Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
All claims are directed towards either a method, an apparatus or a non-transitory computer-readable recording medium and thus satisfies Step 1 as falling into one of the statutory categories.
Step 2A, Prong One:
Independent Claim 1 recites (the same analysis applies to similar independent Claims 10 and 11):
predict a use situation of a target device of a same type as the device installed in a facility based on information on a factor that affects a use situation of the target device;
determine, based on the failure factor information, that the predicted use situation is a use situation that causes the failure factor;
these limitations, under their broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person is capable of predicting a situation or failure of a device based on comparing it to situations and failure informations of other similar devices using observation, evaluation and analysis.
Step 2A, Prong Two:
Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 10 and 11):
acquire failure factor information including a content of a failure that has occurred in a device, a failure factor of the failure, and a use situation of the device that causes the failure factor;
and notify, according to a result of determination, failure prediction information indicating a possibility that a failure occurs in the target device.
These limitations are considered as adding insignificant extra-solution activity (acquiring data and providing data/notifications) to the judicial exception - see MPEP 2106.05(g).
The further additional elements of “processors” and/or “computer” as recited in these independent claims are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as appending well-understood, routine, conventional activities (acquiring data and providing data/notifications) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). The further additional elements of “processors” and/or “computer” as recited in these independent claims amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are therefore not patent eligible.
Dependent Claim 2 is also directed towards the “Mental Processes” groupings of abstract ideas. That is a person is capable of predicting what use situation is most likely for a target device using the failure factor information by using observation, evaluation and analysis of the failure factor information.
Dependent Claim 3 is considered as generally linking the use of the judicial exception to a particular technological environment or field of use (the particular facility) – see MPEP 2106.05(h).
Regarding Claim 4, its limitations (except the last limitation) are considered as generally linking the use of the judicial exception to a particular technological environment or field of use (the particular environment/weather information) – see MPEP 2106.05(h). The last limitation is also directed towards the “Mental Processes” groupings of abstract ideas. That is a person is capable of predicting environment information of the target device using weather information by using observation, evaluation and analysis of the weather information. The “processors” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component.
Regarding Claims 5 and 6, its limitations (except the last limitation) are considered as appending well-understood, routine, conventional activities (acquiring information/data) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). The last limitation is also directed towards the “Mental Processes” groupings of abstract ideas. That is a person is capable of predicting an amount of usage of the target device using sales information by using observation, evaluation and analysis (that is high sales volume at a store will likely indicate higher usage of freezer device, for example). The “processors” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component.
Dependent Claims 7-9 are considered as appending well-understood, routine, conventional activities (acquiring information/determining information – i.e. the failure factor information) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
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.
Claims 1-4, 7-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ogawa, JP 2014174896 A.
Regarding Claim 1, Ogawa teaches:
An information processing apparatus comprising: a memory storing instructions; and one or more processors configured to execute the instructions to (Abstract):
acquire failure factor information including a content of a failure that has occurred in a device, a failure factor of the failure, and a use situation of the device that causes the failure factor (p. 2: “The device management apparatus 50 collects information indicating the attribute and state of the device 60 (hereinafter referred to as “device information”) from each device 60 and forwards the collected device information to the failure management device 10. Computer. The collection of the device information and the transfer to the failure management apparatus 10 may be performed periodically. If there is device information indicating the occurrence of a failure among the collected device information, the device management device 50 transmits information indicating the content of the failure (hereinafter referred to as “failure information”) to the failure management device”. The failure information including failure factor of the failure and use situation of the device that causes the failure factor; see for example p. 7: “The failure information receiving unit 11 receives failure information and device information transmitted from the device management apparatus 50. The received failure information is stored in the failure information storage unit 14. The statistical information generation unit 12 generates statistical information related to a failure based on the failure information stored in the failure information storage unit 14. For example, statistical information indicating the state of the device 60 and the failure occurrence rate, the state of the usage environment of the device 60 and the failure occurrence state, and the like are generated”. The state of the device and environment representative of the failure factor of the failure and a use situation of the device);
predict a use situation of a target device of a same type as the device installed in a facility based on information on a factor that affects a use situation of the target device (p. 10: “the failure occurrence prediction unit 13 calculates the similarity between the statistical information of the target part for the target customer and the statistical information of the target part for the similar customer for each of the indicators”; And, “the failure occurrence prediction unit 13 derives a failure occurrence rate based on the statistical information of the target part in the target customer (S305). Specifically, by applying a value corresponding to the current state of the target device”; And, “The current value of the target device 60 can be specified based on, for example, a record for the target device 60 in the device information storage unit 16, a record for the target customer in the environment information storage unit 42, or the like. For example, the number of output sheets related to the index A and the color usage rate related to the index B can be specified based on the counter information. The number of years elapsed for the index C can be specified based on the date of introduction. For the temperature related to the index D and the humidity related to the index E, the average temperature or average humidity stored in the month to which the current day belongs is applied to the area to which the target customer belongs in the climate information table”. That is the use situation/failure of the target device is predicted based on similar devices in similar environments and conditions. Examiner’s note: see also Seo, US 2019/0196430 A1, for example paragraph 190);
determine, based on the failure factor information, that the predicted use situation is a use situation that causes the failure factor (p. 7: “The failure occurrence prediction unit 13 predicts the occurrence of a failure by applying the current state of the device 60 and the current state of the usage environment of the device”. The current state of the usage environment representative of the use situation);
and notify, according to a result of determination, failure prediction information indicating a possibility that a failure occurs in the target device (p. 11: “the failure occurrence predicting unit 13 indicates that there is a high possibility that a failure will occur for the target part of the target device 60 in the target customer. Prediction information is generated and the prediction information is notified to, for example, a service person (S308). The notification of the prediction information to the service person may be realized, for example, by transmitting an e-mail to an information processing apparatus such as a smartphone, a tablet terminal, a PC (Personal Computer), or a mobile phone that the service person has”).
