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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/19/26 has been entered.
Response to Arguments
Applicant's arguments filed 2/19/26 have been fully considered but they are not persuasive. The arguments that the claims provide a solution/improvement in computer science and a resource saving solution to detect a new disease are not persuasive. The abstract idea alone cannot provide the improvement, but additional elements must also be present in the claim that practically integrate the abstract idea/mental concept into a complete system or method.
The claims at issue are very similar to the guidance provided by the office in example 47, claim 2, where the claim was held “ineligible” under 101—and the guidance is provided below.
“[Claim 2] A method of using an artificial neural network (ANN) comprising:
(a) receiving, at a computer, continuous training data;
(b) discretizing, by the computer, the continuous training data to generate input data;
(c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm;
(d) detecting one or more anomalies in a data set using the trained ANN;
(e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and
(f) outputting the anomaly data from the trained ANN.
Claim 2 is ineligible. Claim Interpretation: Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
Steps (a) and (b) recite receiving and discretizing continuous training data to generate input data. The term “continuous data” is recognized as having its plain meaning of any data that is measured and can take on any number of possible values. The plain meaning of discrete data, as 5 supported by the third paragraph of the background, is data that can be counted, has a limited number of values, and is more suitable for use as training data.
The claim does not put any limits on how the continuous data is received, but the background supports the plain meaning of “receiving” as encompassing receiving the data remotely over a network. The claim also does not limit the plain meaning of “discretizing,” which, as explained in the background, includes any known discretization method, including binning and clustering, as well as numerical discretization, such as rounding continuous data values or performing other basic mathematical calculations that can be performed mentally (see the third paragraph of the background). Step (c) recites training an ANN using a selected algorithm. The training algorithm is a backpropagation algorithm and a gradient descent algorithm. When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient descent algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculations. The fourth paragraph of the background supports the plain meaning by stating the “gradient descent begins by initializing the values of parameters and then applying a gradient descent calculation, which uses mathematical calculations to iteratively adjust the values so they minimize a loss function.” The background also states that “backpropagation is a mathematical calculation for supervised learning of ANNs using gradient descent.”
Steps (a), (b), and (c) are all recited as being performed by a computer. The recited computer is recited at a high level of generality, i.e., as a generic computer performing generic computer functions.
Step (d) recites detecting one or more anomalies in a data set using the trained ANN. The claim does not provide any details about how the trained ANN operates or how the detection is made, and the plain meaning of “detecting” encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of an anomaly in a data set.
Step (e) recites analyzing the one or more detected anomalies using the trained artificial neural network to generate anomaly data. The step of analyzing includes both determining that an anomaly has been detected and may further include suggesting a type or cause of the anomaly. The plain meaning of “analyzing” encompasses evaluating information, which in this claim is limited to evaluating detected anomalies to generate anomaly data by the trained ANN. The claim does not limit how the analysis (evaluation) is performed, and there is nothing about a detected anomaly itself that would limit how it can be analyzed. As explained in the background, “the anomaly data may explain the type of anomaly or a cause of the anomaly.” The claim does not include any additional details that explain the analysis of detected anomalies.
Regarding step (f), the step of outputting the anomaly data merely requires a generic output using the trained ANN. The claim does not impose any limits on how the data is output or require any particular components that are used to output the anomaly data.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of claim 2 is a method that receives continuous training data at a computer, uses the computer to discretize the continuous training data to generate input data, trains the ANN using the input data 6 and a selected backpropagation algorithm and gradient descent algorithm, detects and analyzes anomalies in a data set using the trained ANN, and outputs anomaly data from the trained ANN. The claimed discretizing, detecting, and analyzing steps encompass mental choices or evaluations, and the claimed discretizing and training using a backpropagation algorithm and gradient descent algorithm encompasses performing mathematical calculations.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including receiving continuous training data. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
As discussed above, the broadest reasonable interpretation of steps (b), (d), and (e) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Specifically, step (b) recites discretizing continuous training data to generate input data by processes including rounding, binning, or clustering continuous data, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed discretizing of continuous data encompasses observing continuous data and performing an evaluation, such as rounding the continuous data. Step (d) recites detecting one or more anomalies in a data set using the trained ANN. Under its broadest reasonable interpretation when read in light of the specification, the “detecting” encompasses mental observations or evaluations that are practically performed in the human mind. For example, the claimed detecting of anomalies in a data set encompasses observing data in a data set and performing an evaluation by comparing anomalous and non-anomalous data. Step (e) recites analyzing the one or more detected anomalies using the trained ANN to generate anomaly data. Step (e) encompasses performing evaluation, judgment, and opinion to make a determination about detected anomalies. Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
As discussed above, the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner 7 should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, steps (b), (d), and (e) fall within the mental process grouping of abstract ideas, and steps (b) and (c) fall within the mathematical concepts grouping of abstract ideas. Limitations (b)-(e) are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of “(a) receiving, at a computer, continuous training data,” “using the trained ANN” in limitations (d) and (e), and “(f) outputting the anomaly data from the trained ANN.” The claim also recites that steps (b) and (c) are performed by a computer.
