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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a calculator” in claim 2, line 2 and claim 3, line 6;
“a determiner” in claim 2, line 5
“a delete” in claim 2, line 7;
“a trainer” in claim 4, line 5; and
“an extractor” in claim 5, line 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim recites a computer program as a product without any structural recitations and thus is directed to software per se.
The claim recites a generator configured to generate data by trained generative artificial intelligence. Since generative AI is software, the generator configured to use generative AI is also software without any structural recitations. Specification paragraph [0009] disclose a generation device which includes a CPU, paragraph [0010] discloses the generation device includes generative AI, and the final sentence in paragraph [0034] discloses the generation device is merely one example of a generator. The generator may have other embodiments that lack structural features.
Claims 2-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 2-5 recite a system which falls under one of the four statutory categories of patent eligible subject matter. Claim 2 incorporates the features of claim 1.
Claim 2
Step 2A Prong 1: Calculate a similarity degree indicating a degree of similarity between one piece of artificial data out of the plurality of pieces of artificial data and the user data is a mathematical calculation. In the specification, paragraph [0017] discloses calculating a cosine similarity to determine similarity.
Determine whether the similarity degree is equal to or higher than a predetermined value is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Delete the one piece of artificial data when determination is made that the similarity degree is equal to or higher than the predetermined value is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2 and Step 2B: A generator configured to generate, by generative artificial intelligence trained using user data including information with which an individual is identifiable, a plurality of pieces of artificial data that is used to train a computational model different from the generative artificial intelligence and does not include information with which an individual is identifiable amount to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). Applying generative AI to generate artificial training data, and then training a different computational model based on the artificial training data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A calculator, a determiner, and a deleter amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are generic computer functions as disclosed that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated. Calculate the similarity degree based on the coordinate information included in the user data and the coordinate information included in the one piece of artificial data is a mathematical calculation.
Step 2A Prong 2 and Step 2B: Each of the user data and the plurality of pieces of artificial data is image data including a face amounts to a mere field of use and technological environment under MPEP 2106.05(h).
Each of the user data and the plurality of pieces of artificial data includes coordinate information indicating coordinates of one or more feature points related to the face amounts to a mere field of use and technological environment under MPEP 2106.05(h).
The calculator amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated.
Step 2A Prong 2: A database including artificial data in which the similarity degree indicating the degree of similarity to the user data is lower than the predetermined value out of the plurality of pieces of artificial data amounts to insignificant extra-solution activity under MPEP 2106.05(g).
A trainer configured to train the computational model using the artificial data included in the database amount to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Step 2B: A database including artificial data in which the similarity degree indicating the degree of similarity to the user data is lower than the predetermined value out of the plurality of pieces of artificial data is analogous to storing and retrieving information in memory, which the courts have recognized is a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
A trainer configured to train the computational model using the artificial data included in the database amount to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated. Extract a feature amount of the user data from the user data is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Calculate the similarity degree using the feature amount and the one piece of artificial data is a mathematical calculation. In the specification, paragraph [0017] discloses calculating a cosine similarity to determine similarity.
Step 2A Prong 2: An extractor and a calculator amount to generic computer components to apply the abstract ideas on a generic computer under MPEP 2106.05(f). A storage configured to store the feature amounts to insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: An extractor and a calculator amount to generic computer components to apply the abstract ideas on a generic computer under MPEP 2106.05(f). A storage configured to store the feature is analogous to storing and retrieving information in memory, which the courts have recognized is a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fridental et al. (US 20190325318 A1).
Regarding claim 1, Fridental teaches: An information processing system comprising a generator configured to generate, by generative artificial intelligence trained using user data including information with which an individual is identifiable, a plurality of pieces of artificial data that is used to train a computational model different from the generative artificial intelligence and does not include information with which an individual is identifiable. ([0042]-[0043], [0048], lines 1-6, [0049]-[0050], [0056]-[0057]. The limitation of training generative artificial intelligence is taught by data learning 112 which trains a generative adversarial network (GAN) according to [0049]. A “plurality of pieces of artificial data” is synthesized data 161 including a plurality of synthesized images, and a “computational model” is task learner engine 110.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Fridental et al. (US 20190325318 A1) in view of Khirman et al. (US 11631208 B1).