Regarding Claim 2, Ogawa further teaches:
The information processing apparatus according to claim 1, wherein the one or more processors are further configured to execute the instructions to: predict a use situation included in the failure factor information among use situations of the target device (p. 3: “The failure occurrence prediction unit 13 predicts the occurrence of a failure by applying the current state of the device 60 and the current state of the usage environment of the device”. The usage environment representative of the use situation).
Regarding Claim 3, Ogawa further teaches:
The information processing apparatus according to claim 1, wherein information on the factor that affects the use situation of the target device includes information on a facility in which the target device is installed (p. 6: “The environment information management device 40 manages information related to the environment (hereinafter referred to as “environment information”) for each region to which the customer environment belongs. Examples of environmental information include temperature and humidity”).
Regarding Claim 4, Ogawa further teaches:
The information processing apparatus according to claim 1, wherein the failure factor information includes environment information indicating an environment in which the device is used as a use situation of the device causing the failure factor (p. 6: “The environment information management device 40 manages information related to the environment (hereinafter referred to as “environment information”) for each region to which the customer environment belongs. Examples of environmental information include temperature and humidity”),
wherein information on a factor affecting a use situation of the target device is weather information of an area including a facility in which the target device is installed (p. 6: “The environment information management device 40 manages information related to the environment (hereinafter referred to as “environment information”) for each region to which the customer environment belongs. Examples of environmental information include temperature and humidity”. Temperature and humidity representative of weather information),
and wherein the one or more processors are further configured to execute the instructions to: predict environment information of the target device among use situations of the target device based on the weather information (p. 13: “an environmental information storage unit that stores environmental information that affects the state of the device, The failure prediction system according to claim 2, wherein the prediction unit identifies another place to be used for the correction based on similarity between the environment information for the first place and the environment information of another place”).
Regarding Claim 7, Ogawa further teaches:
The information processing apparatus according to claim 1, wherein the one or more processors are further configured to execute the instructions to: specify the failure factor information based on failure information on a failure occurring in the device, the failure information being collected from a plurality of groups including a plurality of facilities (p. 3: “The device information storage unit 16 stores device information transferred from the device management apparatus 50 in each customer environment”; And, p. 4: “The plurality of customer environments are examples of a plurality of places”; And, p. 6: “The device information collection unit 51 collects device information from each device 60. The collected device information is stored in the device information storage unit 53. If the collected device information includes device information indicating that a failure has occurred, the failure information transmission unit 52 transmits failure information indicating the occurrence of the failure to the failure management apparatus 10. In the present embodiment, for the sake of convenience, the failure information transmission unit 52 also transfers device information to the failure management apparatus”);
and in a case where a number of occurrences of a certain failure in a group is larger than a number of occurrences of the failure in another group by a predetermined value or more, a use situation and a failure factor of the group in which the number of occurrences of the failure is larger by a predetermined value or more are specified as the failure factor information (p. 11: “the failure occurrence prediction unit 13 determines whether the corrected failure occurrence rate based on any one of the indicators A to E exceeds the threshold value α. The threshold value α is a value that should be determined to have a high failure rate, such as 60%, and is set in advance and stored in the auxiliary storage device”. The threshold value representative of the predetermined value).
Regarding Claim 8, Ogawa further teaches:
The information processing apparatus according to claim 7, wherein the one or more processors are further configured to execute the instructions to: among use situations of a group in which a number of occurrences of a failure is greater than or equal to a predetermined value, a use situation having a difference from use situations of other groups is set as the failure factor information in association with the failure (p. 11: “the failure occurrence prediction unit 13 determines whether the corrected failure occurrence rate based on any one of the indicators A to E exceeds the threshold value α. The threshold value α is a value that should be determined to have a high failure rate, such as 60%, and is set in advance and stored in the auxiliary storage device”; And, “If the corrected failure rate for any index exceeds the threshold value α (Yes in S307), the failure occurrence predicting unit 13 indicates that there is a high possibility that a failure will occur for the target part of the target device 60 in the target customer”).
