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 .
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 1, “comprising: a. a first computing unit” and “b. a second computing unit” should read as “comprising: a first computing unit” and “a second computing unit”
Appropriate correction is required.
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.
Claim 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “relevant” in claim 1 is a relative term which renders the claim indefinite. The term “relevant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Para. [0022] in the specification of the instant application discloses “The relevant part can be interpreted as core or essential part. The relevant part of the current machine learning model is identified and replaced by the corresponding relevant part of the retrained machine learning model. In other words, the core of the retrained machine learning model is copied into the core of the current machine learning model. Hence, the second storage medium comprises the relevant parts of the retrained machine learning model after injection and can be applied in the field on application data in a reliable and robust fashion.” However, it is unclear what is considered “core” or “essential” as what constitutes as “core” to a particular ML model could be the whole architecture itself in certain paradigms.
Claims 2-14 are further rejected on virtue of their dependencies to claim 1.
Claim 6 recites the limitation "the input interface." There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites “wherein the current machine learning model and the retrained machine learning model are semantically equivalent, configured as neural networks, even more configured as feedforward neural networks, convolutional neural networks or recurrent neural networks.” It is unclear which model or if both models or other models are configured as feedforward neural networks, convolutional neural networks or recurrent neural networks. For examination purposes, Examiner interprets the claim as “the current machine learning model and the retrained machine learning model are both a same type of neural networks (ie semantically equivalent) wherein the type of neural network can be one of: feedforward neural networks, convolutional neural networks or recurrent neural networks.”
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In regards to claim 1,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm. The specification discloses that the storage medium can be a volatile storage medium (see para. [0029] and [0039] of the instant application); and the specification further discloses that the computing units can be hosted on hardware (see para. [0028]), thus, the computing unit is software and the hardware itself is not claimed. Therefore, in the BRI of the claim and in light of the specification, only software is claimed.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A – Prong 1: Judicial Exception Recited?
MPEP 2106.04(a)(2)(I) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
Yes, the claim recites a mental process, specifically:
the injection comprises an identification of the at least one relevant part of the current machine learning model and a replacement of the identified at least one relevant part of the current machine learning model by the corresponding at least one relevant part of the retrained machine learning model
This limitation encompasses an evaluation of the parts of the current machine learning model for at least one relevant part (an opinion) and providing an opinion of a replacement based on the identified relevant part(s). One of ordinary skills would be able to provide an opinion of a number of neurons or hidden layers or retaining any aspect of a given neural network to identify as relevant and marked for replacement. For example, see the tensorflow playground created by Daniel Smilkov and Shan Carter where one can copy a given neural network architecture to update a current neural network to have the same number of hidden layers and neurons with the aid of a generic computer (can also be performed with the aid of pen and paper). (Smilkov et al., (12/29/2020). “Tinker With a Neural Network Right Here in Your Browser.” playground.tensorflow.org. https://web.archive.org/web/20201229203616/https://playground.tensorflow.org/)
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Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The following steps are mere instructions to apply:
a. a first computing unit comprising a first storage medium, wherein the first computing unit is configured
b. a second computing unit comprising a second storage medium and an injection interface
preprocessing the retrained machine learning model
(Examiner interprets this limitation in light of the specification, “[0030] In a further aspect the preprocessing comprises compressing, decompressing, quantizing, optimizing, deserializing, initializing and/or testing.” Wherein preprocessing can simply be testing or training (ie optimizing) the retrained machine learning model. Thus, the BRI of the limitation encompasses merely executing the retrained machine learning model on the first computing unit)
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering/ post solution activity to be insignificant extra-solution activity. The following steps are insignificant extra-solution activities:
Mere data gathering:
wherein the retrained machine learning model is stored in the first storage medium
(The BRI of this limitation encompasses storing the retraining ML model)
wherein a current machine learning model is stored in the second storage medium
(The BRI of this limitation encompasses storing a current ML model)
Post Solution activity:
providing the retrained machine learning model
(The BRI of this limitation encompasses transmitting a retrained model wherein retrained is interpreted to be merely a trained model as the claim does not explicitly recite a training process nor a retraining process)
wherein the injection interface is configured for: injecting at least one relevant part of the retrained machine learning model after processing from the first storage medium of the first computing unit into the second storage medium of the second computing unit by the injection interface at runtime
(The BRI of this limitation encompasses transmitting part of the data stored in the first storage medium to the second storage medium wherein storing the data at the first storage medium is interpreted to be processing at the first storage medium and an interface is used to identify the relevant parts to transmit)
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
The full context of the claim encompasses generic components to provide a trained model, store the trained model in memory, identify relevant parts of the trained model for replacement to the current ML model via an interface, wherein the current ML model is stored in memory. Thus, the claim merely directs to an interaction with two ML models on a generic computer and determining what parts of the current ML model architecture to update based on the provided trained model. In other words, the BRI of the claim is receiving a new model and updating the model in memory.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and a generic device do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
In particular, the claim recites receiving and transmitting data by generic device.
