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 .
Status of the Claims
Claims 1, 5-8 have been amended. Claims 9-19 have been added. Claims 1-19 are pending.
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 1-19 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 claims 1, 7-8 comprise a very lengthy preamble. A preamble is generally not accorded any patentable weight where it merely recites the purpose of a process or the intended use of a structure, and where the body of the claim does not depend on the preamble for completeness but, instead, the process steps or structural limitations are able to stand alone. See In re Hirao, 535 F.2d 67, 190 USPQ 15 (CCPA 1976) and Kropa V. Robie, 187 F.2d 150, 152, 88 USPQ 478,481 (CCPA 1951). Thus, it is not clear if many recitations of the preamble referred to in the "body" of the claim. Appropriate correction is required.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
The claims at high level disclose mathematical relationships and abstract information processing of a neural network arrangement using generic computer components.
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-19 recite a method, medium and a system, therefore, are directed to the statutory class.
The USPTO Guidance recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). MPEP §§ 2106.04(a), (d).
Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look in Step 2B to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field; or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. MPEP § 2106.05(d).
Step 2A, Prong One: Is a Judicial Exception Recited?
First, determine whether the claims recite any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes). MPEP § 2106.04(a).
Claim 1 recites –
▪ method of a machine learning algorithm modelling a target function mapping inputs in an input domain to outputs in an output range, the machine learning algorithm including an array of processing nodes arranged in a network of layers of nodes including an input layer for receiving an input value, an output layer for providing an output value, and one or more intermediate layers between the input and output layers, each node in the processing set being outside the input layer receiving input from at least some adjacent nodes logically closer to the input layer via weighted connections between nodes, and each node being outside the output layer generating output to at least some adjacent nodes logically closer to the output layer via weighted connections between nodes (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) —Mathematical concepts, weighted sums, node activations, node connectivity, which can be performed by a human mind.)
▪ an adjustable weight for application to each input to the node, the adjustment weight being responsive to a threshold function applied to a value of the node input (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can manually determine and adjust weight by performing mathematical functions);
▪ a combination function for combining outputs of the threshold function (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can identify mathematical relationships); and
▪ a node bypass function for selectively mapping one or more of the inputs to the node to the output of the node, the method comprising iteratively training the machine learning algorithm to model the target function by adjustment, at each iteration, of at least weights of connections between at least a subset of the nodes, such that the nodes of the network are programmable during operation of the machine learning algorithm by adjustment of the threshold function and the bypass function so as to selectively emphasize subsets of nodes in the network (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can logically generate a mathematical model operating on numerical data by means of weighted connections and perform iterative logic to converge to a solution.)
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating and processing known data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Additional elements:
▪ nodes, layers, weighted connections, bypass paths (see MPEP 2106.05(d)(Il). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer and associated computer network to obtain data, use data to identify other data, and comparing data, are some of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. The method claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea of displaying, processing and storing data using some unspecified, generic computer).
▪ training the machine learning algorithm (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using machine learning model is merely invoking a computer component to apply the exception).
Therefore, these claims are directed to an abstract idea.
Step 2B: Does the Claim Provide an Inventive Concept?
No. The claims do not include additional elements that alone or in combination are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate and process known data.
Additionally, the computer components are used for performing insignificant extra-solution activity and well understood, routine, and conventional functions. For example, the claimed server and user client device merely communicates and processes known data. Activities such as these are insignificant extra-solution activity and, therefore, well understood, routine, and conventional. See MPEP 2106.05(d); see also, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (Obtaining information about transactions using the Internet to verify credit card transactions); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d at 715, 112 USPQ2d at 1754 (Consulting and updating an activity log); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display); Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016) (Recording a customer’s order); Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017) (Identifying undeliverable mail items, decoding data on those mail items, and creating output data); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price). Furthermore, limitations such as integrating account details are well-understood, routine, and conventional activity. See Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
Independent system claim 1 contains the identified abstract idea and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-6, 9-19 further describe the abstract idea. Claims at the high level recite conventional mathematical modeling techniques perfumed on generic computer components
The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible. As such, the claims 1-6 are not patent eligible.
Claim Rejections - 35 USC § 102
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 is/are rejected under 35 U.S.C. 102(a)(1)(2) as being anticipated by SON et al. (US 20180285715).
