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 Application
This is a Final-Action in response to the claims amended on 11/17/2025.
Claims 1-4, 6-7 are amended.
Claims 1-7 are examined herein.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-6 is/are rejected under 35 U.S.C. 101 the claimed invention is directed to non-statutory subject matter.
Regarding claim(s) 1-6, the Applicant recites a repair recommendation system. However, the Applicant has failed to properly define the physical components (i.e. processor, hardware computer elements etc.) of such a system in the claim recitation. Accordingly, the Examiner is interpreting such recitations as computer code, per se. Indeed, computer code, per se, is not eligible for patent protection. If Applicant were to amended the claim 1 to positively recite a physical component, such as a device or computer processor that is performing the functions, the 101 rejection would be withdrawn.
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 1-7 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method and system are directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Mental Process” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106 since the claims set forth steps that concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Claims 1 and 7 recites the abstract idea of providing a repair recommendation (see [001], [007]. This idea is described by the following claim steps:
provide, recommendation information including a plurality of the recommended handling contents corresponding to the received failure information and a respective confidence level indicating a degree of confidence corresponding to each recommended handling content of the plurality of recommended handling contents included in the recommendation information; and
classifying the plurality of recommended handling contents based on the respective confidence level associated with a corresponding the recommended handling content into any one of a plurality of determination groups provided according to a degree of the confidence level,
specifying a case resolution rate associated in advance with each determination group of the plurality of determination groups in which the recommended handling content is classified, wherein the case resolution rate is indicated by a number of past handling contents that are correct for addressing a corresponding device failure and that are included among a top N of the past handling contents for the corresponding device failure, and
outputting, as validity information about the recommendation information, a message based on a magnitude of the specified case resolution rate to provide an indication as to a validity of each of the top N recommended handling contents and the respective confidence level for each of the recommended handling contents.
This idea falls within the Mental Processes grouping of abstract ideas because it is directed towards observation of data, evaluation of data and conclude with an opinion such that as required when providing a recommendation for a repair.
Because the above-noted limitations recite steps falling within the Certain Methods Of Organizing Human Activity abstract idea groupings of the MPEP 2106, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the Mental Processes abstract idea groupings described in the MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Claim 12 and 19 recites similar limitations as claim 1 and is therefore determined to recite the same abstract idea.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements that fail to integrate the abstract idea into a practical application are:
a recommendation calculation unit;
and input and output processing unit; and
using a learning model that receives failure information about a device in which a failure occurs and outputs a recommended handling content recommended for handling the failure.
However, using a computer environment such as recommendation calculation unit, a learning model and other recited computer elements amounts to no more than generally linking the use of the abstract idea to a particular technological environment. Providing a repair recommendation can reasonably be performed by pencil and paper until limited to a computerized environment by requiring using a learning model to infer data.
These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and alternatively serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h).
Regarding “using a learning model that receives failure information about a device in which a failure occurs and outputs a handling content recommended for handling the failure”, the examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the claims as a whole merely describes a method, computer system, and computer program product that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
For the reasons identified with respect to Step 2A, prong 2, claims 1, and 7 fail to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a mental processes) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). In addition, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-6 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above.
Dependent claims 2, 6 further limits and narrows the abstract idea by introducing limitations directed to further details related to the abstract process of providing a repair recommendation. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claims 3-5 further limits the abstract idea by embellishing the abstract idea by introducing limitations directed to Mathematical Concepts such as mathematical relationships and calculations. Further embellishing the invention with additional abstract process does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide high level of generality computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information see MPEP 2106.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1. Claim(s) 1-7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Izuoka (US Patent Publication 2003/0088583).
