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 .
Claim(s) 1-20 are pending for examination. Claim(s) 21-24 are withdrawn. This action is Non-Final.
Election/Restrictions
Applicant’s election without traverse of prosecution of Group 1 (Claims 1-20) in the reply filed on 12/16/20205 is acknowledged.
Claim(s) 21-24 is/are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group 2, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/16/20205.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1-20 are directed to a process, machine, and/or, manufacture. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and similar claim(s) 10 and 19, taken as representative, recites at least the following limitations that recite an abstract idea:
A method, comprising:
obtaining
performing
receiving
determining
transforming, using the at least one processor, the warranty coverage metric at a device into human-readable form.
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The broadest reasonable interpretation of these limitations for claim 1 includes obtaininghistorical warranty data and current IoT data; performing
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and similar claim(s) 9 and 15 recite i.e., processor, including memory/medium. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and similar claim(s) 9 and 15 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 9 and 15 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and for similar claim(s) 9 and 15 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and similar claim(s) 9 and 15, i.e., processor, including memory/medium; thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 9 and 15 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 9 and 15 is ineligible.
Regarding Claims 2-8, 10-14, and 16-20, claim(s) 2-8, 10-14, and 16-20 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions) i.e., further features related to “warranty coverage”. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component (i.e., claim 5, and similar claim(s) 13 and 19 – virtual assistant and claim 6, and similar claim(s) 14 and 20 – deep learning/machine learning). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
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.
Claim(s) 1-4, 6-7, 9-12, 14-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tennur Narayanan et al. (US 2021/0398187 A1) in view of Miller et al. (US 2022/0005051 A1) as evidenced by Provisional application No. 63/048,250 dated on 7/6/2020 and Lerick et al. (US 2016/0117646 A1).
Regarding Claim 1;
Tennur Narayanan discloses a method comprising:
obtaining, by at least one processor, service data, originating from at least one service data source, wherein the service data is associated with at least one asset and comprises at least historical warranty data and current ... data ([0027] - The server 104 may receive the telemetry data 146 from multiple computing devices. including, for example, the representative computing device 102. Previously gathered data 122(1) may be associated with a device identifier 120(1) associated with a first computing device and previously gathered data 122(N) may be associated with a device identifier 120(N) associated with an Nth computing device (N>0). For example, for the computing device 102, the device identifier 116 may be one of the device identifiers 120 stored at the server 104 and the telemetry data 146 may be stored with a corresponding one of the associated data 122. To illustrate, the data 122(N) (e.g., associated with a computing device having the device identifier 120(N)) may include historical telemetry data 124, historical service request data 126 and warranty data 128. The warranty data 128 may indicate a current warranty associated with the corresponding computing device identified by the device identifier 120(N)... The historical service requests 126 may include service requests associated with the computing device identified by the device identifier 120. For example, each service request may be a call to a service phone number provided by the manufacturer, a chat session with support personnel on a website maintained by the manufacturer, an email sent by the user to support personnel of the manufacturer, or another type of service request associated with the computing device 102. The historical telemetry data 124 may include accumulated telemetry data sent by the corresponding computing device (e.g., identified by the device identifier 120) starting from when the support assist 144 was activated).
performing, using at the least one processor, predictive analysis to generate an asset survival prediction based on current data associated with a first asset and the service data ([0016] - Based on the usage profile, the server may use one or more machine learning algorithms to make various predictions such as, for example, whether one or more hardware components of the computing device are predicted to fail, how much of the current warranty the user is predicted to use, a cost-benefit analysis to date of the warranty, a projected cost benefit analysis (e.g., up until just prior to expiration of the warranty), and the like and [0018] and [0029])
[concepts of] troubleshooting data ([0027]-[0028] – service requests/historical service requests).