Regarding claim 1, SON teaches a computer implemented method of a machine learning algorithm modelling a target function mapping inputs in an input domain to outputs in an output range, the machine learning algorithm including an array of processing nodes arranged in a network of layers of nodes including an input layer for receiving an input value, an output layer for providing an output value, and one or more intermediate layers between the input and output layers ([0085]), each node in the processing set being outside the input layer receiving input from at least some adjacent nodes logically closer to the input layer via weighted connections between nodes, and each node being outside the output layer generating output to at least some adjacent nodes logically closer to the output layer via weighted connections between nodes ([0084] “nodes included in neighboring layers may be selectively connected according to respective connections, e.g., which may or may not be weighted”, [0091]), wherein each node includes:
an adjustable weight for application to each input to the node, the adjustment weight being responsive to a threshold function applied to a value of the node input ([0084] “connection weightings between nodes of different hidden layers may be recursively adjusted until the corresponding neural network model is trained with a desired accuracy rate or below a maximum error rate”);
a combination function for combining outputs of the threshold function ([0089] “convolutions are performed simultaneously in respective parallel layers, the results of which are ultimately combined in a subsequent same layer”, [0135]); and
a node bypass function for selectively mapping one or more of the inputs to the node to the output of the node ([0154]), the method comprising iteratively training the machine learning algorithm to model the target function by adjustment ([0089] “connection weightings being adjusted through multiple iterations, such as through backpropagation training”, [0160] “not perform all of the convolution operations, by selectively skipping some kernels … some nodes corresponding to skipped kernels as not being active or not being provided respective inputs from a previous layer, the CNN 1102 may also be configured without the example nodes corresponding to the skipped kernels. Thus, the CNN may be selectively reconfigured, or differently configured, depending on whether or which nodes corresponding to which kernel elements or kernels are skipped), at each iteration, of at least weights of connections between at least a subset of the nodes, such that the nodes of the network are programmable during operation of the machine learning algorithm by adjustment of the threshold function (see NOTE)([0084], [0089] “respective convolutions performed on the input data, a pooling or sub-sampling layer configured to perform abstraction to map a plurality of pixels or values from a previous layer to a lesser number of pixels or values, one or more further convolutional layers that respectively generate features through respective convolutions, further pooling or sub-sampling layers … features transferred from one or more previous layers”, [0105], [0129], [0165], [0168], F13) and the bypass function ([0154]-[0155]) so as to selectively emphasize subsets of nodes in the network ([0161] “remaining nodes with hatching represent nodes that are not skipped or are active/considered nodes and thereby output values that may affect the ultimate output of the CNN 1102 and are provided input from the previous layer. Alternatively, the CNN 1102 may be configured only with the determined active/considered nodes without the skipped nodes”, [0161]-[0163]).
NOTE - It is stated on the record that backpropagation iteratively updates a model's parameters to minimize error. The backpropagation primarily known for adjusting weights to map inputs to outputs, it also optimizes thresholds (biases) to improve network performance. The iteratively adjusted bias is the adjusted threshold (i.e. bias acts as a tunable threshold for a neuron's activation).
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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Andoni et al. (20190080240) in view of SON et al. (US 20180285715).
Regarding claim 1, Andoni teaches a computer implemented method of a machine learning algorithm modelling a target function mapping inputs in an input domain to outputs in an output range ([0080]-[0081]), the machine learning algorithm including an array of processing nodes arranged in a network of layers of nodes including an input layer for receiving an input value, an output layer for providing an output value, and one or more intermediate layers between the input and output layers ([0037], [0045], [0047]),
each node in the processing set being outside the input layer receiving input from at least some adjacent nodes logically closer to the input layer via weighted connections between nodes ([0027]), and
each node being outside the output layer generating output to at least some adjacent nodes logically closer to the output layer via weighted connections between nodes ([0048] “different topologies (which may include different input nodes corresponding to different input data fields if the data set includes many data fields) and different connection weights”, [0060]), wherein each node includes:
an adjustable weight for application to each input to the node ([0027]), the adjustment weight being responsive to a threshold function applied to a value of the node input ([0028]-[0029], [0036]);
a combination function for combining outputs of the threshold function ([0028], [0061]); and
the method comprising iteratively training the machine learning algorithm to model the target function by adjustment ([0038]), at each iteration, of at least weights of connections between at least a subset of the nodes ([0039]-[0040]), such that the nodes of the network are programmable during operation of the machine learning algorithm by adjustment of the threshold function
Andoni does not explicitly teach, however SON discloses a node bypass function for selectively mapping one or more of the inputs to the node to the output of the node ([0154]) and such that the nodes of the network are programmable during operation of the machine learning algorithm by adjustment of the threshold function and the bypass function so as to selectively emphasize subsets of nodes in the network ([0154]-[0155], [0161] “remaining nodes with hatching represent nodes that are not skipped or are active/considered nodes and thereby output values that may affect the ultimate output of the CNN 1102 and are provided input from the previous layer. Alternatively, the CNN 1102 may be configured only with the determined active/considered nodes without the skipped nodes”, [0161]-[0163]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Andoni to include bypass function as disclosed by SON. Doing so would improve a performance associated with a speed of processing the convolution operations (SON [0103]).