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to
gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary
computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607,
610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP
Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)
(sending messages over a network); buy SAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112
USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106
(Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how
interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides
the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
(emphasis added)).
The claim further recites storing data by generic device.
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
The additional elements have been considered both individually and as an ordered
combination in the significantly more consideration.
The claim is ineligible.
In regards to claim 2,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – system (a generic computer ie embedded device, embedded control computer, edge device is claimed)
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the first computing unit is hosted on or configured as an embedded device, an embedded control computer, or an edge device
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the first computing unit is hosted on or configured as an embedded device, an embedded control computer, or an edge device
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
In regards to claim 3,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm. The BRI of the claim encompasses “wherein the first storage medium is a volatile” and thus, the first storage medium is non-transitory.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the first storage medium is a [volatile or] non-volatile storage medium
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the first storage medium is a [volatile or] non-volatile storage medium
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
In regards to claim 4,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the preprocessing comprises compressing, decompressing, quantizing, optimizing, deserializing, initializing and/or testing
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
The preprocessing is merely executing the model on the first computing unit.
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the preprocessing comprises compressing, decompressing, quantizing, optimizing, deserializing, initializing and/or testing
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
The preprocessing is merely executing the model on the first computing unit.
In regards to claim 5,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the first computing unit further comprising an input interface
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
configured for receiving a notification when the retrained machine learning model is available from a machine learning platform, other computing unit, or other technical system
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the first computing unit further comprising an input interface
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
configured for receiving a notification when the retrained machine learning model is available from a machine learning platform, other computing unit, or other technical system
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 6,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the input interface is further configured for receiving or downloading the retrained machine learning model from a machine learning platform, other computing unit, or other technical system, after notification
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the input interface is further configured for receiving or downloading the retrained machine learning model from a machine learning platform, other computing unit, or other technical system, after notification
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 7,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the first computing unit is further configured for performing at least one test based on the retrained machine learning model before the injection is performed by the first computing unit through the injection interface of the second computing unit
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
The BRI of this limitation is merely executing the retrained model on some data.
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the first computing unit is further configured for performing at least one test based on the retrained machine learning model before the injection is performed by the first computing unit through the injection interface of the second computing unit
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
The BRI of this limitation is merely executing the retrained model on some data.
In regards to claim 8,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the injection is performed in the case that the test is successful.
This limitation merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP § 2106.05(h)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the injection is performed in the case that the test is successful.
This limitation merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP § 2106.05(h)
In regards to claim 9,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – system (FPGA. ASIC provides a generic computer hardware is claimed).