Regarding claims 1 and 7, Izuoka discloses a repair recommendation system and method (par. [0007] "a troubleshooting support system that has a function of retrieving an optimal remedy based on words in a user's speech") comprising:
a recommendation calculation unit configured to provide, using a learning model that receives failure information about a device in which a failure occurs (pars. [0022]-[0024], in particular par. [0024] "the entry screen includes a field 119 for entering a symptom of the car in a free word") and outputs a recommended handling content recommended for handling the failure (par. [0028] ''The database searching unit 19 searches a troubleshooting (remedy) database 29 using the words as keys. The troubleshooting database 29 stores remedies relating to vehicle types, symptoms, failure situations or the like" and par. [0019] "transmits the document to the personal computer 10 via the Internet"), recommendation information including a plurality of the recommended handling contents corresponding to the received failure information and a respective confidence level indicating a degree of confidence corresponding to of recommended handling content of the plurality of recommended handling contents included in the recommendation information (par. [0030] "The retrieval results of the database-retrieving unit 19 are passed to a weighted calculation unit 21. For each of the retrieved remedies, the weighted calculation unit multiplies the number of hit keywords by weights predetermined for the respective keywords to calculate a score for each of the remedies" par. [0041] "The summarizing and displaying unit 23 transmits the top five remedies with their respective scores to the personal computer 10 in the form of an HTML document. Thus, the staff of the service shop can view a plurality of optimal remedies for the question and the respective scores on the computer." ); and
an input and output processing unit configured to:
classify the plurality of recommended handling contents based on the respective confidence level associated with a corresponding recommended handling content into any one of a plurality of determination groups provided according to a degree of the confidence level ([0041] "The summarizing and displaying unit 23 transmits the top five remedies with their respective scores to the personal computer 10 in the form of an HTML document. Thus, the staff of the service shop can view a plurality of optimal remedies for the question and the respective scores on the computer." The Examiner interprets the ranking of the remedies and their respective scores as a classification of the remedies.),
specify a case resolution rate associated in advance with each determination group of the plurality of recommendation groups in which the recommended handling content is classified, wherein the case resolution rate is indicated by a number of past handling contents that are correct for addressing a corresponding device failure and that are included among a top N of the past handling contents for the corresponding device failure ([0037] In this example, the effectiveness and retrieval frequency, which indicate effectiveness of the information, are assigned weights of 1.0 and 0.5, respectively. The information effectiveness is set reflecting a history of the remedy. These values are different from the weights assigned to the keywords in that they are assigned to each of the remedies. If the remedy is effective for a failure, the effectiveness is upgraded in response to feedback from the service shop. The retrieval frequency indicates the frequency of retrieval of the remedy and is significant in an embodiment in which hierarchical database search is performed. Each remedy includes these items, and thus, they are simply shown in terms of scores in the drawing. ), and
output, as validity information about the recommendation information, a message based on a magnitude of the specified case resolution rate to provide an indication as to validity of each of the top N recommended handling contents and the respective confidence level for each of the recommended handling contents (par. [0037] In this example, the effectiveness and retrieval frequency, which indicate effectiveness of the information, are assigned weights of 1.0 and 0.5, respectively. The information effectiveness is set reflecting a history of the remedy. These values are different from the weights assigned to the keywords in that they are assigned to each of the remedies. If the remedy is effective for a failure, the effectiveness is upgraded in response to feedback from the service shop. The retrieval frequency indicates the frequency of retrieval of the remedy and is significant in an embodiment in which hierarchical database search is performed. Each remedy includes these items, and thus, they are simply shown in terms of scores in the drawing. [0038] The weighted calculation unit 21 calculates a score for each keyword by multiplying the number of hits for the keyword by the weight assigned to the keyword. In the embodiment shown in FIG. 6, with respect to the keywords derived from the free word and the keywords obtained by inquiry, individual keywords are assigned the same weight, respectively. For the keyword derived from the free word, the number of hits is 2 and its weight is 0.9, and therefore, the score is 1.8. For the keyword obtained by inquiry, the number of hits is 3 and the weight is 0.7, and thus, the score is 2.1. [0039] For the keywords based on the synonym, different kinds of keywords are assigned different weights. The keyword relating to the time when the failure occurs is assigned a weight of 0.3, the keyword relating to the failed part is assigned a weight of 0.4, and the keyword relating to the abnormal sound is assigned a weight of 0.8. [0040] These weights may be set separately for each remedy or uniformly for all the remedies. To enhance effectiveness, each keyword is preferably assigned a weight adapted for the remedy. par. [0041] "The summarizing and displaying unit 23 transmits the top five remedies with their respective scores to the personal computer 10 in the form of an HTML document. Thus, the staff of the service shop can view a plurality of optimal remedies for the question and the respective scores on the computer." par. [0042] "If a remedy is effective for a failure, the service staff at the service shop connects the personal computer to the server 20, activates the support system, invokes the feedback screen on the browser, and makes an entry that the remedy 3, for example, was effective. In response to this, the server 20 updates the effectiveness of the remedy 3 from 1. 0, the current value, to 1. 1", see also Figure 6.).