Regarding claims 2 and 9, Andoni as modified teaches the method and the system wherein the target function is defined through example by a set of inputs each associated with an output (Andoni [0109], SON [0086], [0124]-[0125]).
Regarding claims 3 and 10, Andoni as modified teaches the method and the system wherein the machine learning algorithm is iteratively trained using backpropagation (Andoni [0039]-[0040], [0110], SON [0089]).
Regarding claims 4 and 11, Andoni as modified teaches the method and the system wherein the machine learning algorithm is trained by an evolutionary algorithm whereby adjustments to the threshold functions and/or weights of connections between nodes are made by mutation and measurement of a degree of fitness of the machine learning algorithm to model the target function (Andoni [0026], [0036]-[0037], [0067]-[0068], [0072]-[0073], F1:170, SON [0154], [0159]).
Regarding claims 5 and 12, Andoni as modified teaches the method and the system wherein the threshold function of at least a subset of nodes is adjusted during training in response to a measure of a degree of fitness of the machine learning algorithm for modelling the target function (Andoni [0072]-[0073], [0078]-[0079], [0106]).
Regarding claims 6 and 13, Andoni as modified teaches the method and the system where the bypass function of at least a subset of nodes selectively maps in response to a measure of a degree of fitness of the machine learning algorithm for modelling the target function (Andoni [0026], [0030]-[0031], [0036]-[0037], SON [0154]-[0155], [0161]).
Claims 7 and 8 recite substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 14 and 17, Andoni as modified teaches the method and the system wherein the machine learning algorithm is trained by an evolutionary algorithm whereby adjustments to the threshold functions and weights of connections between nodes are made by mutation and measurement of a degree of fitness of the machine learning algorithm to model the target function (Andoni [0026], [0036]-[0037], [0067]-[0068], [0072]-[0073], F1:170, SON [0154], [0159]).
Claim(s) 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Andoni as modified in view of Ahissar et al. (US 7280989) and Frank C. Hoppensteadt “Pattern Recognition Via Synchronization in Phase-Locked Loop Neural Network”.
Regarding claims 15 and 18, Andoni as modified does not explicitly teach, however Ahissar discloses the method and the system wherein the bypass function of each node is configured to provide within each node a phase tracking so as to phase lock nodes to a current state of a connected node (C28L1-30, C14L4-7, C22L27-61, note that oscillator is a connected node, C6L51-65C17L16-37).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Andoni as modified to include a phase tracking as disclosed by Ahissar. Doing so provides an active, closed-loop decoding mechanism, which dynamically adapts its working parameters to match the incoming signal (Ahissar C2L6-63).
Still, if Andoni as modified by Ahissar does not explicitly teach, however Hoppensteadt discloses a phase tracking so as to phase lock nodes to a current state of a connected node (p.736 ¶I-III. Wherein pattern of phase relations is a state)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Andoni as modified to include a phase tracking as disclosed by Hoppensteadt. Doing so retrieves complex oscillatory patterns as synchronized states with appropriate phase relations between neuron (Hoppensteadt Abstract).
Claim(s) 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Andoni as modified in view of Ahissar et al. (US 7280989).
Regarding claims 16 and 19, Andoni as modified does not explicitly teach, however Ahissar discloses the method and the system wherein the bypass function is configured during the operation of the machine learning algorithm to lock a value of the output value to a value of one of the inputs (C5L62-65, C14L4-7, C34L45-53, C38L25-27).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Andoni as modified to lock a value of the output value to a value of one of the input as disclosed by Ahissar. Doing so provides an active, closed-loop decoding mechanism, which dynamically adapts its working parameters to match the incoming signal (Ahissar C2L6-63).