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the second computing unit is configured as an accelerator component, FPGA, Field Programmable Gate Array, or ASIC, Application-Specific Integrated Circuit
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the second computing unit is configured as an accelerator component, FPGA, Field Programmable Gate Array, or ASIC, Application-Specific Integrated Circuit
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
In regards to claim 10,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm. The BRI of the claim encompasses “wherein the second storage medium is a volatile” and thus, the second storage medium is non-transitory.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the second storage medium is a [volatile or] non-volatile storage medium
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the second storage medium is a [volatile or] non-volatile storage medium
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
In regards to claim 11,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
wherein the relevant part of the retrained machine learning model is at least one internal data structure, at least one layer, at least one bias, at least one weight and/or at least one parameter
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses providing an opinion of at least one internal data structure, layer, bias and weight/parameter based on an evaluation of the retrained machine learning model. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 12,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the injection is synchronized with a control cycle clock
This limitation merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (execution of the abstract idea with a control cycle clock) and thus fails to add an inventive concept to the claims. See MPEP § 2106.05(h)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the injection is synchronized with a control cycle clock
This limitation merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (execution of the abstract idea with a control cycle clock) and thus fails to add an inventive concept to the claims. See MPEP § 2106.05(h)
In regards to claim 13,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the injection interface is configured as an Ethernet, a parallel or a serial communication interface, connected to a communication controlled of the second computing unit
This limitation directs to mere data gathering and post solution activity of insignificant extra-solution activity wherein the BRI of the claim encompasses sending data over an ethernet network. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the injection interface is configured as an Ethernet, a parallel or a serial communication interface, connected to a communication controlled of the second computing unit
This limitation directs to mere data gathering and post solution activity of insignificant extra-solution activity wherein the BRI of the claim encompasses sending data over an ethernet network. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 14,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim is directed to non-statutory subject matter and encompasses software per se. The claim does not fall within at least one of the four categories of patent eligible subject matter because the limitations are only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm.
However, Examiner notes that the claim can be amended to fall within a statutory category, thus, Examiner continues the subject matter eligibility test.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the current machine learning model and the retrained machine learning model are semantically equivalent, configured as neural networks, even more configured as feedforward neural networks, convolutional neural networks or recurrent neural networks
This limitation merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (feedforward nn/CNN/RNN) and thus fails to add an inventive concept to the claims. See MPEP § 2106.05(h)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the current machine learning model and the retrained machine learning model are semantically equivalent, configured as neural networks, even more configured as feedforward neural networks, convolutional neural networks or recurrent neural networks
This limitation merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (feedforward nn/CNN/RNN) and thus fails to add an inventive concept to the claims. See MPEP § 2106.05(h)
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Pub No. US20210390402A1 Exner et al. (“Exner”)
In regards to claim 1,
Exner teaches A technical injection system for injecting a retrained machine learning model, comprising: a. a first computing unit ie server 20 comprising a first storage medium ie storage device 21, wherein the first computing unit is configured for: providing the retrained machine learning model; and preprocessing the retrained machine learning model; wherein the retrained machine learning model is stored in the first storage medium;
Examiner interprets this limitation in light of the specification, (“[0030] In a further aspect the preprocessing comprises compressing, decompressing, quantizing, optimizing, deserializing, initializing and/or testing.”)
(Exner, “[0030] The system in FIG. 1 further comprises a server device (“server”) 20 which is connected, by wire or wirelessly, to a storage device 21 that stores at least part of a current version of the NN model 11 [wherein the retrained machine learning model ie NN model 11 is stored in the first storage medium ie storage device 21]. The server 20 and the computer 10 are operable to communicate over a wireless network 30 [preprocessing ie serializing/deserializing the retrained machine learning model]. The wireless network may be a WAN, LAN or PAN or any combination thereof. In some embodiments, the server 20 is operable to provide update data UD for the NN model 11 in the computer 10 in response to request data RD received from the computer 10 [wherein the first computing unit ie server 20 is configured for: providing the retrained machine learning model ie NN model 11].”)
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Exner teaches b. a second computing unit ie computer 10 comprising a second storage medium ie the computer storage of computer 10 and an injection interface ie wireless network 30,
(Exner, [0030], “The server 20 and the computer 10 are operable to communicate over a wireless network 30.”)
