DETAILED ACTION
Summary and Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to Applicant’s reply filed 9/15/2025.
Claims 1, 3, 4, and 6-10 are pending.
Claims 1, 3, 4, and 6-10 are rejected under 35 U.S.C. 112(b).
Claims 1, 3, 4, and 6-10 are rejected under 35 U.S.C. 101.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Objections
Claims 1 and 10 are objected to because of the following informalities:
In claims 1 and 10, in the first limitation “a plurality of cluster” should be “a plurality of clusters”
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.
Claims 1, 3, 4, and 6-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 10 recite at the end of the first limitation, “… when the calculated mean value is non-label data, determining the calculated mean value and the mean value between non-label data closest to the calculated mean value as the clustering center points.” First, it is unclear what is meant by “when the calculated mean value is non-label data” because the “calculated mean value” is calculated for the “plurality of non-label data in each cluster. In that way, the “calculated mean value” is always non-label data. Second, it is unclear what is meant by “determining the calculated mean value and the mean value between non-label data closest to the calculated mean value”. The specification at para. 0030 does not clarify the limitation. Moreover, there is a lack of antecedent basis for “the mean value” recited before “between non-label data”. Clarification is required.
The remaining claims are rejected because they depend on a rejected claim.
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, 3, 4, and 6-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106; See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Pursuant to Step 1, claims 1-9 are directed to the statutory category of a process. Claim 10 is directed to a machine.
Claim 1
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Under the 2019 PEG, claims are deemed to be directed to an abstract idea if they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Here, claim 1 are directed to an abstract idea categorized under mental processes. Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” Similarly, use of a computer as a tool to perform the concept is also considered within the category of mental processes. MPEP 2016(a)(2)(III).
Claim 1 recites limitations for a business task execution method, executed by a target user, as energy equipment, in combination with a joint learning server, comprising the steps of (1) clustering, by the joint learning server, a plurality of non-label data corresponding to a business task of the target user, so as to determine at least two clustering center points, wherein the clustering comprises clustering the plurality of non-label data by a clustering algorithm to determine a plurality of cluster, calculating, for each cluster, a mean value of the plurality of non- label data in the cluster, and when the calculated mean value is non-label data, determining the calculated mean value and the mean value between non-label data closest to the calculated mean value as the clustering center points, (2) determining, by the joint learning server, respective weights corresponding to a plurality of label data of joint users according to the at least two clustering center points and the plurality of non-label data, wherein a plurality pieces of label data correspond to the business task, (3) constructing, by the joint learning server, a joint learning model according to the plurality of label data of each joint user and the respective weights corresponding to the plurality of label data, (4) sending, by the joint learning server, the joint learning model to the target user to enable the target user without label data corresponding to the business task to locally execute the business task by using the joint learning model, the business task including fault prediction, flue gas oxygen content prediction, and remaining service life of electronic equipment, (5) carrying out the fault prediction, the flue gas oxygen content prediction, and the remaining service life prediction of electronic equipment while protecting the label data of each joint user as private data, (6) wherein the step of determining respective weights corresponding to the plurality of label data of the joint users according to the at least two clustering center points and the plurality of non-label data comprises: determining a target similarity between each of the at least two clustering center points and the plurality of non-label data according to the at least two clustering center points and the plurality of non-label data, (7) determining respective similarity weights corresponding to the at least two clustering center points according to the at least two clustering center points, the target similarity between each of the at least two clustering center points and the plurality of non-label data and the plurality of label data of the joint users (8) determining the respective weights corresponding to the plurality of label data of the joint users according to the respective similarity weights corresponding to the at least two clustering center points, (9) wherein the step of determining respective similarity weights corresponding to the at least two clustering center points according to the at least two clustering center points, the target similarity between each of the at least two clustering center points and the plurality of non-label data and the plurality of label data of the joint users comprises: determining a reference correlation between the any two clustering center points according to the any two clustering center points and the plurality of label data of the joint users (10) determining a target correlation between the any two clustering center points according to an initial correlation and the reference correlation between the any two clustering center points, (11) determining the respective similarity weights corresponding to the at least two clustering center points according to the target correlation between the any two clustering center points and the target similarity between each of the at least two clustering center points and the plurality of non-label data, (12) wherein the step of determining the respective similarity weights corresponding to the at least two clustering center points according to the target correlation between the any two clustering center points and the target similarity between each of the at least two clustering center points and the plurality of non-label data comprises: determining a target correlation matrix corresponding to the at least two clustering center points according to the target correlation between the any two clustering center points, (13) determining a target similarity vector according to the target similarity between each of the at least two clustering center points and the plurality of non-label data, (14) correcting the target correlation matrix according to a regularization parameter and an identity matrix to determine a correction correlation matrix; and (15) determining a similarity weight vector according to the correction correlation matrix and the target similarity vector, wherein the similarity weight vector comprises respective similarity weights corresponding to the at least two clustering center points.
Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” The mental process grouping covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. MPEP 2016(a)(2)(III). The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010). Limitations can also be deemed insignificant extra-solution activity (IESA). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g).
