DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. § 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-014398, filed on 02/01/2021.
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.
Claims 2–3 are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “size of each of the bubbles,” and “speed at which the bubbles disappear” (emphasis added) in claim 2 is a relative term which renders the claim indefinite. Examiner construed that the terms are functional and descriptive. The specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
In re claim 3, Applicant recites “color of the edible oil,” “flavor of the edible oil,” and “flavor of the deep-fried food.” (emphasis added) Such terms suggest inherently subjective and qualitative.1
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 2 and 3 recite the broad recitation “or a combination thereof,” (emphasis added) which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
1. Claim 7 is directed to non-statutory subject matter. The broadest reasonable interpretation of the claim in light of the specification2 (since there is no disclosure of a medium) concludes that the claim as a whole covers a transitory signal since the definition of “medium” leaves open the possibility that the medium could be transitory. Appropriate correction is required.3
2. Claims 1 and 6 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The claim(s) recite(s) “an imaging section configured to acquire an image in which edible oil is captured; a state identification section configured to analyze the image to identify a state of the edible oil; and a learning section configured to cause a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil.”4 (emphasis added) This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory). See Alice/Mayo test under MPEP § 2106, II. ESTABLISH BROADEST REASONABLE INTERPRETATION OF CLAIM AS A WHOLE.5
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
STEP 1: STATUTORY CATEGORY DETERMINATION
Conclusion: The claim recites a process within the statutory category of 35 U.S.C. § 101(a).
Analysis:
The claim is directed to “a learning device” and “a method” with recited steps: “an image section configured to acquire an image in which edible oil is captured; a state identification section configured to analyze the image to identify a state of the edible oil; and a learning section configured to cause a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil.”
A method claim constitutes a process, which is explicitly enumerated in § 101.
The claim does not recite a machine, manufacture, or composition of matter as the primary subject matter.
Result: Claim satisfies the statutory category requirement and proceeds to Step 2A.
STEP 2A, PRONG 1: IDENTIFY JUDICIAL EXCEPTION(S)
Conclusion: The claim recites a judicial exception under the abstract idea grouping of mathematical concepts and potentially mental processes.
Offending Clauses:
“an image section configured to acquire an image in which edible oil is captured.”
This recites data-gathering mechanism that feeds the subsequent analytical and learning steps—i.e., recite abstract ideas to determine state identification and correlation.
Per PEG § II.B.1(a), the Court and the USPTO treat activities that can be performed entirely in the human mind, e.g., observation, evaluation, judgement, and opinion formulation—as mental process.
The imaging section merely gathers raw visual data to be processed by abstract identification and learning models, it does not integrate into a practical application under Step 2A.
“a state identification section configured to analyze the image to identify a state of the edible oil.”
Under MPEP § 2106.04(a), limitations that involve recognizing patterns, evaluating data, and determining a status or condition based on information are considered as abstract ideas. The actions of “analyzing” an image and “identifying a state” are quintessential cognitive or computational tasks that can be performed in the human mind or by a generic processor using mathematical algorithms.
The specification (¶320) states that the section parses the image and renders a process of identifying a state of the edible oil.
“a learning section configured to cause a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil.”
Mathematical concepts include correlations, calculations, and statistical learning. The act of “learning a correlation” between variables (an oil state and a rate of deterioration) is fundamentally a mathematical and statistical process. Further, training a model to recognize patterns and association is viewed as a computerized equivalent of a mental process that can be performed in the human mind or via generic mathematical algorithms.
STEP 2A, PRONG 2: INTEGRATION INTO A PRACTICAL APPLICATION
Conclusion: The claim FAILS to integrate the judicial exception into a practical application under Step 2A, Prong 2.
Analysis:
To satisfy Step 2A, Prong 2, the claim must demonstrate that the recited judicial exception is integrated into a practical application by:
Improving a technology or technological process (e.g., computer functioning, camera functionality);
Applying the exception to a particular machine or apparatus;
Transforming an article into a different state or thing; or
Other meaningful limitations beyond merely applying the exception.
Per 2019 PEG § II.B.2(a):
“A claim that recites a judicial exception may still be directed to a patent-eligible application of that exception if the claim as a whole integrates the exception into a practical application.”
Assessment:
1. No Claimed Technological Improvement
The claim does not recite:
Any hardware-specific implementation or architectural innovation
The specification (¶5) acknowledges the problem: “difficult to accurately predict the rate of deterioration” [of oil] but the claim does not recite how the image is acquired using an unconventional structure.
