DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The examiner maintains the rejection under 35 USC 112 because the training data is at the core of a machine learning system and the training data is completely unknown and could be updated after the date of the invention.
Some additional points:
In a machine learning system, the training data is everything. Without training data a machine learning system does nothing and the weights of a neural network have no value.
The capability of a machine learning system scales up based on the quantity of learning data it is trained on. None of the training data is known or disclosed and so the invention cannot be replicated based on the disclosure.
When new targets arise that are unknown, the claims say that the machine learning system learns on these new targets. How does that work? If they are unknown, then it some data source or active user must tell the machine learning system what the new target is.
There is the problem that the scope of the claims is variable because the invention can be dramatically improved within the scope of the claims with a lot more training data. The claims are supposed to cover just what the applicant was in possession of at the time of the invention, but that is not the case here. The applicant could have a poorly functioning system that hardly works at all at the time of the invention and then improve the system over many years with training data and eventually end up with a highly functioning system at some point months or years in the future. In that case, the patent is a placeholder for a future technology that doesn’t exist yet. That is not how patents are supposed to work.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-20 are more specifically rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01.
The central functional system for the instant invention is a machine learning system of deep learning (typically based on on a neural network) that takes ballistic data and shooting result data as input and control outputs for the shooting system.
There is a great deal that is omitted in such a system, and the system acts as kind of a ‘black box’ where the essential functions of the invention are hidden inside the neural network, whose structure is unclear. Some elements shape the final invention which are unclear and unclaimed:
The detailed structure of the neural network which ultimately controls the invention
The TRAINING DATA is what actually builds the neural network, and this is unknown and unclaimed. A neural network that has not been trained on training data does nothing and has no function, and so the training data, which is undisclosed, is an essential part of the invention.
Additionally, the invention is not fixed at the time of filing. Rather, through continuous training and the input of more training data, the invention changes and improves. Patents are supposed to cover a fixed invention, not one that is continually improving.
How are two neural network-based ballistic control systems even to be compared? The core system, as the examiner has noted, is essentially a “black box.”
Additionally, one of ordinary skill in the art would NOT be able to recreate the instant invention based on the specification for many reasons, including that the neural network is structure is not known in detail and the training data, which is essential for the creation of the invention, is not known.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sitrick et al. (US 11,629,934).
Sitrick et al. teaches (column 3, lines 59+):
“(31) A plethora of targeting sensors allows a wide spectrum of sensing beyond the visible spectrum, such as IR, SPI (Spacial Phased Imaging), UV (ULTRAVIOLET), X-Ray, Microwave, Thermal, 3D sensor, Visible light, Radar, Sonar, LIDAR, etc. [For further examples, see the catalog on “Image Sensors”, from Hamamaatsu, December, 2011).] Targeting sensors allow shooting at targets through fog, smoke, rain, and other vision obstructing conditions. This effectively provides an ‘all weather/all conditions’ targeting system. The sensors can also be used to identify not only a target, but the type of target. One means of doing this utilizes neural net pattern recognition means to identify the type of target (person, animal, tank, etc.)
(32) Neural nets can be used both to identify targets, and to compute firing solutions. Alternatively, or additionally, traditional computing means, can be employed in the targeting subsystem for identifying and selecting targets. Neural nets can be used to both reduce the power used, and reduce the compute time for identifying and selecting a target.
(33) There is literature teaching the use of neural nets in the use of target identification and tracking. For example, IBM has been working on a new hybrid technology of blending traditional computing architectures with neural nets to achieve a ‘best of both worlds’ processing system. This system could be utilized in the targeting subsystem for identifying targets, tracking targets and computing firing solutions.”
From all this it is clear that using neural networks to both identify targets and create firing solutions is known in the art. That is what the instant invention claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL A HESS whose telephone number is (571)272-2392. The examiner can normally be reached Monday through Friday, from 9 AM to 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael G. Lee can be reached at (571)272-2398. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL A HESS/Primary Examiner, Art Unit 2876