or parameters derived from the measured parameters…”), and classifies the object based on the object region unit feature value (Par. [0069] discloses “the automatic system is ready to classify objects existing within the VOI”, moreover, Par. [0068] discloses “additional filtering process to the trajectories by their deflection angle in the VOI” wherein, the data obtained from the deflection angle contributes to the volume density map, Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value) and the partial region unit feature value (Par. [0069] discloses “obtained distribution functions can be associated with the material and its density filling the volume of the voxel, in particular those voxels in a region of interest in which the detected material is located…statistical features, such as the obtained count rate, scattering angle and their distribution functions as training data, can be fed into a supervised machine-learning algorithm , as an example probability distribution function based classification” wherein, the count rate, scattering angle and their distribution functions indicate the partial region unit feature value).
Regarding claim 2, Georgadze teaches the object classification device according to claim 1 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), wherein the information per unit of the object region is formed as a fixed-sized one-dimensional or two-dimensional signal indicating a feature of the object region (“or” indicates a selection, only one of the options is the instant scope of the claim, the examiner selects “the information per unit of the object region is formed as a fixed-sized two-dimensional signal indicating a feature of the object region” which is disclosed in Par. [0122] of “object detection in 2D plane is conducted on the input slices (voxel plants) with a single value assigned to each voxel” wherein, the single value assigned to each voxel indicating fixed-sized 2D plane data).
Regarding claim 7, Georgadze teaches the object classification device according to claim 1, wherein the arithmetic operation device determines a classification result per unit of the partial region (Par. [0069] discloses “statistical features…as training data…a probability distribution function based classification with KLD…the automatic system is ready to classify objects existing within the VOI” training data including classification result being used for training the neural network, analogous to analyzing the classification result as claimed, since the training data includes labels already determined) based on the object region unit feature value (Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value) and the partial region unit feature values of a plurality of the partial regions (Par. [0067] discloses “different spectral regions are treated separately to construct multiple volume density maps…a hit map (volume density map) can thereby be reconstructed to locate objects in the VOI…the volume density map can be based on…measured parameter such as counts of completed trajectories, total scattering or parameters derived from the measured parameters…”), and classifies the object based on the classification result (Par. [0069] discloses “the automatic system is ready to classify objects existing within the VOI”, moreover, Par. [0068] discloses “additional filtering process to the trajectories by their deflection angle in the VOI” wherein, the data obtained from the deflection angle contributes to the volume density map, Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value) per unit of the partial region (Par. [0069] discloses “obtained distribution functions can be associated with the material and its density filling the volume of the voxel, in particular those voxels in a region of interest in which the detected material is located…statistical features, such as the obtained count rate, scattering angle and their distribution functions as training data, can be fed into a supervised machine-learning algorithm , as an example probability distribution function based classification” wherein, the count rate, scattering angle and their distribution functions indicate the partial region unit feature value).
Regarding claim 8, Georgadze teaches the object classification device according to claim 1, an object classification system comprising: the object classification device according to claim 1 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”); an image pickup device for picking up one or more images to be input to the object classification device (Par. [0001] discloses “devices and processes for detection and/or identification of articles using charged particles, for example based on cosmic ray tomography” indicating an image pickup device for capturing microscopic images; Par. [0069] discloses “the automatic system is ready to classify objects existing within the VOI”); and a quantitative arithmetic operation device for calculating a quantitative value for each type of an object contained in the one or more images (Par. [0160] discloses “autonomous material identification for the detected objected by using trained data and machine learning” indicating performing the object detection in respect to the using of the trained data [classification results], wherein the material identification is analogous to the recited quantitative value) from a classification result of the object classification device with respect to the one or more images (Par. [0069] discloses “statistical features…as training data, can be fed into a supervised machine-learning algorithm” wherein the processor performing this step is analogous to the recited quantitative arithmetic operation device), wherein the one or more images are classified based on the quantitative value (Par. [0069] discloses “the automatic system is ready to classify objects existing within the VOI” moreover, Par. [0160] discloses “autonomous material identification for the detected objects by using trained data and machine learning”).
