DETAILED ACTION
This action is responsive to the application filed 11/27/2023.
Claims 1-17 are pending.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a system comprised of a plurality of “units.” The claims do not recite any particular structural elements, and the recited units carry out functions which can be carried out in embodiments where the “units” are software units. Therefore, the broadest reasonable interpretation of these claims includes an embodiment where the claims are software per se, which does not constitute a process, machine, manufacture, or composition of matter within the scope of the statute. As the broadest reasonable interpretation of the claims includes non-statutory embodiments, the claims must be rejected.
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.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ladvocat Cintra, U.S. PGPUB No. 2020/0104698 (“Cintra”), in view of Rodriguez, U.S. PGPUB No. 2024/0220851 (“Rodriguez”).
With regard to Claim 1, Cintra teaches an inspection system comprising:
a first measurement unit configured to measure a target ([0053] describes a recording device; [0060] describes that a recording device cvan include models that are used to classify objects in a media file);
a first determination unit configured to make a first determination on whether the target includes a predetermined object using a first machine learning model based on a measurement result by the first measurement unit ([0060] describes that the device includes machine learning models to carry out tasks of identifying specific objects in media files);
a second measurement unit configured to measure the target ([0078] describes a second media recording device, where [0060] describes the recording devices as identifying objects); and
a second determination unit configured to make a second determination on whether the target includes the predetermined object based on a measurement result by the second measurement unit ([0060] describes that the device includes machine learning models to carry out tasks of identifying specific objects in media files).
Cintra, in view of Rodriguez teaches a processing unit configured to generate first update data of the first machine learning model based on a result of the second determination and transmit the first update data to the first determination unit. Cintra teaches at [0058] that stored machine learning models on recording devices can be updated. Rodriguez teaches at [0038] that a ML model on a device can be updated based on additional data and input labels by a user on the device. [0040] describes that a second device can receive the updated model, and [0044] describes an additional, third device receiving the model, thereby enabling an update to a model at one device to also update models on other devices.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Rodriguez with Cintra. One of skill in the art would have sought the combination, to improve system functioning by enabling ongoing updates to models shared across devices, thereby ensuring devices have accurate machine learning models carrying out recognition tasks.
Claim 17 recites a method which is carries out by the inspection system of Claim 1, and is similarly rejected.
With regard to Claim 2, Cintra teaches that the first determination unit is configured to update the first machine learning model based on the first update data. [0058] describes that a model on a device can be adaptive, such that the device can change the model after processing new input data. Therefore, Cintra teaches a device updating its model when receiving update data.
With regard to Claim 3, Cintra teaches that the first determination unit is configured to make the first determination again after updating the first machine learning model. [0110] describes that a recording device can apply an updated model subsequent to a received update, thereby indicating that determinations of an object are also made after the model is updated.
With regard to Claim 4, Cintra teaches that the second determination unit is configured to make the second determination using a second machine learning model based on the measurement result by the second measurement unit. [0060] describes that recording devices include machine learning models for making determinations.
With regard to Claim 5, Cintra teaches that the first measurement unit is configured to obtain a first feature amount of the target, the second measurement unit is configured to obtain a second feature amount of the target, and the second feature amount is different from the first feature amount. [0043] describes that image sensors and other sensors obtain values, such as pixel values of objects, audio signals, motion related values, etc. [0054] describes that the processor uses the data for input to the machine learning models. As the first and second elements are contained in separate recording devices, and can be different sensor types, the features and feature amounts will be different.
With regard to Claim 6, Cintra teaches that the first measurement unit is configured to obtain a feature amount of the target at a first level of detail, the second measurement unit is configured to obtain the feature amount at a second level of detail, and the second level of detail is higher than the first level of detail. [0033] describes different types of recording devices used with the system, therefore enabling one recording device such as the second, to capture and process media at a higher level of detail than another device.
With regard to Claim 7, Cintra teaches that reliability of the second determination is higher than reliability of the first determination. Cintra teaches at [0060] that devices use machine learning models to identify objects. Therefore, on of skill in the art would understand that a second device can make more reliable determinations, such as when data is of better quality.
