DETAILED ACTION
This Office Action is responsive to the claims filed on June 29, 2026. Claims 1-2, 4-5, and 7-18 are under examination. Claims 1, 13, and 15-16 are independent claims.
Claims 1-2, 4-5, and 7-18 are rejected under 35 USC 101.
Claims 2, 14, and 17-18 are rejected under 35 U.S.C. 112(d).
Claims 1-2, 4-5, and 7-18 are allowable over art.
Response To Amendments And Arguments
35 USC 112(b): The Amendments and Arguments have been considered and are persuasive. The rejections are withdrawn.
35 USC 103: The Applicant’s amendments and arguments have been considered and are persuasive. The rejections are withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2, 14, 17, and 18 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The features of claims 2, 14, 17, and 18 are entirely taught by the newly amended generating steps of their respective independent claims. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-2 and 4-5, and 7-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims amount to generic training of a machine learning model to take in data and output data. This is akin to Electric Power Group and Example 47, claim 2, which was found to be ineligible. Diagnosis of a condition is an abstract idea. The additional limitations presented in the claims fail to integrate the diagnosis into a practical application at Step 2A, Prong 2, and fail to combine with the other elements of the claims to provide significantly more than the abstract idea. For example, the model training is a supervised learning model training (generic computer operation recited at a high level; a longstanding practice, and well-understood, routine, and conventional activity) with the type of data used for training and for inference merely limiting the abstract idea to a particular field. As the claims are written, the claims provide no improvement to any technology, whether it be machine learning (for which the claims use basic supervised learning) or camera repair (as no repair is done in the claims). The information to determine a current condition of a camera is input, and the assessment of the current condition of the camera is output. The Applicant is invited to amend the claims to recite an improvement to machine learning technology, which would have to include an improvement to how the model is trained, or an improvement to camera repair technology, which would include an affirmative repair step with a mechanism that uses the data output by the machine learning model. Short of these, it is unlikely that the claims will overcome 35 USC 101, without explicitly removing language that recites an abstract idea. Be careful to limit any amendments made to ones supported by the original specification.
Independent Claims 1, 13, and 15-16
Claim 1 (Statutory Category – Machine)
Claim 13 (Statutory Category – Machine)
Claim 15 (Statutory Category – Machine)
Claim 16 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process, which is an abstract idea.
Claim 1 recites:
generate the deterioration in the expected lens performance of the target interchangeable lens by providing first information on a usage history of the target interchangeable lens after usage of the target interchangeable lens by the user and second information on an expected lens performance of the target interchangeable lens prior to the usage of the target interchangeable lens by the user as input data to [the mind]. (Mental Process – The generation of information based on other information (inference) is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so this is an evaluation, a mental process, an abstract idea.)
Claim 13 recites:
generating the deterioration in the expected lens performance of the target interchangeable lens by providing first information on a usage history of the target interchangeable lens after usage of the target interchangeable lens by the user and second information on an expected lens performance of the target interchangeable lens prior to the usage of the target interchangeable lens by the user as input data to [the mind]. (Mental Process – The generation of information based on other information (inference) is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so this is an evaluation, a mental process, an abstract idea.)
Claim 15 recites:
generating the deterioration in the expected lens performance of the target interchangeable lens by providing first information on a usage history of the target interchangeable lens after usage of the target interchangeable lens by the user and second information on an expected lens performance of the target interchangeable lens prior to the usage of the target interchangeable lens by the user as input data to [the mind]. (Mental Process – The generation of information based on other information (inference) is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so this is an evaluation, a mental process, an abstract idea.)
Claim 16 recites
generate third information on an estimated lens performance of the target interchangeable lens after usage of the target interchangeable lens by the user, based on the first information on the a usage history of the target interchangeable lens after usage of the target interchangeable lens by the user, second information on an expected lens performance of the target interchangeable lens prior to the usage of the target interchangeable lens by the user, and [the mind]; and (Mental Process – The generation of information based on other information (inference) is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so this is an evaluation, a mental process, an abstract idea.)
perform a predetermined determination on the target interchangeable lens based on the third information. (Mental Process – Generically determining something about a lens is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so this is an evaluation, a mental process, an abstract idea.)