Regarding claim 2, Izuoka discloses the repair recommendation system according to claim 1, wherein the recommended handling contents contained in the recommendation information are each assigned a rank in order from a highest degree to a lowest degree of the associated confidence level, (pars.[0037]-[0039]; [0037] In this example, the effectiveness and retrieval frequency, which indicate effectiveness of the information, are assigned weights of 1.0 and 0.5, respectively. The information effectiveness is set reflecting a history of the remedy. These values are different from the weights assigned to the keywords in that they are assigned to each of the remedies. If the remedy is effective for a failure, the effectiveness is upgraded in response to feedback from the service shop. The retrieval frequency indicates the frequency of retrieval of the remedy and is significant in an embodiment in which hierarchical database search is performed. Each remedy includes these items, and thus, they are simply shown in terms of scores in the drawing. [0038] The weighted calculation unit 21 calculates a score for each keyword by multiplying the number of hits for the keyword by the weight assigned to the keyword. In the embodiment shown in FIG. 6, with respect to the keywords derived from the free word and the keywords obtained by inquiry, individual keywords are assigned the same weight, respectively. For the keyword derived from the free word, the number of hits is 2 and its weight is 0.9, and therefore, the score is 1.8. For the keyword obtained by inquiry, the number of hits is 3 and the weight is 0.7, and thus, the score is 2.1. [0039] For the keywords based on the synonym, different kinds of keywords are assigned different weights. The keyword relating to the time when the failure occurs is assigned a weight of 0.3, the keyword relating to the failed part is assigned a weight of 0.4, and the keyword relating to the abnormal sound is assigned a weight of 0.8. See also Figures 6,7; par. [0041] [0041] In the example shown in FIG. 6, the total score of the remedy 3 (replacement of fuel pump) is 9.5. FIG. 7 shows a hit result and the score of a remedy number 15 (checking voltage of electrical system) for the same question. The summarizing and displaying unit 23 transmits the top five remedies with their respective scores to the personal computer 10 in the form of an HTML document. Thus, the staff of the service shop can view a plurality of optimal remedies for the question and the respective scores on the computer. Instead of the scores, the summarizing and displaying unit may transmit priorities of the remedies, indicated by symbols A, B and C or the like, to the personal computer. ).
Regarding claim 3, Izuoka discloses the repair recommendation system according to claim 2, wherein the determination groups are individual groups obtained by dividing a determination coefficient into a predetermined number within a predetermined range, the determination coefficient being obtained by accumulating the confidence level associated with the recommended handling content in descending order of the rank ([0030] The retrieval results of the database-retrieving unit 19 are passed to a weighted calculation unit 21. For each of the retrieved remedies, the weighted calculation unit multiplies the number of hit keywords by weights predetermined for the respective keywords to calculate a score for each of the remedies, and passes top several remedies to a summarizing and displaying unit 23. [0038] The weighted calculation unit 21 calculates a score for each keyword by multiplying the number of hits for the keyword by the weight assigned to the keyword. In the embodiment shown in FIG. 6, with respect to the keywords derived from the free word and the keywords obtained by inquiry, individual keywords are assigned the same weight, respectively. For the keyword derived from the free word, the number of hits is 2 and its weight is 0.9, and therefore, the score is 1.8. For the keyword obtained by inquiry, the number of hits is 3 and the weight is 0.7, and thus, the score is 2.1. [0039] For the keywords based on the synonym, different kinds of keywords are assigned different weights. The keyword relating to the time when the failure occurs is assigned a weight of 0.3, the keyword relating to the failed part is assigned a weight of 0.4, and the keyword relating to the abnormal sound is assigned a weight of 0.8. See also Figs. 6 and 7).
Regarding claim 4, Izuoka discloses the repair recommendation system according to claim 3, wherein when the case resolution rate specified based on the confidence level corresponding to the recommended handling content is equal to or less than a predetermined threshold value, the input and output processing unit outputs the recommendation information and a message that prompts input of information necessary and sufficient for inference of the recommended handling content ([0042] In general, the service staff starts from the remedy of the highest score. If a remedy is effective for a failure, the service staff at the service shop connects the personal computer to the server 20, activates the support system, invokes the feedback screen on the browser, and makes an entry that the remedy 3, for example, was effective. In response to this, the server 20 updates the effectiveness of the remedy 3 from 1.0, the current value, to 1.1. ).