Regarding claim 9, Georgadze teaches an object classification method for classifying an object in an image (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”),wherein a device (Par. [0056] discloses “certain embodiments of the invention was discussed below may be incorporated as code…residing in firmware and/or computer usable medium…on a computer system” wherein, the computer system is analogous to the recited device) determines an object region of an entire object in the image (Par. [0069] discloses “those voxels in a region of interest in which the detected material is located”; moreover, Par. [0128] discloses “object reconstruction and identification…with different filtering values and combinations being selected which maximizes gradients in volume map distributions for that specific region of the VOI or an object”), the device generates information per unit of the object region based on the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit), the device extracts an object region unit feature value from the information per unit of the object region (Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value), the device extracts a partial region unit feature value from each of one or more partial regions in the object region (Par. [0067] discloses “different spectral regions are treated separately to construct multiple volume density maps…a hit map (volume density map) can thereby be reconstructed to locate objects in the VOI…the volume density map can be based on…measured parameter such as counts of completed trajectories, total scattering or parameters derived from the measured parameters…”), and the device classifies the object based on the object region unit feature value (Par. [0069] discloses “the automatic system is ready to classify objects existing within the VOI”, moreover, Par. [0068] discloses “additional filtering process to the trajectories by their deflection angle in the VOI” wherein, the data obtained from the deflection angle contributes to the volume density map, Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value) and the partial region unit feature value (Par. [0069] discloses “obtained distribution functions can be associated with the material and its density filling the volume of the voxel, in particular those voxels in a region of interest in which the detected material is located…statistical features, such as the obtained count rate, scattering angle and their distribution functions as training data, can be fed into a supervised machine-learning algorithm , as an example probability distribution function based classification” wherein, the count rate, scattering angle and their distribution functions indicate the partial region unit feature value).
Regarding claim 10, Georgadze teaches the object classification method according to claim 9 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), wherein the information per unit of the object region is formed as a fixed-sized one-dimensional or two-dimensional signal indicating a feature of the object region (“or” indicates a selection, only one of the options is the instant scope of the claim, the examiner selects “the information per unit of the object region is formed as a fixed-sized two-dimensional signal indicating a feature of the object region” which is disclosed in Par. [0122] of “object detection in 2D plane is conducted on the input slices (voxel plants) with a single value assigned to each voxel” wherein, the single value assigned to each voxel indicating fixed-sized 2D plane data).
Regarding claim 15, Georgadze teaches the object classification method according to claim 9, wherein the device determines a classification result per unit of the partial region (Par. [0069] discloses “statistical features…as training data…a probability distribution function based classification with KLD…the automatic system is ready to classify objects existing within the VOI” training data including classification result being used for training the neural network, analogous to analyzing the classification result as claimed, since the training data includes labels already determined) based on the object region unit feature value (Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value) and the partial region unit feature values of a plurality of the partial regions (Par. [0067] discloses “different spectral regions are treated separately to construct multiple volume density maps…a hit map (volume density map) can thereby be reconstructed to locate objects in the VOI…the volume density map can be based on…measured parameter such as counts of completed trajectories, total scattering or parameters derived from the measured parameters…”), and classifies the object based on the classification result (Par. [0069] discloses “the automatic system is ready to classify objects existing within the VOI”, moreover, Par. [0068] discloses “additional filtering process to the trajectories by their deflection angle in the VOI” wherein, the data obtained from the deflection angle contributes to the volume density map, Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value) per unit of the partial region (Par. [0069] discloses “obtained distribution functions can be associated with the material and its density filling the volume of the voxel, in particular those voxels in a region of interest in which the detected material is located…statistical features, such as the obtained count rate, scattering angle and their distribution functions as training data, can be fed into a supervised machine-learning algorithm , as an example probability distribution function based classification” wherein, the count rate, scattering angle and their distribution functions indicate the partial region unit feature value).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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.
Claims 3, 5, 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Anzori Georgadze et. al. (”US 2021/0003735 A1” hereinafter as “Georgadze”) and David Groszki et. al. (“US 2013/0271139 A1” hereinafter as “Groszki”).