Claim(s) 6 and 13 are additionally rejected under 35 U.S.C. 103 as being unpatentable over Andoni et al. (2019/0080240) in view of Kung et al. (US 20190378017) or MATSUMOTO et al. (US 20180053085).
Regarding claims 6 and 13, Andoni as modified teaches the method and the system, as disclosed above, Kung additionally teaches where the bypass function of at least a subset of nodes selectively maps in response to a measure of a degree of fitness of the machine learning algorithm for modelling the target function ([0095], [0071], [0074]). MATSUMOTO discloses the same in [0047], [0185], [0215].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Andoni to include bypass function to a measure of a degree of fitness of the algorithm as disclosed by Kung or MATSUMOTO. Doing so provides a higher design, flexibility, accuracy, processing speed, and yield noticeable improvement in both network reduction and performance enhancement (Kung [0064]).
Response to Arguments
Applicant's arguments filed 04/17/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 112, the applicant argues “there is no US caselaw support for the Office Action's apparent allegation that a claim is indefinite based on the length of the claim's preamble.”
However, it is respectfully noted that a preamble is generally not accorded any patentable weight where it merely recites the purpose of a process or the intended use of a structure, and where the body of the claim does not depend on the preamble for completeness but, instead, the process steps or structural limitations are able to stand alone. See In re Hirao, 535 F.2d 67, 190 USPQ 15 (CCPA 1976) and Kropa v. Robie, 187 F.2d 150, 152, 88 USPQ 478,481 (CCPA 1951).
The limitations in the preamble are directed to an intended use or purpose and usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates." Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F.3d 1339,1345 (Fed. Cir. 2003). "[s]uch statements often ... appear in the claim's preamble," and provide a statement of intended use or purpose. See In re Stencel, 828 F.2d 751,754 (Fed. Cir. 1987).
Thus, given the length of the preamble, it is not clear whether the preamble have to be given the patentable weight or whether the preamble is merely an intended use or a purpose. The applicant is advised to rewrite the preamble in order for the limitations to be properly considered (i.e. – 1. “A computer implemented method comprising: a machine learning algorithm modelling a target function mapping inputs in an input domain to outputs in an output range … “) in order to overcome the rejection.
With respect to the rejection under 35 USC 101, the applicant’s arguments are not persuasive. The claims are directed to a well-known practice of applying a mathematical modelling a target function mapping inputs by a backpropagation. While the claims may represent an improvement to the abstract idea, they in no way either claimed or disclosed represent a practical application.
Under the 2019 Revised Guidance, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)-(h)). See 2019 Revised Guidance, 84 Fed. Reg. at 51-52, 55. Acclaim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See 2019 Revised Guidance, 84 Fed. Reg. at 54.
For example, limitations that are indicative of "integration into a practical application" include:
- Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
- Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
- Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP §2106.05(c); and
- Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
- Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP § 2106.05(+);
- Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
- Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
See 2019 Revised Guidance, 84 Fed. Reg. at 54-55 ("Prong Two’).
In view of the 2019 Revised Guidance, one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract element recited in the only independent claim is: machine learning, a processor and a memory (claim 7) and non-transitory computer-readable storage medium (claim 8). These generic computer hardware merely perform generic computer functions of receiving, processing and transmitting data and represent a purely conventional implementation of applicant's determining of an event timeline and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
This recited additional element is merely a generic computer component. The claims do present any other issues as set forth in the 2019 Revised Guidance regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. See Revised Guidance, 84 Fed. Reg. at 55. Rather, the claims on appeal merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea. The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
The machine learning algorithm is trained such that the nodes of the network are programmable during operation of the algorithm by adjustment of the threshold function and the bypass function so as to selectively emphasize subsets of nodes in the network. Claims 1, 7-8 are a computer-implemented method for training a machine learning model, which corresponds to a mathematical problem. The inputs and the outputs of the machine learning algorithm are not defined. As it is presently written, claims 1, 7-8 merely amount to define the implementation of a mathematical model processing abstract data. Thus, the distinguishing features are purely mathematical differences that do not achieve any technical effect serving a technical purpose.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e) (h)), the claims do not integrate the judicial exception into a practical application.
With respect to the rejection under 35 USC 102 and the reference of Son, the applicant’s arguments are not persuasive.
The applicant argues – “claim 1 define nodes of the network being programmable during operation of the algorithm by adjustment of the threshold function and the bypass function so as to selectively emphasize subsets of nodes in the network. Son fails to teach or suggest such a feature … While Son discloses adjustment of weightings between nodes, Son fails to disclose an adjustment to the threshold function as claimed.”