Regarding claim 5, Izuoka discloses the repair recommendation system according to claim 4, wherein when the case resolution rate associated with the determination group does not reach a predetermined target value, the input and output processing unit adjusts the case resolution rate to match the target value by changing a range of the determination coefficient defining the determination groups and/or the number of divisions of the determination groups ([0041] In the example shown in FIG. 6, the total score of the remedy 3 (replacement of fuel pump) is 9.5. FIG. 7 shows a hit result and the score of a remedy number 15 (checking voltage of electrical system) for the same question. The summarizing and displaying unit 23 transmits the top five remedies with their respective scores to the personal computer 10 in the form of an HTML document. Thus, the staff of the service shop can view a plurality of optimal remedies for the question and the respective scores on the computer. Instead of the scores, the summarizing and displaying unit may transmit priorities of the remedies, indicated by symbols A, B and C or the like, to the personal computer. [0042] In general, the service staff starts from the remedy of the highest score. If a remedy is effective for a failure, the service staff at the service shop connects the personal computer to the server 20, activates the support system, invokes the feedback screen on the browser, and makes an entry that the remedy 3, for example, was effective. In response to this, the server 20 updates the effectiveness of the remedy 3 from 1.0, the current value, to 1.1.).
Regarding claim 6, Izuoka discloses the repair recommendation system according to claim 1, wherein the failure information includes a natural language described in a free format, and at least one of the recommended handling contents included in the recommendation information is a replacement component candidate to be used for repairing the device in which the failure occurs (pars. [0024]-[0027]; [0024] In one embodiment of the invention, the entry screen includes a field 119 for entering a symptom of the car in a free word. The free word field is intended for the symptom of the car that the user has told. The field enables a delicate nuance of the failure or trouble to be included in the query to the support system. [0025] When the service staff finishes entry of these items and clicks a transmission button on the screen, the entry data is transmitted to the server 20. The server 20 has a support program installed therein that provides a remedy for a failure in response to a query. The block diagram of the server 20 shown in FIG. 1 illustrates functional blocks of such a support program. [0026] A question data receiving unit 11 receives the data transmitted from the personal computer 10, passes data in the free word field to a free word fragmenting unit 13, passes data in the failure situation (inquiry) field 117 to a synonym retrieving unit 15, and passes data in the other fields to a keyword compiling unit 17. The free word-fragmenting unit 13 has a document analysis function of fragmenting a free word into separate words. The document analysis function may be the one used in a translation program from Japanese to English, for example. [0027] The words derived from the free keyword and the words in the failure situation field 117 are passed to the synonym-retrieving unit 15. The synonym-retrieving unit 15 searches a synonym database 27 using the words as keys. The synonym database 27 stores synonyms and quasi-synonyms of the respective words. The synonym-retrieving unit 15 passes, to the keyword-compiling unit 17, the data in the failure situation field 117 and words resulting from fragmentation in the free word-fragmenting unit together with the synonyms and quasi-synonyms thus obtained. The keyword-compiling unit 17 compiles the bibliographic data of the car received from the question data-receiving unit and the words received from the synonym-retrieving unit 15 and passes them to a database-searching unit 19; par. [0036] In the rightmost column in FIG. 5, hit results of keywords for a remedy 3 (replacement of fuel pumps) are shown. The keywords are weighted according to the significance. FIG. 6 shows an example of the weights. In the basic information, a keyword relating to the model is assigned a weight of 0.9, a keyword relating to the failed part is assigned a weight of 0.5, and a keyword relating to the rough classification is assigned a weight of 0.5. A keyword derived from the free word is assigned a weight of 0.9, a keyword based on inquiry is assigned a weight of 0.7. As for a keyword from the synonym, a keyword relating to the time when the failure occurs is assigned a weight of 0.3, a keyword relating to the failed part is assigned a weight of 0.4, and a keyword relating to the abnormal sound is assigned a weight of 0.8. ).
Response to Arguments
Applicant's arguments filed 11/17/2025 have been fully considered but they are not persuasive.
35 USC 101
“Applicant’s claims are directed to a technological improvement in machine learning” Examiner respectfully disagrees. The Applicant appears to argue a business solution to improve the a determination of a recommended handling contents, however no improvement in machine learning is observed. It is noted that the arguments are directed to the mental or manual steps which are part of the abstract idea. It is acknowledge that a person, wither mentally or perhaps with the aid of pen and paper, could receive failure information about a device and determine a plurality of recommended handling contents for handling the failure and also determine a respective confidence level for each of the recommended handling content. A person could also classify the plurality of recommended handling contents based on the respective confidence level associated with each corresponding recommended handling content into any of the plurality of determination groups provided according to a degree of the confidence level as argued. It is further acknowledged that a person is able to specify a case resolution rate associated in advance with each determination group of the plurality of determination groups in which the recommended handling content is classified and output, as validity information about the recommendation information, a message based on a magnitude of the specified case resolution rate to provide an indication as to a validity of each of the top N recommended handling contents and the respective confidence levels. All this steps of the process could be performed mentally, perhaps with the aid of pen and paper until limited by a specific environment when requiring the use of a machine learning model to perform the steps. The system is merely facilitating the process by requiring the use of a machine learning model to perform the steps of the abstract idea.