Regarding claim 3, Georgadze teaches the object classification device according to claim 2 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), wherein the arithmetic operation device is configured to set a point in the object region (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory”), and to generate the information per unit of the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit) by a form having the point as an original point (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory” wherein, entering point is analogous to the recited original point).
However, Georgadze does not explicitly teach the form being a polar coordinate form.
Groszki teaches the form being a polar coordinate form (Par. [0022] discloses “contiguous path in k-space between a start point of the trajectory in k-space and an end point of the trajectory in k-space” which is analogous to Georgadze’ starting point of the trajectory; moreover, Par. [0032] discloses “the aid of a polar angle y by an amplitude G0 and a direction provided by the direction from the origin to a point (cos y, sin y)” wherein, the points are expressed in polar form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an object classification device, wherein the arithmetic operation device is configured to set a point in the object region, and to generate the information per unit of the object region by a form having the point as an original point, with the teachings of Groszki of having the form being a polar coordinate form.
Wherein having Georgadze’s device of having the form being a polar coordinate form.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging. Since both Georgadze and Groszki perform article imaging analysis through trajectory tracking and angle detection analysis. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Groszki’s system improves magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging, (see Groszki’s Par. [0150]).
Regarding claim 5, Georgadze in view of Groszki teaches the object classification device according to claim 3, Georgadze teaches wherein the arithmetic operation device determines a starting point of a deflection angle (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory”) when generating the information per unit of the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit) by the form (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory” wherein, entering point is analogous to the recited original point) based on a shape of the object region (Par. [0162] discloses “input hodoscope spectral filtering for efficient object reconstruction…detection of the uranium with the same size and shape can be exposed”).
However, Georgadze does not explicitly teach the form being a polar coordinate form.
Groszki teaches the form being a polar coordinate form (Par. [0022] discloses “contiguous path in k-space between a start point of the trajectory in k-space and an end point of the trajectory in k-space” which is analogous to Georgadze’ starting point of the trajectory; moreover, Par. [0032] discloses “the aid of a polar angle y by an amplitude G0 and a direction provided by the direction from the origin to a point (cos y, sin y)” wherein, the points are expressed in polar form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an object classification device, wherein the arithmetic operation device is configured to set a point in the object region, and to generate the information per unit of the object region by a form having the point as an original point, with the teachings of Groszki of having the form being a polar coordinate form.
Wherein having Georgadze’s device of having the form being a polar coordinate form.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging. Since both Georgadze and Groszki perform article imaging analysis through trajectory tracking and angle detection analysis. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Groszki’s system improves magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging, (see Groszki’s Par. [0150]).
Regarding claim 11, Georgadze teaches the object classification method according to claim 10 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), wherein the device is configured to set a point in the object region (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory”), and to generate the information per unit of the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit) by a form having the point as an original point (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory” wherein, entering point is analogous to the recited original point).
However, Georgadze does not explicitly teach the form being a polar coordinate form.
Groszki teaches the form being a polar coordinate form (Par. [0022] discloses “contiguous path in k-space between a start point of the trajectory in k-space and an end point of the trajectory in k-space” which is analogous to Georgadze’ starting point of the trajectory; moreover, Par. [0032] discloses “the aid of a polar angle y by an amplitude G0 and a direction provided by the direction from the origin to a point (cos y, sin y)” wherein, the points are expressed in polar form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an object classification method, wherein the arithmetic operation method is configured to set a point in the object region, and to generate the information per unit of the object region by a form having the point as an original point, with the teachings of Groszki of having the form being a polar coordinate form.
Wherein having Georgadze’s method of having the form being a polar coordinate form.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging. Since both Georgadze and Groszki perform article imaging analysis through trajectory tracking and angle detection analysis. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Groszki’s system improves magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging, (see Groszki’s Par. [0150]).