Regarding claim 2, Fridental teaches: The information processing system according to claim 1, further comprising:
Fridental teaches the plurality of pieces of artificial data at [0050], lines 8-12. However, Fridental does not explicitly teach: a calculator configured to calculate a similarity degree indicating a degree of similarity between one piece of artificial data out of the plurality of pieces of artificial data and the user data; a determiner configured to determine whether the similarity degree is equal to or higher than a predetermined value; and a deleter configured to delete the one piece of artificial data when determination is made that the similarity degree is equal to or higher than the predetermined value.
But Khirman teaches: a calculator configured to calculate a similarity degree indicating a degree of similarity between one piece of artificial data
a determiner configured to determine whether the similarity degree is equal to or higher than a predetermined value; and (C. 18, L. 14-19)
a deleter configured to delete the one piece of artificial data when determination is made that the similarity degree is equal to or higher than the predetermined value. (C. 18, L. 14-19 implies deleting the anonymous image when the similarity value is above the threshold.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have compared Fridental’s private data and synthesized data using Khirman’s machine learning model that outputs a similarity value. A motivation for the combination is that an objective measure helps ensure that a human observer does not recognize the identity of the anonymous as that of the real person depicted in the real image. (Khirman, C. 5, L. 61 to C. 6, L. 3)
Regarding claim 4, the combination of Fridental and Khirman teaches: The information processing system according to claim 2, further comprising:
Fridental teaches: a database including artificial data, and the plurality of pieces of artificial data ([0043], [0049] where the stored synthesized data 161 amounts to a database.)
a trainer configured to train the computational model using the artificial data included in the database. ([0050], [0056]-[0057])
However, Fridental does not explicitly teach: a database including artificial data in which the similarity degree indicating the degree of similarity to the user data is lower than the predetermined value out of the plurality of pieces of artificial data; and
But Khirman teaches: the similarity degree indicating the degree of similarity to the user data is lower than the predetermined value out of the (C. 18, L. 3-19, where one piece of artificial data is an anonymous image, and user data is a real image.)
A motivation for the combination is the same as the motivation given for claim 2.
Regarding claim 5, the combination of Fridental and Khirman teaches: The information processing system according to claim 2, further comprising:
However, Fridental does not explicitly teach: an extractor configured to extract a feature amount of the user data from the user data; and a storage configured to store the feature amount, wherein the calculator is configured to calculate the similarity degree using the feature amount and the one piece of artificial data.
But Khirman teaches: an extractor configured to extract a feature amount of the user data from the user data; and (C. 13, L. 39-57 and C. 14, L. 21-35 discloses extracting a real facial region (“a feature amount”) from a real image.)
a storage configured to store the feature amount, wherein the calculator is configured to calculate the similarity degree using the feature amount and the one piece of artificial data. (C. 11, L. 63 to “data” in 64 discloses a data storage device. C. 17, L. 20-28 and C. 18, L. 3-19 disclose that the real facial region is incorporated into both the real image and synthetic image, and calculating a similarity value between the real image and synthetic image. Thus the calculation uses the real facial region and the synthetic image. The real facial region would be stored in the data storage device.)
A motivation for the combination is the same as the motivation given for claim 2.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Fridental et al. (US 20190325318 A1) in view of Khirman et al. (US 11631208 B1) and Uhr et al. (US 20220029825 A1).
Regarding claim 3, the combination of Fridental and Khirman teaches: The information processing system according to claim 2, wherein:
Fridental teaches: each of the user data and the plurality of pieces of artificial data is image data including a face; ([0050], line 1 to col. 2, line 2)
However, Fridental does not explicitly teach: each of the user data and the plurality of pieces of artificial data includes coordinate information indicating coordinates of one or more feature points related to the face; and the calculator is configured to calculate the similarity degree based on the coordinate information included in the user data and the coordinate information included in the one piece of artificial data.
But Khirman teaches: each of the user data
the calculator is configured to calculate the similarity degree based on
A motivation for the combination is the same as the motivation given for claim 2.
Although Khirman teaches applying landmark detection to a real image, and separately teaches evaluating similarity between a real image and synthetic images, Fridental and Khirman do not explicitly teach: calculate the similarity degree based on the coordinate information included in the user data and the coordinate information included in the one piece of artificial data.
But Uhr teaches: calculate the similarity degree based on the coordinate information included in the user data and the coordinate information included in the one piece of artificial data. ([0081]-[0082] and Figs. 4-5 disclose comparing facial feature points extracted from a captured facial image to facial feature points extracted from a registered facial image. Regarding “artificial data,” [0083]-[0085] and [0087]-[0089] discloses that the captured face might be artificial.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Uhr’s face comparison technique to Fridental and Khirman. A motivation for the combination is that facial feature points comprise less data than sensor data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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