Claims are given their broadest reasonable interpretation in light of the specification. MPEP 2111.
Limitation (1) is directed to a step of clustering and determining, which are mental process steps. The limitation does not provide specific steps of clustering that would make it impractical or impossible for a person to perform. The limitation further recites calculating a mean value, which is a mathematical calculation, which can be performed by a person with the aid of pen and paper or with the aid of a computer as a tool. Limitation (2) is a step of determining respective weights, which is a mathematical calculation or mental process step, given its broadest reasonable interpretation. Limitation (3) is directed to constructing a joint learning model according to data and weights, which is a mental process step. Limitation (4) is directed to sending the joint learning model to the target user to enable business task execution, which is IESA because it is mere data output required by the recited abstract idea. Limitation (5) is directed to IESA because it essentially execution of the task, much like limitation (4). Limitations (6) through (8) are directed to determining steps, which are mathematical calculations. Limitations (9) to (11) are similarly directed to determining steps, which are mathematical calculations. Limitations (12), (13), and (15) are directed to determining steps, which are mathematical calculations. Limitation (14) is directed to correcting a target correlation matrix, which is a mental step that can be performed by a person.
The recited “joint learning server” and “energy equipment” are recited at a high level of generality, i.e., as a generic components performing generic computer functions.
With regards to limitations treated as being directed to mental process steps and mathematical calculations, the Supreme Court has treated claims that include multiple exceptions in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). As there are no bright lines between the types of judicial exceptions, and many of the concepts identified by the courts as exceptions can fall under several exceptions, MPEP 2106.04, subsection I instructs examiners to “identify . . . the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) [that] aligns with at least one judicial exception.”
For at least these reasons, claim 1 is directed to an abstract idea directed to mental process steps.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the 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). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
In this case, as explained above, claim 1 merely recites an abstract idea categorized under mental processes. As discussed above, the limitations are mental process steps of clustering, determining, constructing, and correcting, but are not recited with sufficient specificity to convey an asserted improvement. Much of the determining steps involve mathematical calculations with reference to observed data. Lastly, limitations (4) and (5) are directed to sending the joint learning model to the target user to enable the target user to perform tasks, which is IESA because it is directed to mere data output required by the abstract idea. The additional limitations limiting the business tasks merely confines the use of the abstract idea to a particular technological environment and thus fails to integrate the abstract idea into a practical application/add an inventive concept to the claims. See MPEP 2106.04(d)/MPEP 2106.5(h) While claim 1 recite additional components in the form of “joint learning server” and “energy equipment”, these components are recited at a high level of generality, which do not add meaningful limits on the recited abstract idea to integrate it into a practical application by providing an improvement to the functioning of a computer or technology, implementing the abstract idea with a particular machine or manufacture that is integral to the claim, effecting a transformation or reduction of a particular article to a different state or thing, nor applying the abstract idea in some meaningful way beyond linking its use to computer technology. See MPEP 2106.04(d).
For at least these reasons, claim 1 does not integrate the judicial exception into a practical application.
Pursuant to Step 2B, claims are analyzed to determine 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. In this case, claim 1 does not recite limitations that amount to significantly more than the abstract idea. 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 above, limitations (4) and (5) are directed to IESA of receiving or transmitting data over a network, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II. As recited, the limitations are steps involving mental processes that can be practically performed by a human with the aid of a computer as a tool. As explained above, a person can use a computer to aid in clustering data, performing the mathematical calculations in the clustering step and for all the determining steps, constructing the learning model, and sending the learning model to a user. As recited, these limitations do not recite sufficient limitations to adequately convey or demonstrate the asserted improvement to improve the functioning of a computer, improve the technology, apply the abstract idea to a particular machine, effect a transformation, nor provide meaningful limitations beyond linking the abstract idea to computer technology. 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. For at least these reasons, claim 1 is nonstatutory because they are directed to a judicial exception without significantly more.
Claims 3, 4, and 6-9
Pursuant to step 2A, part 1, claims 3, 4, and 6-9 depends on claim 1 and therefore recite the same abstract idea. Pursuant to step 2A, part 2, each of claims 3, 4, and 6-8 recite limitations directed to performing observations and calculations. For much of the same reasons as explained with regards to claim 1 above, these limitations merely recite steps of observation and mathematical calculation, which can be performed by a person with the aid of a computer as a tool, for example, to perform the various calculations. For example, the similarity calculations, correlations, and distributions are not recited with sufficient specificity to adequately convey the asserted improvement. Therefore, these additional limitations do not integrate the abstract idea into a practical application. Pursuant to step 2B, the additional limitations do not amount to significantly more than the abstract idea because the limitations are not recited in a manner that provides improvements to the functioning of a computer or any other technology or technical field. Claim 9 merely recites that a cluster center point is different from any of the non-labeled data, which does not put meaningful limits on the abstract idea and also does not add significantly more than the abstract idea. For at least these reasons, claims 3, 4, and 6-9 are directed to an abstract idea without significantly more.