With regard to Claim 8, Cintra teaches that the second measurement unit is configured to measure the target after the first measurement unit measures the target. [0071] describes that media data can be shared among devices and processed using machine learning algorithms. As a second device can identify objects in parts of media files later than that of another model, a second unit is configured to measure a target after the first.
With regard to Claim 9, Cintra teaches that each of the first measurement unit and the second measurement unit comprises a radar, a metal detector, a liquid detector, an X-ray diagnosis device, or a camera. [0041] describes that recording devices can be many types of cameras.
With regard to Claim 10, Cintra teaches that the second measurement unit comprises a metal detector or a camera. [0041] describes that recording devices can be many types of cameras.
With regard to Claim 11, Cintra teaches a third measurement unit configured to measure the target; and a third determination unit configured to make a third determination on whether the target includes the predetermined object based on a measurement result by the third measurement unit, wherein the third measurement unit is configured to measure the target after the first measurement unit measures the target, and the second measurement unit is configured to measure the target after the third measurement unit measures the target. [0080] describes three recording devices, where [0071] describes that media data can be shared among devices and processed using machine learning algorithms. As a second device can identify objects in parts of media files later than that of another model, a second unit is configured to measure a target after the first. Therefore, a third measurement unit can measure a particular target after other devices have also done so in some instances.
With regard to Claim 12, Cintra, in view of Rodriguez teaches that the third determination unit is configured to make the third determination using a third machine learning model based on the measurement result by the third measurement unit, the processing unit is configured to generate second update data of the third machine learning model based on a result of the second determination and transmit the second update data to the third determination unit, and the third determination unit is configured to update the third machine learning model based on the second update data.
Cintra teaches at [0058] that stored machine learning models on recording devices can be updated; [0080] describes three devices that store machine learning models. Rodriguez teaches at [0038] that a ML model on a device can be updated based on additional data and input labels by a user on the device. [0040] describes that a second device can receive the updated model, and [0044] describes an additional, third device receiving the model, thereby enabling an update to a model at one device to also update models on other devices.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Rodriguez with Cintra. One of skill in the art would have sought the combination, to improve system functioning by enabling ongoing updates to models shared across devices, thereby ensuring devices have accurate machine learning models carrying out recognition tasks.
With regard to Claim 13, Cintra teaches that the third determination unit is configured to make the third determination again after updating the third machine learning model. [0110] describes that a recording device can apply an updated model subsequent to a received update, thereby indicating that determinations of an object are also made after the model is updated.
With regard to Claim 14, Cintra teaches that the first measurement unit is configured to obtain a first feature amount of the target, the second measurement unit is configured to obtain a second feature amount of the target, the third measurement unit is configured to measure a third feature amount of the target, the second feature amount is different from the first feature amount, and the third feature amount is different from the second feature amount. [0043] describes that image sensors and other sensors obtain values, such as pixel values of objects, audio signals, motion related values, etc. [0054] describes that the processor uses the data for input to the machine learning models. [0080] describes three devices. Therefore, as the elements are contained in three separate recording devices, and can be different sensor types, the features and feature amounts will be different.
With regard to Claim 15, Cintra teaches that the first measurement unit is configured to measure a feature amount of the target at a first level of detail, the third measurement unit is configured to measure the feature amount at a third level of detail, the second measurement unit is configured to measure the feature amount at a second level of detail, and the second level of detail is higher than the first level of detail and the third level of detail. [0033] describes different types of recording devices used with the system, therefore enabling each of the three recording devices described at [0080] to capture and process media at different level of detail from one another, as they are different types of recording devices.
With regard to Claim 16, Cintra teaches that reliability of the second determination is higher than reliability of the first determination and reliability of the third determination. Cintra teaches at [0060] that devices use machine learning models to identify objects. Therefore, on of skill in the art would understand that a second device can make more reliable determinations, such as when data is of better quality, than those of a first and third device when there are 3 recording devices as described at [0080].
Conclusion
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/KEITH D BLOOMQUIST/Primary Examiner, Art Unit 2171
2/22/2026