Claims 1, 13, and 15-16 recite mental processes.
Claims 1, 13, and 15-16 recite abstract ideas.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
The additional limitations:
Mere Data Gathering MPEP 2106.05(g)
Claim 1 recites:
acquire first information on a usage history of a first interchangeable lens attachable to a camera body to capture an image, the first interchangeable lens having at least one optical element, after shipment and usage of the first interchangeable lens by a user, the usage history including information on an operating environment of first interchangeable lens, the information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
acquire second information on an expected lens performance of the first interchangeable lens or a reference interchangeable lens different from the first interchangeable lens and a target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
acquire a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquire fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in an expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 13 recites:
a first acquisition process for acquiring first information on a usage history a first interchangeable lens attachable to a camera body to capture an image, the fist interchangeable lens having at least one optical element, after shipment and usage of the first interchangeable lens by a user, the usage history including information on an operating environment of the first interchangeable lens, the information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
a second acquisition process for acquiring second information on an expected lens performance of the first interchangeable lens or a reference interchangeable lens different from the first interchangeable lens and a target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
a first processing process for
acquiring a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquiring fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model, and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 15 recites:
a first acquisition process for acquiring first information on a usage history of a first interchangeable lens attachable to a camera body to capture an image, the first interchangeable lens having at least one optical element, after shipment and usage of first interchangeable lens by a user, the usage history including information on an operating environment of the first interchangeable lens, information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
a second acquisition process for acquiring second information on an expected lens performance of the first interchangeable lens, the target interchangeable lens, or a reference interchangeable lens different from the first interchangeable lens and the target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
a first processing process for:
acquiring a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquiring fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 16 recites:
acquire first information on a usage history of a first interchangeable lens attachable to a camera body to capture an image, the first interchangeable lens having at least one optical element, after shipment and usage of the first interchangeable lens by a user, the usage history including information on an operating environment of the first interchangeable lens, the information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
acquire second information in which on an expected lens performance of the first interchangeable lens or a reference interchangeable lens different from the first interchangeable lens and the target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
acquire a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquiring fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information;
These limitations merely gather existing information for training of a model and for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, analogous examples are provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions” “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Collecting data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). These limitations add nothing more than insignificant extra solution activity, so they fail to integrate the abstract idea into a practical application in Step 2A Prong Two. NOTE ON THE PROVIDING STEPS – The claim does not positively recite the training of the model, but merely the provision of information for that purpose, so it is mere data gathering.
Apply It/Generic Computer Implementation MPEP 2106.05(f)
Claim 1:
An information processing apparatus, comprising:
a memory configured to store a program; and
a processor communicatively connected to the memory and configured to execute the program to: […]
[…] first/second machine learning model […]
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in an expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 13:
An information processing method […]
[…] first/second machine learning model […]
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model, and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 15:
A non-transitory computer-readable storage medium storing a program for causing a computer to execute an information processing method […]
[…] first/second machine learning model […]
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 16:
An information processing system comprising:
a memory configured to store a program; and
a processor communicatively connected to the memory and configured to execute the program […]
[…] first/second machine learning model […]
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information;
These elements are generic computing implementations recited at a high level. As such, under MPEP 2106.05(f), the computer implementation implements the recited abstract idea on a generic computer, and does not integrate the abstract idea into a practical application in Step 2A Prong Two. NOTE ON PROVIDING STEP: Again, the step merely recites provision of information, which should be considered mere data gathering. However, should it be found otherwise, the use of labeled data in a supervised machine learning model training (without reciting any particular improvement in how the supervised learning is executed) is now a generic computing operation.
Mere Limitation To A Technological Field
The data used in the training and inference steps merely limit the abstract idea to a particular field, so they fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claims 1, 13, and 15-16 fails to recite additional limitations that integrate the abstract idea into a practical application.