determining, using the at least one processor, a warranty coverage metric based on the asset survival prediction and the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to the asset survival prediction and the troubleshooting data ([0028]-[0029] - The server 104 may include one or more machine learning algorithms 130 to perform various actions, including determining a usage profile 131 associated with a user of a computing device, determining one or more predictions 132 associated with the computing device (e.g., when one or more components are predicted to fail), determining a cost-benefit analysis 134, analyzing a current warranty and the associated service requests of the computing device... and [0033] - Based on the predictions 132, the recommendations 136 may include a warranty recommendation 138 to purchase an extended warranty or upgrade from a current warranty to a higher level warranty (e.g., Bronze to Silver, Silver to Gold, Premium to Premium Plus, or the like), a backup recommendation 140 to purchase a data backup service (e.g., to protect against data loss), an upgrade recommendation of (i) a component of the computing device 102 or (ii) to a different computing device, or any combination thereof) and
transforming, using the at least one processor, the warranty coverage metric at a device into human-readable form ([0033]).
Tennur Narayanan fails to explicitly disclose
obtaining, by at least one processor, service data, originating from at least one service data source, wherein the service data is associated with at least ... current IoT data;
[...]
receiving, using the least one processor, troubleshooting data associated with the first asset from at least one knowledge data source;
determining, using the at least one processor, a warranty coverage metric based on the ... the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to ... the troubleshooting data; and
transforming, using the at least one processor, the warranty coverage metric at a device into human-readable form.
However, in an analogous art, Miller teaches
obtaining, by at least one processor, service data, originating from at least one service data source, wherein the service data is associated with at least ... current IoT data ([0039]-[0040] - The IoT devices 24 may include, or receive data from, sensors or other circuits that monitor the assets 26... The IoT devices 24 may collect and transmit data to the server 18.) As evidenced by Provisional application No. 63/048,250 see Abstract and [0003] and [0010]-[0012].
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Miller to the service data source of Tennur Narayanan to include obtaining, by at least one processor, service data, originating from at least one service data source, wherein the service data is associated with at least ... current IoT data.
One would have been motivated to combine the teachings of Miller to Tennur Narayanan to do so as it provides / allows improved management of asset entitlements using IoT-based technologies (Miller, [0005]).
Further, in an analogous art, Lerick teaches
receiving, using the least one processor, troubleshooting data associated with the first asset from at least one knowledge data source ([0046] - For example, the computing device 124 can provide a set of stock images of candidate components, and optionally, additional descriptive information (e.g., product information, location where the components were installed in the building at issues, troubleshooting information) for each of the candidate components. In some implementations, the computing device 124 can provide a visual indication of a candidate matching component that corresponds to an unidentified component visually captured by the computing device 124. For example, an augmented reality feature of the computing device 124 can highlight an unidentified component corresponding to a potentially matching component on a display of the computing device 124 in near real-time and [0109] - Other features are depicted in the screenshot 600c, including a warranty option 612, which the user can select to view warranty information for the selected component; a coverage option 614, which the user can select to view coverage of the selected component; a troubleshoot option 616, which the user can select to troubleshoot a resolution to the issue themselves; and a service call option 618, which the user can select to directly place a service call);
determining, using the at least one processor, a warranty coverage metric based on the ... the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to ... the troubleshooting data ([0032] - Such management can include providing the owner 110 with access to information about the building 104, such as component and warranty information and [0036]-[0039] - As indicated by step D (128), the computer system 102 can receive the information describing the issue from the owner 110 and can, based on the information, identify one or more of the components 106 as candidates for being a source of the issue... The computer system 102 can additionally identify current warranty coverage for these components as they are installed in the building 104. For example, the kitchen faucet may be still under a manufacturer's warranty whereas a manufacturer's warranty for the refrigerator may have expired and [0052] - For instance, the instructions that are sent to the computing device 124 may include a description of where the building owner 110 should go within the house to the resolve the issue and pictures of components, such as a water shutoff valve, that the owner 110 can use to mitigate the issue. Although not depicted in FIG. 1, such instructions can be output on the computing device 124 in a similar manner to the user interface outputting information about the candidate components and warranty information.); and
transforming, using the at least one processor, the warranty coverage metric at a device into human-readable form (FIG. 6D and [0032] - Such management can include providing the owner 110 with access to information about the building 104, such as component and warranty information).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Lerick to the service data source of Tennur Narayanan and Miller to include receiving, using the least one processor, troubleshooting data associated with the first asset from at least one knowledge data source; determining, using the at least one processor, a warranty coverage metric based on the ... the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to ... the troubleshooting data; and transforming, using the at least one processor, the warranty coverage metric at a device into human-readable form.