Exner teaches wherein the injection interface is configured for: injecting at least one relevant part of the retrained machine learning model after processing from the first storage medium of the first computing unit into the second storage medium of the second computing unit by the injection interface at runtime; wherein a current machine learning model is stored in the second storage medium;
(Exner, “[0040] In the context of FIG. 2, the method 300 may be performed at a current time, represented by t0, to estimate an upcoming time point of available computation capacity, represented by ti, and compute MAX for the time period Δt between t0 and ti, given the download bandwidth. It is realized that if the selected partition is determined so as to be substantially equal to or smaller than MAX, the selected partition will be available for execution at the computer 10 at the time point ti when the computer 10 has available computation capacity. Thereby, the example method 300 enables a seamless download, update and execution of the selected partition of the NN model 11 [wherein the injection interface is configured for: injecting at least one relevant part ie selected partition of the retrained machine learning model after processing from the first storage medium of the first computing unit into the second storage medium of the second computing unit by the injection interface at runtime; wherein a seamless download, update and execution is interpreted as after processing from the server and at runtime on the computer; wherein a current machine learning model is stored in the second storage medium; the NN model 11 is downloaded to the computer, thus it is stored].”)
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Exner teaches and the injection comprises an identification of the at least one relevant part of the current machine learning model and a replacement of the identified at least one relevant part of the current machine learning model by the corresponding at least one relevant part of the retrained machine learning model.
(Exner, “[0009] Some aspects of the present disclosure are based on the insight that an updated neural network (NN) model may be downloaded to a computation device in partitions [an identification of the at least one relevant part of the current machine learning model and a replacement of the identified at least one relevant part of the current machine learning model by the corresponding at least one relevant part of the retrained machine learning model; wherein identifying a part and replacing the part is interpreted to be updating and Exner teaches updating a NN model in partitions]. Further, some aspects of the present disclosure are based on the insight that the end-to-end time between start of download and inference for the updated NN model on the computation device may be reduced by clever selection of the size of the respective partition, and possibly by clever selection of the order in which the partitions are downloaded.”)
In regards to claim 2,
Exner teaches The technical injection system according to claim 1,
Exner teaches first wherein the first computing unit is hosted on or configured as an embedded device, an embedded control computer, or an edge device.
(Exner, “[0031] In some embodiments, the server 20 is an edge-computer or a cloud-computer, and the computer 10 is an edge-computer. In the above-described example of autonomous vehicles, the server 20 may be in a leading vehicle, and the computer 10 may be in one of the other vehicles.”)
In regards to claim 3,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the first storage medium is a volatile or non-volatile storage medium.
(Exner, “[0030] The system in FIG. 1 further comprises a server device (“server”) 20 which is connected, by wire or wirelessly, to a storage device 21 [first storage medium is a volatile or non-volatile storage medium] that stores at least part of a current version of the NN model 11.”)
In regards to claim 4,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the preprocessing comprises compressing, decompressing, quantizing, optimizing, deserializing, initializing and/or testing.
(Exner, “The server 20 and the computer 10 are operable to communicate over a wireless network 30 [preprocessing ie serializing/deserializing].”)
In regards to claim 5,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the first computing unit further comprising an input interface, configured for receiving a notification when the retrained machine learning model is available from a machine learning platform, other computing unit, or other technical system.
(Exner, [0034], “It is realized that by starting the download of the update for layer Lx at time point to, or earlier, it is ensured that layer Lx has been updated when ODx−1 from layer Lx−1 is available [an input interface, configured for receiving a notification when the retrained machine learning model is available from a machine learning platform, other computing unit, or other technical system; wherein the notification is the deemed availability]. This means that the layer Lx may be seamlessly updated and executed.”)
In regards to claim 6,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the input interface is further configured for receiving or downloading the retrained machine learning model from a machine learning platform, other computing unit, or other technical system, after notification.
(Exner, [0034], “It is realized that by starting the download of the update for layer Lx at time point to, or earlier, it is ensured that layer Lx has been updated when ODx−1 from layer Lx−1 is available. This means that the layer Lx may be seamlessly updated [wherein the input interface is further configured for receiving or downloading the retrained machine learning model from a machine learning platform, other computing unit, or other technical system, after notification; the update only happens after the notified availability] and executed.”)