Claims 1, 13, and 15-16 are directed to the abstract idea.
Step 2B: Claims provide an Inventive Concept?
No.
The additional limitations:
Well-Understood, Routine, and Conventional Activity (WURC) MPEP 2106.05(d)
Claim 1 recites:
acquire first information on a usage history of a first interchangeable lens attachable to a camera body to capture an image, the first interchangeable lens having at least one optical element, after shipment and usage of the first interchangeable lens by a user, the usage history including information on an operating environment of first interchangeable lens, the information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
acquire second information on an expected lens performance of the first interchangeable lens or a reference interchangeable lens different from the first interchangeable lens and a target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
acquire a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquire fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in an expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 13 recites:
a first acquisition process for acquiring first information on a usage history a first interchangeable lens attachable to a camera body to capture an image, the fist interchangeable lens having at least one optical element, after shipment and usage of the first interchangeable lens by a user, the usage history including information on an operating environment of the first interchangeable lens, the information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
a second acquisition process for acquiring second information on an expected lens performance of the first interchangeable lens or a reference interchangeable lens different from the first interchangeable lens and a target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
a first processing process for
acquiring a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquiring fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model, and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 15 recites:
a first acquisition process for acquiring first information on a usage history of a first interchangeable lens attachable to a camera body to capture an image, the first interchangeable lens having at least one optical element, after shipment and usage of first interchangeable lens by a user, the usage history including information on an operating environment of the first interchangeable lens, information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
a second acquisition process for acquiring second information on an expected lens performance of the first interchangeable lens, the target interchangeable lens, or a reference interchangeable lens different from the first interchangeable lens and the target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
a first processing process for:
acquiring a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquiring fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 16 recites:
acquire first information on a usage history of a first interchangeable lens attachable to a camera body to capture an image, the first interchangeable lens having at least one optical element, after shipment and usage of the first interchangeable lens by a user, the usage history including information on an operating environment of the first interchangeable lens, the information on the operating environment including at least one of a drive or control information, temperature information, humidity information, geographic position information indicating a geographic location of the first interchangeable lens, and information about an external force applied to the first interchangeable lens;
acquire second information in which on an expected lens performance of the first interchangeable lens or a reference interchangeable lens different from the first interchangeable lens and the target interchangeable lens, prior to the usage of the first interchangeable lens or the target interchangeable lens by the user; and
acquire a first machine learning model, in which parameters are adjustable, to generate third information on an estimated lens performance of the first interchangeable lens;
acquiring fourth information on actual lens performance of the first interchangeable lens after usage of the first interchangeable lens by the user;
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information;
These limitations are receiving or transmitting data, so they are analogous to the examples cited in MPEP 2106.05(d): “i. Receiving or transmitting data over a network”; “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “v. Electronically scanning or extracting data from a physical document” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”) representing well-understood, routine, and conventional (WURC) activity, which, under MPEP 2106.05(d), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept. NOTE: Again, the provide limitation merely provides data. It does not affirmatively train the model as a step. However, should it be found otherwise, even if the claim did positively recite the training, generic supervised machine learning with labeled data (without any explicit improvement) is also WURC for the same reasons.
Apply It/Generic Computer Implementation MPEP 2106.05(f)
Claim 1:
An information processing apparatus, comprising:
a memory configured to store a program; and
a processor communicatively connected to the memory and configured to execute the program to: […]
[…] first/second machine learning model […]
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in an expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 13:
An information processing method […]
[…] first/second machine learning model […]
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model, and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 15:
A non-transitory computer-readable storage medium storing a program for causing a computer to execute an information processing method […]
[…] first/second machine learning model […]
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 16:
An information processing system comprising:
a memory configured to store a program; and
a processor communicatively connected to the memory and configured to execute the program […]
[…] first/second machine learning model […]
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information;
These elements are generic computing implementations recited at a high level. As such, under MPEP 2106.05(f), the computer implementation implements the recited abstract idea on a generic computer, and fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B. NOTE ON PROVIDING STEP: Again, the step merely recites provision of information, which should be considered mere data gathering. However, should it be found otherwise, the use of labeled data in a supervised machine learning model training (without reciting any particular improvement in how the supervised learning is executed) is now a generic computing operation.