Regarding Claim 9, Ogawa further teaches:
The information processing apparatus according to claim 7, wherein the one or more processors are further configured to execute the instructions to: in a case where a difference in a number of occurrences of a certain failure for each group is less than a predetermined value, a use situation is not associated with the failure factor information of the device in which the failure occurs (p. 11: “On the other hand, no prediction information is generated for any part whose failure rate is equal to or less than the threshold value α”).
Claims 10 and 11 are similar to Claim 1 and are rejected under the same rationale as stated above for that claim.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Ogawa, JP 2014174896 A, in view of Ozaki, JP 2020113165 A.
Regarding Claim 5, with Ogawa teaching those limitations of the claim as previously pointed out, Ogawa may not have taught all of the following, however, Ozaki shows:
The information processing apparatus according to claim 1, wherein the failure factor information includes a number of times of use or a use amount of the device as a use situation of the device causing the failure factor, wherein information on a factor that affects a use situation of the target device is information indicating a sales prediction of the facility (Abstract: “A failure prediction system comprises: a sales information storage part that is managed by a seller selling a device or a component to a user who uses the device or the component and that records sales information of the device or the component for the user; a collection part that collects operation information of the device or the component via a network; an operation information storage part that records the operation information of the device or the component in association with the sales information stored in the sales information storage part; and a prediction part that predicts a failure time point of the device or the component by performing machine learning on the basis of the sales information and the operation information”),
and wherein the one or more processors are further configured to execute the instructions to: predict a number of times of use or a use amount of the target device among use situations of the target device based on information indicating a sales prediction of the facility (p. 7: “In addition, the failure prediction system 1 enables the seller to obtain information about failure prediction, and therefore, the failure prediction system 1 can be used for the sales prediction of the seller itself and the manufacturing prediction and sales prediction of a manufacturer who sells products and parts to the seller”; And, p. 4: “The predicting unit 19 predicts a failure timing (failure prediction time) of the device or the component by machine learning based on the sales information and the operation information. Specifically, the prediction unit 19 machine-learns past sales information and operation information accumulated in the sales information storage unit 17 and the operation information storage unit 18 from the time the device operates until the time the device fails, and Generate an inference model to infer the point of failure of the part”). (Emphasis added).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Ozaki with that of Ogawa for predicting sales or use time of a device using a number of times of use or a use amount of the device.
The ordinary artisan would have been motivated to modify Ogawa in the manner set forth above for the purposes of enabling a seller to enjoy the merit of being able to predict the proper supply timing and supply quantity of the product or component and also to propose the supply before the stock runs out [Ozaki: p. 7].
Regarding Claim 6, with Ogawa teaching those limitations of the claim as previously pointed out, Ogawa may not have taught all of the following, however, Ozaki further shows:
The information processing apparatus according to claim 5, wherein information on a factor affecting a use situation of the target device is sales data of the facility (Abstract: “A failure prediction system comprises: a sales information storage part that is managed by a seller selling a device or a component to a user who uses the device or the component and that records sales information of the device or the component for the user; a collection part that collects operation information of the device or the component via a network; an operation information storage part that records the operation information of the device or the component in association with the sales information stored in the sales information storage part; and a prediction part that predicts a failure time point of the device or the component by performing machine learning on the basis of the sales information and the operation information”),
and wherein the one or more processors are further configured to execute the instructions to: generate, based on the sales data, information indicating a sales prediction of the facility (p. 7: “In addition, the failure prediction system 1 enables the seller to obtain information about failure prediction, and therefore, the failure prediction system 1 can be used for the sales prediction of the seller itself and the manufacturing prediction and sales prediction of a manufacturer who sells products and parts to the seller”);
and predict a number of times of use or a use amount of the target device based on calculated information indicating a sales prediction of the facility (p. 4: “The predicting unit 19 predicts a failure timing (failure prediction time) of the device or the component by machine learning based on the sales information and the operation information. Specifically, the prediction unit 19 machine-learns past sales information and operation information accumulated in the sales information storage unit 17 and the operation information storage unit 18 from the time the device operates until the time the device fails, and Generate an inference model to infer the point of failure of the part”; And Abstract: “A failure prediction system comprises: a sales information storage part that is managed by a seller selling a device or a component to a user who uses the device or the component and that records sales information of the device or the component for the user; a collection part that collects operation information of the device or the component via a network; an operation information storage part that records the operation information of the device or the component in association with the sales information stored in the sales information storage part; and a prediction part that predicts a failure time point of the device or the component by performing machine learning on the basis of the sales information and the operation”. The operation information of the device representative of the number of times of use or a use amount of the target device).
Examiner's Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Watanabe, US 2014/0143625 A1, teaches generating a failure predictor pattern in accordance with previous cases of failure that has occurred in a first system configuration, and determining, in accordance with calculated difference information, whether the failure predictor pattern is applicable to detection of a predictor of failure in a second system configuration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVE MISIR/Primary Examiner, Art Unit 2127
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