In regards to claim 7,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the first computing unit is further configured for performing at least one test based on the retrained machine learning model before the injection is performed by the first computing unit through the injection interface of the second computing unit.
(Exner, “[0028] Embodiments relate to techniques for updating an NN model on a computation device from a server device while maintaining responsivity of the NN model during the update process. To maintain responsivity, embodiments enable the updated NN model to be executed on the computation device while it is being downloaded. This will enable the updated NN model to operate on input data that is available at start of download and to provide output data when download is completed, or even before that [wherein the first computing unit is further configured for performing at least one test based on the retrained machine learning model before the injection is performed by the first computing unit through the injection interface of the second computing unit; wherein the NN model on the computer 10 is configured to execute during, before and after the update process].”)
In regards to claim 8,
Exner teaches The technical injection system according claim 7,
Exner teaches wherein the injection is performed in the case that the test is successful.
(Exner, “[0028] Embodiments relate to techniques for updating an NN model [wherein the injection is performed in the case that the test is successful; Exner teaches a continuous updating process and thus, injection as it is always assumed that the execution is successful] on a computation device from a server device while maintaining responsivity of the NN model during the update process. To maintain responsivity, embodiments enable the updated NN model to be executed on the computation device while it is being downloaded. This will enable the updated NN model to operate on input data that is available at start of download and to provide output data when download is completed, or even before that.”)
In regards to claim 9,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the second computing unit is configured as an accelerator component, FPGA, Field Programmable Gate Array, or ASIC, Application-Specific Integrated Circuit.
(Exner, “[0067] The structures and methods disclosed herein may be implemented by hardware or a combination of software and hardware. In some embodiments, the hardware comprises one or more software-controlled computer resources. FIG. 7 schematically depicts such a computer resource 70, which comprises a processing system 71, computer memory 72, and a communication interface or circuit 73 for input and/or output of data. The communication interface 73 may be configured for wired and/or wireless communication, for example with the server 20. The processing system 71 may, for example, include one or more of a CPU (“Central Processing Unit”), a DSP (“Digital Signal Processor”), a GPU (“Graphics Processing Unit”), a microprocessor, a microcontroller, an ASIC (“Application-Specific Integrated Circuit”), a combination of discrete analog and/or digital components, or some other programmable logical device, such as an FPGA (“Field Programmable Gate Array”).”)
In regards to claim 10,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the second storage medium is a volatile or non-volatile storage medium.
(Exner, “[0040] In the context of FIG. 2, the method 300 may be performed at a current time, represented by t0, to estimate an upcoming time point of available computation capacity, represented by ti, and compute MAX for the time period Δt between t0 and ti, given the download bandwidth. It is realized that if the selected partition is determined so as to be substantially equal to or smaller than MAX, the selected partition will be available for execution at the computer 10 at the time point ti when the computer 10 [wherein the second storage medium is a volatile or non-volatile storage medium] has available computation capacity. Thereby, the example method 300 enables a seamless download, update and execution of the selected partition of the NN model 11.”)
In regards to claim 11,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the relevant part of the retrained machine learning model is at least one internal data structure, at least one layer, at least one bias, at least one weight and/or at least one parameter.
(Exner, “[0009] Some aspects of the present disclosure are based on the insight that an updated neural network (NN) model may be downloaded to a computation device in partitions [wherein the relevant part of the retrained machine learning model is at least one internal data structure; wherein an internal data structure is interpreted to be a partition]. Further, some aspects of the present disclosure are based on the insight that the end-to-end time between start of download and inference for the updated NN model on the computation device may be reduced by clever selection of the size of the respective partition, and possibly by clever selection of the order in which the partitions are downloaded.”)