2. No Particular Machine or Apparatus
The claim recites:
“an imaging section”—a generic field-of-use limitation, not a particular machine
“a learning section”—a generic reference to a neural network, without specific architecture, hardware constraints, or technological limitations
“a state identification section”—an abstract mathematical construct, not a particular machine
Per Alice Corp. v. CLS Bank, 573 U.S. 208 (2014), and the PEG, data manipulation alone does not constitute a practical application.
4. Insignificant Extra-Solution Activity
The steps recited are:
an imaging procedure to acquire an image;
a state identification procedure to identify a state of the edible oil;
a learning procedure to learn a correlation between the state and a rate of deterioration
These steps are routine, conventional, and insignificant in the context of analyzing images of edible oil. These processes can be done by human without machine learning aid. They represent the ordinary operation of image analysis without additional technological constraints or improvements.
Per 2019 PEG § II.B.2(b):
“Mere instructions to apply the exception, insignificant extra-solution activity, or field-of-use limitations do not suffice.”
The claim provides “mere instructions” to apply a learning procedure—it does not recite how this application improves camera calibration technology or solves a technological problem beyond the abstract mathematical operations.
5. Field-of-Use Limitation
The phrase “edible oil” is a field-of-use limitation—it merely restricts the abstract mathematical operations to a particular industry or application domain (foodservice, restaurant, grocery stores, etc.). Per the PEG, field-of-use limitations alone do not integrate an exception into a practical application.
Conclusion on Step 2A, Prong 2:
The claim does not integrate the recited statistical concepts into a practical application. The claim recites generic steps to apply well-known mathematical operations without demonstrating:
A specific technological improvement
A particular machine or apparatus
A transformation of a physical article
Meaningful limitations beyond applying the exception
STEP 2B: SIGNIFICANTLY MORE / WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL (WURC)
Conclusion: The additional claim elements are NOT “significantly more” than the judicial exception because they consist of well-understood, routine, and convention (WURC) elements under Berkheimer standards.
Analysis:
Per 2019 PEG § II.B.3:
“If a claim recites a judicial exception and does not integrate the exception into a practical application under Step 2A, Prong 2, then determine whether the additional elements in the claim provide ‘significantly more’ than the exception itself. The additional elements must be more than well-understood, routine, and conventional (WURC) in the relevant field.”
Identification of Additional Elements:
Beyond the recited judicial exceptions, the claim recites:
“an imaging section configured to acquire an image in which edible oil is captured” – generic hardware
“a state identification section configured to analyze the image to identify a state of the edible oil” – observational step
“a learning section configured to cause a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil” – standard artificial intelligence tool
WURC Analysis:
1. an imaging section configured to acquire an image in which edible oil is captured
Evidence from Specification: The specification (¶319) states: the imaging section acquires an image in which the edible oil is captured.
The use of conventional computers or standard data-gathering devices (e.g., cameras or sensors) to perform generic functions does not amount to “significantly more.”
Berkheimer Support: The applicant provides no technical evidence (e.g., performance metrics, comparative studies, implementation details) demonstrating that simply stating generic hardware to capture data before running an algorithm is non-routine or non-conventional in the field of computer vision and machine learning.
2. a state identification section configured to analyze the image to identify a state of the edible oil – WURC
The specification (¶320) suggests identifying a state of the edible oil.
Under MPEP § 2106.05(d), generic computer components or software modules performing routine data analysis, pattern recognition, or image processing are considered WURC.
Berkheimer Support: Absent.
3. a learning section configured to cause a learning model to learn a correlation between the state and a rate deterioration indicating an increased level of deterioration of the edible oil – WURC
The language “configured to cause a learning model to learn a correlation” suggests a high level of abstraction, meaning it specifies what the AI does (learns a correlation), but not how it is uniquely structured to do so.
Utilizing generic machine learning models or standard training algorithms to find correlations is considered WURC.
Berkheimer Support: The claimed language states a broad, functional result of training a model to find a correlation. The limitation does not produce synergistic, non-conventional technical result that solves a technical problem internal to the computer or the imaging hardware.
No factual support is provided in the specification to establish that any of these elements are non-routine or non-conventional. The specification describes standard implementations of known techniques without disclosing novel modifications, unexpected results, or technological improvements.
Therefore, the additional elements do NOT amount to “significantly more” than the judicial exception.
CONCLUSION
Claim 1 is INELIGIBLE under 35 U.S.C. § 101.
Summary:
Step
Finding
Rationale
Step 1
✓ Eligible Category
Step 2A, Prong 1
✗ Recites Exception
Step 2A, Prong 2
✗ Not Integrated
Step 2B
✗ Not “Significantly More”
Overall Eligibility
INELIGIBLE
In re claim 6, it has been rejected in the same manner as claim 1.