Apply It/Generic Computer Implementation MPEP 2106.05(f)
Claim 1:
An information processing apparatus, comprising:
a memory configured to store a program; and
a processor communicatively connected to the memory and configured to execute the program to: […]
[…] first/second machine learning model […]
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in an expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 13:
An information processing method […]
[…] first/second machine learning model […]
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model, and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 15:
A non-transitory computer-readable storage medium storing a program for causing a computer to execute an information processing method […]
[…] first/second machine learning model […]
providing the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the machine learning model and providing the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
Claim 16:
An information processing system comprising:
a memory configured to store a program; and
a processor communicatively connected to the memory and configured to execute the program […]
[…] first/second machine learning model […]
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in the expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information;
These elements are generic computing implementations recited at a high level. As such, under MPEP 2106.05(f), the computer implementation implements the recited abstract idea on a generic computer, and fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B. NOTE ON PROVIDING STEP: Again, the step merely recites provision of information, which should be considered mere data gathering. However, should it be found otherwise, the use of labeled data in a supervised machine learning model training (without reciting any particular improvement in how the supervised learning is executed) is now a generic computing operation.
Mere Limitation To A Technological Field
The data used in the training and inference steps merely limit the abstract idea to a particular field, so they fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Claims 1, 13, and 15-16 fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Claims 1, 13, and 15-16 are ineligible.
Dependent Claims
Dependent claims 2, 4-5, 7-12, 14, and 17-18 are also ineligible for at least the following reasons.
Claims 2, 14, and 17-18
Claims 2, 14, and 17-18 repeat features of their respective independent claims, respectively (see 35 USC 112(d) rejection), so they fail to confer eligibility for at least the same reasons as the independent claims from which claims 2, 14, and 17-18 depend.
Claims 2 and 14 are ineligible.
Claim 4
wherein the second information includes at least one of environmental test information, durability test information, load test information, vibration test information, and impact test information of the first interchangeable lens, the target interchangeable lens, or the reference interchangeable lens.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 4 fails to provide any additional limitations that confer eligibility.
Claim 4 is ineligible.
Claim 5
wherein the second information includes at least one of optical performance information, operation performance information, dust-proof or drip-proof performance information, evaluation information in an appearance or operation state, and degree of wear information of components of the first interchangeable lens, the target interchangeable lens, or the reference interchangeable lens after a predetermined test has been further performed on the first interchangeable lens, the target interchangeable lens, or the reference interchangeable lens.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 5 fails to provide any additional limitations that confer eligibility.
Claim 5 is ineligible.
Claim 7
The information processing apparatus according to Claim 1, wherein the fourth information includes at least one of optical performance information, operation performance information, dust-proof or drip-proof performance information, and appearance or operation state information of the first interchangeable lens or the reference interchangeable lens.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 7 fails to provide any additional limitations that confer eligibility.
Claim 7 is ineligible.
Claim 8
wherein the first information or the fourth information is acquired when a predetermined inspection is performed after shipment of the first interchangeable lens to the user.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 8 fails to provide any additional limitations that confer eligibility.
Claim 8 is ineligible.
Claim 9
wherein the second information further includes fifth information including at least any one of design-time information, manufacturing-time information, and catalog data of the first interchangeable lens or the reference interchangeable lens.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 9 fails to provide any additional limitations that confer eligibility.
Claim 9 is ineligible.
Claim 10
wherein the first information further includes sixth information including at least one of optical performance information, operation performance information, dust-proof or drip-proof performance information, and appearance or operation state information of the first interchangeable lens.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 10 fails to provide any additional limitations that confer eligibility.
Claim 10 is ineligible.
Claim 11
wherein the third information includes at least one of optical performance information, operation performance information, dust-proof or drip-proof performance information, and appearance or operation state information of the target interchangeable lens.