Regarding claim 13, Georgadze teaches the object classification method according to claim 11, wherein the device determines a starting point of a deflection angle (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory”) when generating the information per unit of the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit) by the form (Par. [0019] discloses “we get as many volume density maps as we decide by back projecting the trajectories from the measured point to entering point for each trajectory” wherein, entering point is analogous to the recited original point) based on a shape of the object region (Par. [0162] discloses “input hodoscope spectral filtering for efficient object reconstruction…detection of the uranium with the same size and shape can be exposed”).
However, Georgadze does not explicitly teach the form being a polar coordinate form.
Groszki teaches the form being a polar coordinate form (Par. [0022] discloses “contiguous path in k-space between a start point of the trajectory in k-space and an end point of the trajectory in k-space” which is analogous to Georgadze’ starting point of the trajectory; moreover, Par. [0032] discloses “the aid of a polar angle y by an amplitude G0 and a direction provided by the direction from the origin to a point (cos y, sin y)” wherein, the points are expressed in polar form).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an object classification method, wherein the arithmetic operation method is configured to set a point in the object region, and to generate the information per unit of the object region by a form having the point as an original point, with the teachings of Groszki of having the form being a polar coordinate form.
Wherein having Georgadze’s method of having the form being a polar coordinate form.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging. Since both Georgadze and Groszki perform article imaging analysis through trajectory tracking and angle detection analysis. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Groszki’s system improves magnetic resonance imaging system by having effective measures in order to reduce noise generation during the imaging, (see Groszki’s Par. [0150]).
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Anzori Georgadze et. al. (”US 2021/0003735 A1” hereinafter as “Georgadze”) and Tokihiro Kosaka (“US 5,448,349” hereinafter as “Kosaka”).
Regarding claim 4, Georgadze teaches the object classification device according to claim 1, wherein the arithmetic operation device generates the information per unit of the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit) based on information of the object region (Par. [0066-0067] discloses grouping the lepton flux based on the distribution of angular deflection….construct multiple volume density maps” for different spectral regions).
However, Georgadze does not explicitly teach based on information of the object region and a pixel of the image.
Kosaka teaches based on information of the object region and a pixel of the image (Col. 3, lines 58-68, disclose “control the deflection angle of the optical deflection means…allows an irradiation are defined on the particle to shift in the direction in which the particle move”; moreover, Col. 4, lines 44-53, disclose “initiates control of the deflection angle” which is analogous to the deflection angle of Georgadze; and Col. 3, lines 41-45, disclose “a sensor such as a CCD line sensor is used in which a plurality of light detecting pixels are arranged in a row so that signals from the pixels are serially output upon scanning” wherein, the image captured through such deflection angle is in pixel of the region of the object).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an object classification device, wherein the arithmetic operation device generates the information per unit of the object region based on information of the object region, with the teachings of Kosaka of having based on information of the object region and a pixel of the image.
Wherein having Georgadze’s device of having based on information of the object region and a pixel of the image.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve optical deflection mean control to capture images of particle more effectively according to the relative speed of the particle and the dimension of the image sensor so that the analysis precision of the particle is improved. Since both Georgadze and Kosaka perform article imaging analysis through trajectory tracking and angle detection analysis. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Kosaka’s system improves optical deflection mean control to capture images of particle more effectively according to the relative speed of the particle and the dimension of the image sensor so that the analysis precision of the particle is improved, (see Kosaka’s Col. 3, lines 58-68).
Regarding claim 12, Georgadze teaches the object classification method according to claim 9, wherein the device generates the information per unit of the object region (Par. [0137] discloses “large fraction of the mouns within the region between angle 0-0.6 degree are shown with exaggerated deflection angle” wherein, the fraction of mouns within the region is analogous to the recited information per unit) based on information of the object region (Par. [0066-0067] discloses grouping the lepton flux based on the distribution of angular deflection….construct multiple volume density maps” for different spectral regions).
However, Georgadze does not explicitly teach based on information of the object region and a pixel of the image.