(Exner, “[0035] It should be noted that the methodology shown in FIG. 2 may not only be used for updating a single layer in a NN model, but could be used for updating plural layers, or a subdivision (part) of one or more layers [at least one layer]. Such a subdivision may be spatial and may be applied to one or more layers in the height dimension (vertical in FIG. 2) and/or in the width dimension (horizontal in FIG. 2). Alternatively or additionally, the subdivision may be used for updating a channel of the NN model, or part of a channel.”)
In regards to claim 12,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the injection is synchronized with a control cycle clock.
Examiner interprets this limitation in light of the specification, “[0041] In a further aspect the injection is synchronized with a control cycle clock. Accordingly, the injection is performed before the system's control cycle clock triggers the input to the machine learning model. In other words, there is a delay, At, between the injection and the scoring of the retrained machine learning model, which ensures that the injection is not performed during scoring. The control cycle clock can trigger the injection and scoring periodically (e.g., every 10 milliseconds) or event-based (e.g., when a new control command is issued by an operator or subsystem). The advantage of the control cycle clock is that it synchronizes distinct computing devices in a way that avoids deadlocks and optimizes system performance.”)
(Exner, “[0051] As understood from FIG. 4B, repeated execution of the method 300 enables the network model 11 to be executed at the same time as it is updated, by downloading a sequence of selected partitions of the NN model 11 and scheduling the selected partitions for execution in synchronization with the available computation capacity of the computer 10 [wherein the injection is synchronized with a control cycle clock; wherein the control cycle clock can be the scheduling of the selected partitions (as an event-based clock) and thus, the injection is ‘synchronized’ with a control cycle clock. See example]. In the example of FIG. 4B, during the updating procedure, the NN model 11 is capable of processing input data (ID in FIG. 4A), which is provided to the NN model at t=5 ms, so as to provide corresponding output data (OD in FIG. 4A) at t=36 ms. Thus, the end-to-end time from start of download to inference (“total inference time”) is 36 ms, which may be compared to the approach of first downloading and updating D1-D7 (30 ms) and then performing I1-I7 (31 ms), which results in a total inference time of 61 ms.”)
In regards to claim 13,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the injection interface is configured as an Ethernet, a parallel or a serial communication interface, connected to a communication controlled of the second computing unit.
(Exner, “[0030] The system in FIG. 1 further comprises a server device (“server”) 20 which is connected, by wire or wirelessly, to a storage device 21 that stores at least part of a current version of the NN model 11. The server 20 and the computer 10 are operable to communicate over a wireless network 30. The wireless network may be a WAN, LAN or PAN or any combination thereof.”)
In regards to claim 14,
Exner teaches The technical injection system according to claim 1,
Exner teaches wherein the current machine learning model and the retrained machine learning model are semantically equivalent, configured as neural networks, even more configured as feedforward neural networks, convolutional neural networks or recurrent neural networks.
(Exner, “[0025] An NN model as used herein is not limited to any particular type of model. Non-limiting examples of NN models include Perceptron (P), Feed Forward (FF), Radial Basis Network (RBN), Deep Feed Forward (DFF), Recurrent Neural Network (RNN), Long/Short term Memory (LSTM), Gated Recurrent unit (RCU), Auto Encoder (AE), Variational AE (VAE), Denoising AE (DAE), Sparse AE (SAE), Markov Chain (MC), Hopfield Network (HN), Boltzmann Machine (BM), Restricted BM (RBM), Deep Belief Network (DBN), Deep Convolutional Network (DCN), Deconvolutional Network (DN), Deep Convolutional Inverse Graphics Network (DCIGN), Generative Adversarial Network (GAN), Liquid State Machine (LSM), Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Residual Network (DRN), Kohonen Network (KN), Support Vector Machine (SVM), and Neural Turing Machine (NTM), or any combination thereof. When one or more NN models are applied to perform a machine learning task, the resulting model is generally referred to as a Machine Learning (ML) model.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20200219009A1 Dao et al. teaches Method for securing a machine learning based decision system
US20200005191A1 Ganti et al. teaches Ranking and updating machine learning models based on data inputs at edge nodes
US20200050443A1 Edelsten et al. teaches Optimization and update system for deep learning models
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.T.T./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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