One would have been motivated to combine the teachings of Lerick to Tennur Narayanan in view of Miller to do so as it provides / allows managing building information and resolving building issues (Lerick, [0002]).
Regarding Claim 2;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Miller further teaches wherein the service data includes data associated with asset health of an entity (Abstract - Sensors categorized as part of an industrial IoT environment are used to detect asset health, including asset abnormalities and trends). As evidenced by Provisional application No. 63/048,250 see Abstract and [0003] and [0010]-[0012].
Similar rationale and motivation is noted for the combination of Miller to Tennur Narayanan in view of Miller and Lerick, as per claim 1, above.
Regarding Claim 3;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Tennur Narayanan further discloses wherein performing the predictive analysis is based on, at least in part, feedback associated with the troubleshooting data ([0027] - The historical service requests 126 may include service requests associated with the computing device identified by the device identifier 120. For example, each service request may be a call to a service phone number provided by the manufacturer, a chat session with support personnel on a website maintained by the manufacturer, an email sent by the user to support personnel of the manufacturer, or another type of service request associated with the computing device 102. The historical telemetry data 124 may include accumulated telemetry data sent by the corresponding computing device (e.g., identified by the device identifier 120) starting from when the support assist 144 was activated)
Regarding Claim 4;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Tennur Narayanan further discloses wherein the predictive analysis comprises survivability modeling ([0029] - The server 104 may include one or more machine learning algorithms 130 to perform various actions, including determining a usage profile 131 associated with a user of a computing device, determining one or more predictions 132 associated with the computing device (e.g., when one or more components are predicted to fail), determining a cost-benefit analysis 134, analyzing a current warranty and the associated service requests of the computing device (e.g., “By upgrading to a higher-level or extended warranty you saved $M in out-of-pocket expenses because your service requests were covered by the warranty”, M>0), providing one or more recommendations 136, or any combination thereof. The machine learning algorithms 130 may include one or more of types of supervised learning, such as, for example, Support Vector Machines (SVM), linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithm, Neural Networks such as Multilayer perceptron or similarity learning, or the like).
Regarding Claim 6;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Tennur Narayanan further discloses concepts of deep-learning... model ([0029] – ... neural networks such as Multilayer perceptron or similarity learning...).
Lerick further teaches wherein the at least one knowledge data source is a deep-learning image classification model or a machine learning classification model ([0045] - For example, the computer system 102 can determine candidate matching components using the building data 122a and based one or more matching techniques (e.g., image recognition techniques, pattern matching techniques) scoring the candidate components along one or more dimensions (e.g., visual similarity to the target component, location similarity, function similarity and [0064] - For example, analytics can be provided to and/or to other parties... such analytics can be provided using any of a variety of appropriate techniques, such as machine learning techniques (e.g., neural networks, clustering, regressions, decision trees) that can identify correlations and associations....).
Similar rationale and motivation is noted for the combination of Lerick to Tennur Narayanan in view of Miller and Lerick, as per claim 1, above.