Kosaka teaches based on information of the object region and a pixel of the image (Col. 3, lines 58-68, disclose “control the deflection angle of the optical deflection means…allows an irradiation are defined on the particle to shift in the direction in which the particle move”; moreover, Col. 4, lines 44-53, disclose “initiates control of the deflection angle” which is analogous to the deflection angle of Georgadze; and Col. 3, lines 41-45, disclose “a sensor such as a CCD line sensor is used in which a plurality of light detecting pixels are arranged in a row so that signals from the pixels are serially output upon scanning” wherein, the image captured through such deflection angle is in pixel of the region of the object).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an object classification method, wherein the arithmetic operation method generates the information per unit of the object region based on information of the object region, with the teachings of Kosaka of having based on information of the object region and a pixel of the image.
Wherein having Georgadze’s method of having based on information of the object region and a pixel of the image.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve optical deflection mean control to capture images of particle more effectively according to the relative speed of the particle and the dimension of the image sensor so that the analysis precision of the particle is improved. Since both Georgadze and Kosaka perform article imaging analysis through trajectory tracking and angle detection analysis. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Kosaka’s system improves optical deflection mean control to capture images of particle more effectively according to the relative speed of the particle and the dimension of the image sensor so that the analysis precision of the particle is improved, (see Kosaka’s Col. 3, lines 58-68).
Claims 6 and 14 rejected under 35 U.S.C. 103 as being unpatentable over Anzori Georgadze et. al. (”US 2021/0003735 A1” hereinafter as “Georgadze”) and Yasufumi Hirakawa (“US 2019/0378279 A1” hereinafter as “Hirakawa”).
Regarding claim 6, Georgadze teaches the object classification device according to claim 1 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), wherein the arithmetic operation device analyzes the object region unit feature value and/or the partial region unit feature value (“and/or” indicates a selection, the examiner selects “object region unit feature value” to be the instant scope of the claim, Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value), and a classification result of the object (Par. [0069] discloses “statistical features…as training data…a probability distribution function based classification with KLD…the automatic system is ready to classify objects existing within the VOI” training data including classification result being used for training the neural network, analogous to analyzing the classification result as claimed, since the training data includes labels already determined), determines an evaluation value of the information per unit of the object region and/or the partial region unit feature value with respect to the classification result (“and/or” indicates a selection, the examiner selects “object region unit feature value” to be the instant scope of the claim; Par. [0160] discloses “autonomous material identification for the detected objected by using trained data and machine learning” indicating performing the object detection in respect to the using of the trained data [classification results], wherein the material identification is analogous to the recited evaluation value).
Georgadze does not explicitly teach displays the evaluation value for a user.
Hirakawa teaches displays the evaluation value for a user (Par. [0015] discloses “a display unit to display a trajectory indicating a change in the position of the object” wherein, the trajectory of change of object is analogous to Georgadze’s object detection result and trajectory tracking).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an arithmetic operation device analyzes the object region unit feature value and/or the partial region unit feature value, and a classification result of the object, determines an evaluation value of the information per unit of the object region and/or the partial region unit feature value with respect to the classification result, with the teachings of Hirakawa of having wherein to display the evaluation value for a user.
Wherein having Georgadze’s device of having wherein to display the evaluation value for a user.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve object detection with displaying of object trajectory effectively that enhance user’s ability to recognize objects. Since both Georgadze and Hirakawa perform object detection and tracking based on object trajectories. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Hirakawa’s system improves object detection with displaying of object trajectory effectively that enhance user’s ability to recognize objects, (see Hirakawa’s Par. [0094]).
Regarding claim 14, Georgadze teaches the object classification method according to claim 9 (Par. [0069] discloses “automatic system is ready to classify objects existing within the VOI”), wherein device analyzes the object region unit feature value and/or the partial region unit feature value (“and/or” indicates a selection, the examiner selects “object region unit feature value” to be the instant scope of the claim, Pars. [0064-0067] disclose “certain fraction of particles will be attenuated in the CV…for object localization, the VOI is subdivided into certain volumes…different spectral regions are treated separately to construct multiple volume density maps” wherein, the region’s volume density map is analogous to the recited object region unit feature value), and a classification result of the object (Par. [0069] discloses “statistical features…as training data…a probability distribution function based classification with KLD…the automatic system is ready to classify objects existing within the VOI” training data including classification result being used for training the neural network, analogous to analyzing the classification result as claimed, since the training data includes labels already determined), determines an evaluation value of the information per unit of the object region and/or the partial region unit feature value with respect to the classification result (“and/or” indicates a selection, the examiner selects “object region unit feature value” to be the instant scope of the claim; Par. [0160] discloses “autonomous material identification for the detected objected by using trained data and machine learning” indicating performing the object detection in respect to the using of the trained data [classification results], wherein the material identification is analogous to the recited evaluation value).