The limitations “(a) receiving, at a computer, continuous training data” and “(f) outputting the anomaly data from the trained ANN” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, limitations (a), (b), and (c) are recited as being performed by a computer. The computer is recited at a high level of generality. In limitation (a), the computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). In limitations (b) and (c), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations in (d) and (e) reciting “using the trained ANN” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data” is performed “using the trained ANN.” The trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions. Rather, these limitations only recite the outcome of “detecting one or more anomalies” and “analyzing the one or more detected anomalies” and do not include any details about how the “detecting” and “analyzing” are accomplished. See MPEP 2106.05(f).
The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, there are four additional elements. The additional element of “using the trained ANN” in limitations (d) and (e) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Additional elements (a) and (f) were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the recitations of “(a) receiving continuous training data” and “(g) outputting the anomaly data from the trained ANN” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 9 As discussed in Step 2A, Prong Two above, the recitation of a computer to perform limitations (a), (b), and (c) amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).”
The disclosure discusses the use of conventional/classic computer programs and systems to perform the claimed methods/limitations (e.g. paras. 95, 124, 139-144, etc.) and therefore the steps of the claims are recited as being performed by a computer. The recited computer is recited at a high level of generality, i.e., as a generic computer performing generic computer functions. The computer is used as a tool to perform the generic computer function, such as training the computer, receiving data/ECG information, and analyzing/applying the machine learning processes on the received data such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Finally, the recently decided case Recentive Analytics, Inc. v. Fox Corp has held that claims relying on machine learning elements may be considered patent ineligible under 101 where the claims are directed to no more than applying well-established machine learning techniques to a new data environment. While the applicant argues that the CNN improves computer functionality and addresses specific technical challenges in ECG analysis, the current claims are directed to no more than applying well-established machine learning techniques to a new data environment of ECG images. As seen by the cited prior art of Kim, it is well-known and conventional to use CNN with adjusted weighting of the convolution filters and configuring the kernel size to produce spatial and temporal features (e.g. paras. 17, 182-192, 206-213, 227, etc.). While Kim may not disclose all the claimed elements, those issues are directed to anticipation and obviousness, not to whether the claims are statutory under 101, and therefore the claims are still rejected under 101.
Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
Misnumbered claims 22, 22, and 23 should be renumbered by the applicant, such as canceling the second claim 22 and claim 23 and renumbering them as claims 25 and 26.
For the purposes of this action, the second claim 22 will be referred to as 22/2.
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 1-2, 4, 7-9, 11, 14, 17-20, 22, 22/2, and 23 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.
In claim 1, lines 11-15 are vague, in the passive voice, and it is unclear if the claims are positively reciting/claiming these limitations as method steps. It is suggested to use active voice, such as “acquiring…” or “generating…”. In the next to last line “multiple types of heart conditions” is vague as this phrase is also used in line 5 and it is unclear if the two items are the same or different items. It is suggested to use “the multiple…” in the next to last line
Claim 11 uses the same language and is therefore vague for the reasons given above.
In claim 7, “acquired…” is vague and in the passive voice and it is unclear if a positive method step recitation is being recited.
In claim 20, “the one or more machine learning processes” lacks antecedent basis. In line 3, “trained” is vague and in the passive voice.
In claim 22/2, “when the heart condition is critical” is vague as claim 1 does not set forth any step for determining when the heart condition is “critical”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4, 7-9, 11, 14, 17-20, 22, 22/2 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the mental concept of training machine learning processes with heart data, receiving ECG information, and applying the machine learning to the received ECG information to output a heart condition of the patient. This judicial exception is not integrated into a practical application because the combination of additional elements (e.g. neural network—i.e. general computer processor as set forth in the disclosure in paras. 95, 124, 139-144, etc.-- and image sensor for claim 7, and similar elements in a non-transitory computer readable media of claim 11) fail to integrate the judicial exception into a practical application. The generically recited CNNs, and/or computer readable medium do not add a meaningful limitation to the abstract idea because it amounts to simply implementing the abstract idea on a computer. In addition, the limitation of “receiving the multiple-lead ECG information by a computerized system”, and “image sensor” appears to be just a data gathering step to get data for the mental concept and does not add a meaningful limitation as it is merely a nominal or token extra-solution component of the claim, and is nothing more than an attempt to generally link the method/system to a particular technological environment. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered separately and in combination do not add significantly more to the exception. The additional limitations only receive and process data and these are well-understood, routine, conventional computer functions or neural networks as recognized by the court decisions listed in MPEP 2016.05. In addition, see the recent decision Recentive Analytics, Inc. v. Fox Corp.