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 (i.e., changing from AIA to pre-AIA ) 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 1 and 5–8 are rejected under 35 U.S.C. § 103 as being unpatentable over Pollman (U.S. 9,599,553 B2) in view of Sekiguchi et al. (U.S. 12,394,039 B2).
Regarding claim 1, Pollman discloses a learning device comprising:
an imaging section configured to acquire an image in which edible oil is captured; (Per Fig. 1, Pollman’s apparatus collects an image 13 of an oil. Pollman col. 1 lines 60–67. The testing device 12 captures an image 13 (picture) of the oil sample, processes pixels of the image,)
a state identification section configured to analyze the image to identify a state of the edible oil. (Per Fig. 1, Pollman’s apparatus 10 discloses an indication 14 of the oil’s quality based on pixel values. Ibid. [d]etermines quality of the oil based on hue and saturation of the pixels, and outputs (reports) an indication 14 of the oil's quality.)
However, Pollman fails to specifically disclose a learning section configured to cause a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil.
In related art, Sekiguchi discloses a learning section configured to cause a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil. (Per Fig. 3, Sekiguchi’s evaluation unit 30 discloses evaluation reference data 100 and image analysis data 200 where degradation of an oil is evaluated. Sekiguchi col. 8 lines 12–21. [a]nd compares the image analysis data 200 with the evaluation reference data 100, thereby creating the evaluation result 300 regarding the degree of degradation of the evaluation lubricating oil.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sekiguchi into the teachings of Pollman to appropriately evaluate degradation and contamination state of oil. Ibid. col. 1 lines 14–24.
Regarding claim 6, Pollman discloses a method of learning carried out by a learning device, comprising:
an imaging procedure in which the learning device acquires an image in which edible oil is captured; (Per Fig. 1, Pollman’s apparatus collects an image 13 of an oil. Pollman col. 1 lines 60–67. The testing device 12 captures an image 13 (picture) of the oil sample, processes pixels of the image,)
a state identification procedure in which the learning device analyzes the image to identify a state of the edible oil. (Per Fig. 1, Pollman’s apparatus 10 discloses an indication 14 of the oil’s quality based on pixel values. Ibid. [d]etermines quality of the oil based on hue and saturation of the pixels, and outputs (reports) an indication 14 of the oil's quality.)
However, Pollman fails to specifically disclose a learning procedure in which the learning device causes a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil.
In related art, Sekiguchi discloses a learning procedure in which the learning device causes a learning model to learn a correlation between the state and a rate of deterioration indicating an increased level of deterioration of the edible oil. (Per Fig. 3, Sekiguchi’s evaluation unit 30 discloses evaluation reference data 100 and image analysis data 200 where degradation of an oil is evaluated. Sekiguchi col. 8 lines 12–21. [a]nd compares the image analysis data 200 with the evaluation reference data 100, thereby creating the evaluation result 300 regarding the degree of degradation of the evaluation lubricating oil.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sekiguchi into the teachings of Pollman to appropriately evaluate degradation and contamination state of oil. Ibid. col. 1 lines 14–24.
Regarding claim 5, Pollman as modified by Sekiguchi, discloses a prediction device comprising a prediction section configured to predict the rate of deterioration using a learned model that is the learning model made to learn by the learning device. (Per Fig. 3, Sekiguchi’s machine learning unit 50 renders a prediction model 500 to analyze image data 200. Sekiguchi col. 8 lines 48–61. [t]hereby creating a prediction model 500 for determining image analysis data 200 from the imaging data 210 and the input variable.)
Regarding claim 7, Pollman as modified by Sekiguchi, discloses a computer readable media for causing a computer to execute the learning method. (Fig. 1, 10 a storage unit)
Regarding claim 8, Pollman as modified by Sekiguchi, discloses a learning system comprising: the learning device; (Fig. 3, 50 a machine learning unit) and the prediction device (Fig. 3, 30 an evaluation unit).
Claims 2–4 are rejected under 35 U.S.C. § 103 as being unpatentable over Pollman in view of Sekiguchi and further in view of Masakatsu (JP 2018065979 A).
Regarding claim 2, Pollman as modified by Sekiguchi, discloses the claimed invention, but fails to specifically disclose the learning device, wherein the state is a rate of formation of bubbles formed per predetermined time in the edible oil, the number of bubbles, size of each the bubbles, speed at which the bubbles disappear, or a combination thereof.