Georgadze does not explicitly teach displays the evaluation value for a user.
Hirakawa teaches displays the evaluation value for a user (Par. [0015] discloses “a display unit to display a trajectory indicating a change in the position of the object” wherein, the trajectory of change of object is analogous to Georgadze’s object detection result and trajectory tracking).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Georgadze of having an arithmetic operation method analyzes the object region unit feature value and/or the partial region unit feature value, and a classification result of the object, determines an evaluation value of the information per unit of the object region and/or the partial region unit feature value with respect to the classification result, with the teachings of Hirakawa of having wherein to display the evaluation value for a user.
Wherein having Georgadze’s method of having wherein to display the evaluation value for a user.
The motivation behind the modification would have been to improve detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, and further to improve object detection with displaying of object trajectory effectively that enhance user’s ability to recognize objects. Since both Georgadze and Hirakawa perform object detection and tracking based on object trajectories. Wherein Georgadze’s system improves detecting of material or article in a volume of interest by detecting with an input hodoscope and detecting with an output hodoscope and associating particles correctly to perform particle detection effectively, (see Georgadze’s Pars. [0007-0013]) and Hirakawa’s system improves object detection with displaying of object trajectory effectively that enhance user’s ability to recognize objects, (see Hirakawa’s Par. [0094]).
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Szczerba, Joseph F. et. al., “US 2010/0253595 A1”, discloses a method to display a graphic control display in a vehicle and receive a control input based upon the graphic control display includes displaying upon a surface within the vehicle a graphic control display based upon a control configuration, the graphic control display comprising an illustrative graphic presenting for selection a setting of a feature of the vehicle wherein the surface is coated with a material reactive to display graphics in response to an excitation projector. A control input corresponding to the illustrative graphic is monitored, and the feature of the vehicle is controlled based upon monitoring the control input.
Zhang, Ning et. al., “US 9483812 B2”, discloses methods and systems for digitally enhancing the characteristics of an image sequence including video and motion picture images are provided. Highly accurate motion information represented by trajectories are generated through analyzing available image frames in the image sequence. Some embodiments of the present invention relate to generating multiple layer trajectories from an image sequence. Certain aspects may be applicable to the enhancement of three-dimensional (3D) image sequences including 3D video and 3D motion pictures.
LANIO, Stefan et. al., “ US 2015/0228452 A1”, discloses a secondary charged particle detection system for a charged particle beam device is described. The detection system includes a beam splitter for separating a primary beam and a secondary beam formed upon impact on a specimen; a beam bender for deflecting the secondary beam; a focusing lens for focusing the secondary beam; a detection element for detecting the secondary beam particles, and three deflection elements, wherein at least a first deflector is provided between the beam bender and the focusing lens, at least a second deflector is provided between the focusing lens and the detection element, at least a third deflector is provided between the beam splitter and the detection element.
Zeidler, Dirk et. al., “US 2025/0379030 A1”, discloses a particle beam system includes: a multi-beam particle source configured to generate a multiplicity of particle beams; an imaging optical unit configured to image an object plane in particle-optical fashion into an image plane and direct the multiplicity of particle beams on the image plane; and a field generating arrangement configured to generate electric and/or magnetic deflection fields of adjustable strength in regions close to the object plane. The particle beams are deflected in operation by the deflection fields through deflection angles that depend on the strength of the deflection fields.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG HAU CAI whose telephone number is (571)272-9424. The examiner can normally be reached M-F 8:30 am - 5:00pm.
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, Chineyere Wills-Burns can be reached at (571) 272-9752. 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.
/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