This merely describes an element of the data gathering in claim 1, so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d) for at least the same reasons.
Further, this merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 11 fails to provide any additional limitations that confer eligibility.
Claim 11 is ineligible.
Claim 12
wherein the processor is further configured to execute the program to
This is a generic computing element recited at a high level, so it fails to confer eligibility under MPEP 2106.05(f).
perform a lens quality determination on the target interchangeable lens based on the third information.
Determining lens quality based on information is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea. Therefore, this feature does not provide any additional limitations.
Claim 12 fails to provide any additional limitations that confer eligibility.
Claim 12 is ineligible.
Claims Allowable Over Art
Claims 1-2 and 4-5, and 7-18 are allowable over the prior art.
The following is an examiner’s statement of reasons for allowance over art.
The limitations of the independent claims include (or analogously include),
provide the first information on the usage history of the first interchangeable lens and the second information on the expected lens performance of the first interchangeable lens or the reference interchangeable lens as input data to the first machine learning model, and provide the fourth information as teacher data to the first machine learning model, to perform learning to adjust the parameters of the first machine learning model and generate a second machine learning model for estimating a deterioration in an expected lens performance of the target interchangeable lens after the usage of the target interchangeable lens by the user, the second machine learning model being generated by updating one or more coupling weight coefficients between nodes of the first machine learning model based on an error between the generated third information and the acquired fourth information; and
generate the deterioration in the expected lens performance of the target interchangeable lens by providing first information on a usage history of the target interchangeable lens after usage of the target interchangeable lens by the user and second information on an expected lens performance of the target interchangeable lens prior to the usage of the target interchangeable lens by the user as input data to the second machine learning model
in combination with the remaining limitations.
The closest prior art references of record are Montminy, Kubo, Hania, and Mullis. Montminy teaches a system for automatically generating camera health records using machine intelligence. The Montminy system stores records over time indicating the health of the camera and relative differences in health over time of the camera. Kubo teaches training a machine learning model using supervised learning and using optical component data to determine lens health. Hania teaches specific conditions that can affect camera health, including environmental factors and physical impacts. Mullis teaches accounting for calibration or other manufacturing issues that may have originated in a factory when assessing the health of a lens.
The references alone or in combination fail to disclose that first information on the usage history of the lens and second information on the expected lens performance prior to usage of the lens are used as inputs to a machine learning model to predict the label data, which is the actual condition of the camera after the usage, or the use of this train model for inferences on the current condition of a camera based on its history and the expected quality of the lens before it was used, without the use of impermissible hindsight. Therefore, claims 1-2 and 4-5, and 7-18 as drafted, are rendered neither obvious nor anticipated by the prior art of record and the available field of prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(From A Prior Office Action)
NPL: “11 Ways to Improve the Sharpness of Your Images, Part 1” by Daniel (Daniel teaches that optical design of a lens and missed focus are parameters that affect image sharpness. It would be obvious to use these as input or output parameters in a machine learning model.)
NPL: “11 Ways to Improve the Sharpness of Your Images, Part 2” by Daniel (Daniel teaches that depth of field and camera shake are parameters that affect image sharpness. It would be obvious to use these as input or output parameters in a machine learning model.)
NPL: “11 Ways to Improve the Sharpness of Your Images, Part 3” by Daniel (Daniel teaches that noise and atmospheric distortion are parameters that affect image sharpness. It would be obvious to use these as input or output parameters in a machine learning model.)
NPL: “11 Ways to Improve the Sharpness of Your Images, Part 4” by Daniel (Daniel teaches that mirror slap, shutter vibration, and diffraction are parameters that affect image sharpness. It would be obvious to use these as input or output parameters in a machine learning model.)
NPL: “11 Ways to Improve the Sharpness of Your Images, Part 5” by Daniel (Daniel teaches that image stabilization is a parameter that affects image sharpness. It would be obvious to use this as an input or output parameter in a machine learning model.)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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