“the human mind is not capable of processing the large amounts of training data and case resolution data for accurately determining the validity confidence level” Examiner respectfully disagrees. First, it is noted that the applicant has not determined the amount of data used within the method claimed and furthermore even if the process is a cumbersome process it does not make the process any less abstract. In the instant case, the technology is been used as a tool to facilitate a cumbersome process.
“Applicant’s claims integrate the judicial exception into a practical application… ” Examiner respectfully disagrees. Generally linking the use of the judicial exception to particular technological field by requiring the use of machine learning is not indicative of integration into a practical application. See MPEP 2106.05(h).
“Similar to the concepts discussed in BASCOM, Applicant's claims include elements that are non-conventional and sufficient to ensure that the claims amount to significantly more than an abstract idea…The recited elements are significant, at least because the claim recites an inventive way of determining the validity of the top N outputs and associated confidence levels of a learning model” Examiner respectfully disagrees. The claimed elements either individually or as an ordered combination, recite no more than routine steps of data collection and analysis, wherein the machine learning is merely used to provide a recommendation based on failure information about a deice. The system is using generic computer components and conventional computer data processing activities, this is regardless of the type of information used and being analyzed. In BASCOM the inventive concept is found in the ordered combination of the limitations, that is the inventive concept described and claimed is the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user. District Court Order, 107 F. Supp. 3d at 652-53. This design gives the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on the ISP server. BASCOM explains that the inventive concept rests on taking advantage of the ability of at least some ISPs to identify individual accounts that communicate with the ISP server, and to associate a request for Internet content with a specific individual account. That is, is the ordered combination of the computing elements that amounts to significantly more than the abstract idea not the information used in the filtering scheme and elements. Bascom addressed technological problems using a technical solution. It is noted that applicant is arguing an improvement in the field of repair and maintenance services by determining the validity of the recommendation information provided by the machine learning, that is the system is merely evaluation the recommendation provided.
“Applicant’s claim 1 includes an inventive concept. Particularly, the claimed invention addresses a challenge in a machine learning in which users may rely to their detriment on an indicated confidence level for a learning model output as discussed above, when the confidence level may actually be misleading” Examiner respectfully disagrees. The Applicant is not improving the machine learning, applicant is merely evaluating the output of the machine learning (i.e. recommendation), which is a step that could be performed manually perhaps with the aid of pen and paper and which is part of the abstract idea. No improvement in technology is obtained but rather an evaluation in the use of a machine learning which does not provide any improvement to the technology.
“Instead, the claimed solution herein is necessarily rooted in computer technology because it represents an ordered combination of steps for enabling a determination of the validity of the output of a learning model.” Examiner respectfully disagrees. The invention is not rooted in computer technology but rather is using the technology (i.e. machine learning) as a tool to perform the abstract process of determining a recommendation and a confidence level of the recommendation, which is further evaluated to determine the validity of such recommendation. This is a process that can be performed completely by a person using pen and paper until limited to the use of a machine learning to provide the recommendation based on the failure recommendation received.
35 USC 102
“Izuoka does not teach or suggest using a learning model to provide top N recommendations and a respective confidence level associated with each recommendation, and further providing an indication of the validity of each recommendation and the respective confidence level.” Examiner respectfully disagrees. Izuoka, as presented above, discloses providing a set of recommendations and their respective confidence level ranked (i.e. classified) which are further analyzed taking the output of the users in order to determine an effectiveness of the recommendations by providing an effectiveness score as disclosed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1. Singh, US Patent Publication 2021/0264438 GUIDED PROBLEM RESOLUTION USING MACHINE LEARNING. This invention relates generally to resolution of customer problems and, more particularly to the identification of a course of action to resolve a problem through the use of machine learning techniques.
THIS ACTION IS MADE FINAL. 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 MARIA C SANTOS-DIAZ whose telephone number is (571)272-6532. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA C SANTOS-DIAZ/ Primary Examiner, Art Unit 3629