The claims are directed to an abstract idea and/or the end result of the system/method, the essence of the whole, is a patent-ineligible concept. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to a general computer performing a calculation. The claims are directed to an abstract idea, i.e. implementing the idea of training machine learning processes with heart data, receiving ECG information, and applying the machine learning to the received ECG information to output a heart condition of the patient, such as may be done by a mental process, critical thinking, and/or paper and pencil, or done by a mathematical equation, with additional generic computer elements, or additional structure (e.g. computerized system, CNN, image sensor, and/or computer readable medium, etc.) recited at a high level of generality that perform generic functions routinely used in the art, and do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation or in the relevant art. Thus, the recited generic computer components perform no more than their basic computer functions. These additional elements are well‐understood, routine and conventional limitations (see cited document(s)) that amount to mere instructions or elements to implement the abstract idea. In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. See the recent decisions by the U.S. Supreme Court, including Alice Corp., Myriad, and Mayo. In addition, the current claims are similar to other recent court decisions dealing with analyzing, comparing, and/or displaying data, such as Electric Power Group, Digitech, Grams, and Classen.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of the claims (e.g. claim 1 having a processor/computerized system, and corresponding claim 11 directed to a computer readable medium) is a method with a system having a processor, wherein the processor is programmed with executable instructions to perform the calculations/mental process/critical thinking. The claims do not impose any limits on how the multiple-lead ECG information is received by the processor, and thus this step covers any and all possible ways in which this can be done, for instance by typing the information into the system, or by the system obtaining the information from another device. In addition, the limitation of “receiving the multiple-lead ECG information by a computerized system”, and “image sensor” appears to be just a data gathering step to get data for the mental concept and does not add a meaningful limitation as it is merely a nominal or token extra-solution component of the claim, and is nothing more than an attempt to generally link the method/system to a particular technological environment. The claim also does not impose any limits on how the computations are accomplished, and thus it can be performed in any way known to those of ordinary skill in the art.
The calculations are simple enough to be practically performed in the human mind or through critical thinking. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. Nor does the recitation of a processor in the claim negate the mental nature of this limitation because the claim here merely uses the processor as a tool to perform the otherwise mental process.
The computerized system/processor is recited so generically (no details whatsoever are provided other than that it is a general computer/processor) that it represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Although the processor or claim limitations may fall under several exceptions (e.g., a mathematical concept-type abstract idea or a mental process-type abstract idea), there are no bright lines between the types of exceptions. See, e.g., MPEP 2106.04(I). Thus, it is sufficient for the examiner to identify that the limitations align with at least one judicial exception, and to conduct further analysis based on that identification.
The limitations of the claims are carried out by the processor/computerized system, where the processor performs the necessary software tasks so that the result of the abstract mental process is just data/determining the heart condition of the patient. In addition, the limitation of “receiving the multiple-lead ECG information by a computerized system”, and “image sensor” appears to be just a data gathering step to get data for the mental concept and does not add a meaningful limitation as it is merely a nominal or token extra-solution component of the claim, and is nothing more than an attempt to generally link the method/system to a particular technological environment. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity. Even when viewed in combination, the additional elements in this claim do no more than automate the mental processes (e.g., the mental computation of training machine learning processes with heart data, receiving ECG information, and applying the machine learning to the received ECG information to output a heart condition of the patient), using the computer components as a tool. While this type of automation may improve the life of a practitioner/physician (by minimizing or eliminating the need for mentally computing metrics), there is no change to the computers and other technology that are recited in the claim as automating the abstract ideas, and thus this claim cannot improve computer functionality or other technology. See, e.g., Trading Technologies Int’l v. IBG, Inc., 921 F.3d 1084, 1093 (Fed. Cir. 2019) (using a computer to provide a trader with more information to facilitate market trades improved the business process of market trading, but not the computer) and the cases discussed in MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality). Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to George Robert Evanisko whose telephone number is (571)272-4945. The examiner can normally be reached M-F 8AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/George R Evanisko/ Primary Examiner, Art Unit 3792 3/19/26