Claim 10 recites essentially the same subject matter as claim 1 in the form of a device. The modules are recited at a high level of generality and do not provide meaningful limits on the abstract idea. The additional component of a “processor” is also recited at a high level of generality and does not provide meaningful limits on the abstract idea. Therefore, it is rejected for the same reasons.
Claims 1, 3, 4, and 6-10 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory) above are further rejected as set forth below in anticipation of applicant amending these claims to overcome the rejection.
Response to Amendment
Objection to claim 6 for Minor Informalities
Applicant’s amendment to claim 6 to address the minor informalities is acknowledged. Consequently, the objection to claim 6 is withdrawn.
Rejection of Claim 6 under 35 U.S.C 112(b)
Applicant’s amendment to claim 6 is acknowledged. However, Applicant’s amendments raise new issues as set forth in the new grounds of rejection of all claims above. Consequently, the rejection to claim 6 under 35 U.S.C. 112(b) is maintained because it depends on a rejected claim.
Response to Arguments
Rejection of Claims 1, 3, 4, and 6-10 under 35 U.S.C 101
Applicant’s arguments in regards to the rejection of claims 1, 3, 4, and 6-10 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
In regards to step 2A-1, Applicant argues the claims recite specific steps of clustering or determining center points, which make the steps impractical for a person to do with the aid of pen and paper or a computer as a tool. Remarks at 11. Examiner respectfully disagrees. Contrary to Applicant’s argument, the claim does not recite specific steps of clustering other than “by a clustering algorithm”. As described in Applicant’s specification, a “clustering algorithm” can be any of a plurality of known methods of clustering, such as k-means clusters, hierarchical clustering, or density clustering. Spec at para. 0030. The remainder of the limitation referenced by Applicant discusses finding center points of each cluster by calculating a mean but does not specify if the mean calculation is any specific calculation to the invention. Based on para. 0030 of the specification, the mean seems to be a conventional “average” calculation, which contrary to Applicant’s argument, can be calculated by a person with the aid of pen and paper or with the aid of a computer as a tool. Accordingly, contrary to Applicant’s arguments, the limitations recited in the claims are directed to an abstract idea categorized under mental processes.
In regards to step 2A-2, Applicant argues the limitation of "carrying out the fault prediction, the flue gas oxygen content prediction, and the remaining service life prediction of electronic equipment while protecting the label data of each joint user as private data" integrates the claim into a practical application. Remarks at 11. Applicant argues the limitation shows claim one improves the technology of energy equipment executing failure prediction, prediction of remaining service life of the equipment, variable prediction and so-on, while protecting the label data of each joint user as private data. Remarks at 11. Examiner respectfully disagrees.
Claim limitations are given their broadest reasonable interpretation in light of the specification. However, limitations form the specification are not read into the claims. MPEP 2111.
While Applicant points to components recited in the claims of “energy equipment” and a “joint learning server”, these components are recited at a high level of generality. These limitations amount to simply tying the limitations to a field of technology (i.e., energy equipment) and intended uses (i.e., fault prediction, flue gas oxygen content prediction, etc. ). However, the limitations merely requires sending the joint learning model to the target user to enable the target user to locally execute the business task. Even though the limitation recites the business task, there are no requirements or limitations as to how the business task is executed that adequately demonstrate “protecting the label data of each joint user as private data.” Simply reciting that the “business task” is executed by “using the joint learning model” does not sufficiently provide meaningful limits on the abstract idea.
Applicant also cites to the background of the specification, which discusses how fine tuning a global model needs to use label data that can be difficult to obtain. However, the recited steps, as discussed in the rejection above, are steps of insignificant extra solution activity, mathematical calculations, and mental process steps. The limitations do not recite sufficient specificity to convey the asserted improvement of “ensuring the user’s private data” as asserted by Applicant. For example, there are no limitations with regards to how all the determining steps (both mental and mathematical calculations) are used to construct a joint learning model that is sent to the joint learning server and how the model is utilized to perform a business task. For at least these reasons, the limitations do not sufficiently recite limitations that integrate the abstract idea into a practical application.
In regards to Step 2B, Applicant refers to the arguments presented with regards to step 2A-2, which are addressed above.
Applicant does not provide arguments with regards to the remaining claims and only refers to those presented in regards to claim 1, which are addressed above. For at least the reasons discussed above, the claims 1, 3, 4, and 6-10 are directed to an abstract idea without significantly more. Consequently, the rejection to claims 1, 3, 4, and 6-10 under 35 U.S.C. 101 is maintained.
Additional Prior Art
Additional relevant prior art are listed on the attached PTO-892 form. Some examples are:
Zhu et al. (US Patent Pub 2022/0114475) discloses a system and method for decentralized learning for training machine learning models.
Biswas et al. (“Privacy Preserving Approximate K-means Clustering) discloses a system and method for privacy conserving computation in a cloud computing environment.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Michael Le whose telephone number is 571-272-7970 and fax number is 571-273-7970. The examiner can normally be reached Mon-Fri 9:30 AM – 6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL LE/Examiner, Art Unit 2163
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163