In related art, Masakatsu discloses the learning device, wherein the state is a rate of formation of bubbles formed per predetermined time in the edible oil, the number of bubbles, size of each the bubbles, speed at which the bubbles disappear, or a combination thereof. (Masakatsu discloses stable and continuous generation of bubbles in production of edible oil. Masakatsu Spec. ¶69. [i]t is possible to achieve both a sufficient cavitation effect and a sufficient flow rate, and the flow deviation between the multiple throttling holes 9 is effectively suppressed, allowing for stable and continuous generation of fine bubbles based on the cavitation effect.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Masakatsu into the teachings of Pollman and Sekiguchi to process the oil much simpler and healthier. Ibid. ¶7.
Regarding claim 3, Pollman as modified by Sekiguchi, discloses the claimed invention, but fails to specifically disclose the learning device, wherein the rate of deterioration is a rate of increase in viscosity of the edible oil, an acid value of the edible oil, color of the edible oil, an Anisidine value of the edible oil, the quantity of polar compounds of the edible oil, a Carbonyl value of the edible oil, a smoke point of the edible oil, Tocopherol content of the edible oil, an iodine value of the edible oil, a refractive index of the edible oil, the quantity of volatile compounds of the edible oil, a composition of volatile compounds of the edible oil, flavor of the edible oil, the quantity of volatile compounds of a deep-fried food obtained by deep-fry cooking with the edible oil, a composition of volatile compounds of the deep-fried food, flavor of the deep-fried food, or a combination thereof.
In related art, Masakatsu discloses the learning device, wherein the rate of deterioration is a rate of increase in viscosity of the edible oil, an acid value of the edible oil, color of the edible oil, an Anisidine value of the edible oil, the quantity of polar compounds of the edible oil, a Carbonyl value of the edible oil, a smoke point of the edible oil, Tocopherol content of the edible oil, an iodine value of the edible oil, a refractive index of the edible oil, the quantity of volatile compounds of the edible oil, a composition of volatile compounds of the edible oil, flavor of the edible oil, the quantity of volatile compounds of a deep-fried food obtained by deep-fry cooking with the edible oil, a composition of volatile compounds of the deep-fried food, flavor of the deep-fried food, or a combination thereof. (Masakatsu discloses flow rates of achieving microbubble formation efficiency. Masakatsu Spec. ¶45. This configuration is effective in achieving good microbubble formation efficiency in configurations requiring high flow rates.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Masakatsu into the teachings of Pollman and Sekiguchi to process the oil much simpler and healthier. Ibid. ¶7.
Regarding claim 4, Pollman as modified by Sekiguchi, discloses the claimed invention, but fails to specifically disclose the learning device, wherein the state is identified based on initial foam formed in edible oil having an acid value of less than 0.2.
In related art, Masakatsu discloses the learning device, wherein the state is identified based on initial foam formed in edible oil having an acid value of less than 0.2. (Per Fig. 1, Masakatsu discloses that processed oil’s acid value has less than 0.2. Masakatsu Spec. ¶53. [v]irgin olive oil or argan oil immediately after pressing, with an acid value of 0.5 or less, preferably 0.2 or less (in the case of olive oil, an acidity of 0.15% or less, preferably 0.08% or less),)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Masakatsu into the teachings of Pollman and Sekiguchi to process the oil much simpler and healthier. Ibid. ¶7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-17:00 (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, Stephen R. Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 Unless Applicant’s Spec. defines the terms using objective, quantifiable standards (e.g., color space coordinates to determine whether the oil is edible, or standardized sensory panel ratings for flavor), the terms are indefinite.
2 Applicant’s Spec. ¶391 explicitly states that the program is provided “via a computer-readable recording medium or a telecommunication line such as a network.” Examiner construed that this language suggests delivering thereof via transitory network signals.
3 Under In re Nuijten, transitory signals are not considered a “manufacture” under § 101 and therefore ineligible for patent protection. The Court held that a transitory signal—a carrier wave or electromagnetic transmission—is not a “manufacture” because it is not a physical, tangible entity. It is fleeting, lacks permeance, and does not fit within the traditional definition of a physical article of manufacture. See MPEP § 2106.03, II. ELIGIBILITY STEP 1: WHETHER A CLAIM IS TO A STATUTORY CATEGORY. Thus, a claim to a computer readable medium that can be a compact disc (emphasis added) or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
4 The terms “imaging section,” “state identification section,” and “learning section” are expressed in functional language, meaning generic processors perform routine functions.
5 The court disagreed, because it interpreted the claims as encompassing nothing other than pure mental steps (and thus falling within an abstract idea grouping) because the claims did not include any limitations requiring computer implementation.