Such arguments are not persuasive. Son clearly teaches training of the neural network. The neural network comprise –
“weighted connections between neighboring nodes in neighboring layers of the neural network structure”, the weighted “connections within layers may be varied during training until the neural network is trained to a desired acceptability for the desired interpretation objective,” “the neural network may be trained … through a backpropagation or simulated annealing algorithms … weightings between nodes of different hidden layers may be recursively adjusted until the corresponding neural network model is trained with a desired accuracy rate or below a maximum error rate … weightings between nodes within respective layers may be adjusted in the recursive adjusting” [0084];
“connection weightings being adjusted through multiple iterations, such as through backpropagation training” [0089].
It is stated on the record that backpropagation iteratively updates a model's parameters to minimize error. The backpropagation primarily known for adjusting weights to map inputs to outputs, it also optimizes thresholds (biases) to improve network performance. Through gradient descent, both weights and bias terms are adjusted iteratively to minimize loss The iteratively adjusted bias is the adjusted threshold (i.e. bias acts as a tunable threshold for a neuron's activation).
Still, Son clearly teaches adjustment to the threshold –
“determination of whether to skip … based on whether the example weight value is less than a minimum threshold, such that if the weight value is less than the minimum threshold … updates or adjusts respective output elements … based on one or more biases … values of the one or more biases … may be stored in a memory … as parameters of the corresponding CNN” [0165], “applies biases corresponding to kernel elements” [0168] and as shown in F13 -
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In summary, training CNN through a backpropagation fully shows iteratively optimizing and adjusting thresholds (biases) through the updating output in order to achieve trained objectives. Thus, Son fully teaches adjustment to the threshold function as claimed.
◊ The applicant further argues – “Son fails to disclose an adjustment to the bypass function as claimed.”
The arguments are not persuasive. Son clearly teaches “convolution operation may include determining whether to skip at least one operation” [0022], which is analogous to a bypass function. Son further teaches –
“convolution operation, corresponding to the at least one skip target kernel, and updating respective output elements of the convolutional layer, corresponding to the skipped respective operations, based on at least one bias” [0028];
“A skip target kernel element … of which a degree of contribution to an output corresponding to a convolution operation satisfies a predefined condition … whose degree of contribution to the output is determined or predicted, e.g., currently or previously determined or predicted, to be less than a threshold” [0154], specifically –
“scheme of defining skip target kernel elements may be applied based on various references or considerations,”
“convolution operation that would have otherwise involved the skip target kernel based on a start point of a skip target kernel included or indicated in the kernel information” [0156],
“updating at least one output element based on at least one bias corresponding to the skip target kernel for which the operation was skipped” [0157].
The convolution operation which decides whether to skip (bypass) is “based on kernel information” (F9:902). The kernel information is iteratively adjusted – “acquires kernel information in operation 1202, and acquires weights in operation” –
“determines whether to skip at least one operation, e.g., a convolution operation, between the input and the weights based on the kernel information … weights may be values of kernel elements applied in an MAC operation with an input element to generate the output, for example. For example, the CNN processing apparatus may selectively zero-skip a multiplication or multiplication and accumulation operation associated with a kernel element when the kernel element is determined to have a weight value of 0, or based on the kernel information. Based on a result of determination, the CNN processing apparatus may select to skips the operation between the input and the kernel weight in operation 1205 if the weight value is determined to be zero or may perform the MAC operation involving that kernel weight in operation 1206 if the weight value is determined to not be zero” [0165]
“when a weight is determined to be skipped then the corresponding MAC operation to perform the convolution with respect to the weight and the input is not performed, while the MAC operation with respect to the weight and the input may otherwise be performed when the weight is not skipped” [0168.
Updating output elements of the convolutional layer, corresponding to the skipped respective operations, based on the various references is analogous to adjustment to the bypass function. Thus, Son fully teaches adjustment to the threshold function and bypass function as required.
With respect to the rejection under 35 USC 103 and the reference of Son, the applicant’s arguments are not persuasive. The applicant argues – “Andoni does not disclose "a node bypass function for selectively mapping one or more of the inputs to the node to the output of the node".”
The arguments are not persuasive. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck& Co., 800 F.2d 1091,231 USPQ 375 (Fed. Cir. 1986). Andoni is not relied upon to teach the limitation, as it’s fully disclosed by Son as argued above.
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.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 May 4, 2026
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