Regarding Claim 7;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Tennur Narayanan further discloses wherein the warranty coverage metric is a warranty pricing strategy [0033] - Based on the predictions 132, the recommendations 136 may include a warranty recommendation 138 to purchase an extended warranty or upgrade from a current warranty to a higher level warranty (e.g., Bronze to Silver, Silver to Gold, Premium to Premium Plus, or the like), a backup recommendation 140 to purchase a data backup service (e.g., to protect against data loss), an upgrade recommendation of (i) a component of the computing device 102 or (ii) to a different computing device, or any combination thereof).
Regarding Claim(s) 9-12 and 14; claim(s) 9-12 and 14 is/are directed to a/an system associated with the method claimed in claim(s) 1-4 and 6. Claim(s) 9-12 and 14is/are similar in scope to claim(s) 1-4 and 14, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 15-18 and 20; claim(s) 15-18 and 20 is/are directed to a/an medium associated with the method claimed in claim(s) 1-4 and 6. Claim(s) 15-18 and 20is/are similar in scope to claim(s) 1-4 and 6, and is/are therefore rejected under similar rationale.
Claim(s) 5, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tennur Narayanan et al. (US 2021/0398187 A1) in view of Miller et al. (US 2022/0005051 A1) as evidenced by Provisional application No. 63/048,250 dated on 7/6/2020 and Lerick et al. (US 2016/0117646 A1) and further in view of Nagarjuna et al. (US 2021/0149979 A1).
Regarding Claim 5;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Tennur Narayanan in view of Miller and Lerick fail to explicitly disclose wherein the at least one knowledge data source is a virtual assistant decision tree.
However, in an analogous art, Nagarjuna teaches wherein the at least one knowledge data source is a virtual assistant decision tree ([0046] - When a search is performed or a query is processed, cognitive knowledge platform 102 includes computer executable instructions that use a number of functions to locate and provide access to help content in help content data sources 114, decision trees 110, and knowledge based articles 112, including but not limited to, a contextual search, document features, scoring criteria, machine learning, ranking for relevancy, and caching. Document features may include the date of document creation, date published, date modified, date last viewed, date last liked, number of likes, number of boosts, date last boosting happened. It may also take into account where the “intent” of the search was found such as title of the document, body of the document, document category/sub category, attachments, etc. Scoring for each document may be done based on an algorithm (e.g., BM25) after the “intent” is identified. The process along involved “stemming” and “lemmatization” of the content, along with other techniques such as similarity search. Once the initial scoring is done, other document features are applied to the machine learning model to raking the content, leading to relevancy of the searched articles/content).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Nagarjuna to the knowledge data source of Tennur Narayanan and Miller and Lerick to include wherein the at least one knowledge data source is a virtual assistant decision tree
One would have been motivated to combine the teachings of Nagarjuna to Tennur Narayanan and Miller and Lerick to do so as it provides / allows efficient identification of useful help content without having to conduct searches on multiple platforms (Nagarjuna, [0004]).
Regarding Claim(s) 13; claim(s) 13 is/are directed to a/an system associated with the method claimed in claim(s) 5. Claim(s) 13 is/are similar in scope to claim(s) 5, and is/are therefore rejected under similar rationale.
Regarding Claim(s) 19; claim(s) 19 is/are directed to a/an medium associated with the method claimed in claim(s) 5. Claim(s) 19 is/are similar in scope to claim(s) 5, and is/are therefore rejected under similar rationale.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tennur Narayanan et al. (US 2021/0398187 A1) in view of Miller et al. (US 2022/0005051 A1) and Lerick et al. (US 2016/0117646 A1) as evidenced by Provisional application No. 63/048,250 dated on 7/6/2020 and further in view of Gray (US 2014/0047271 A1).
Regarding Claim 8;
Tennur Narayanan in view of Miller and Lerick discloses the method to Claim 1.
Tennur Narayanan in view of Miller and Lerick fail to explicitly disclose wherein the asset survival prediction is a Weibull distribution, Kaplan-Meier (KM) estimator, Cox-proportional hazard model, or Random Survival Forest model.
However, in an analogous art, Gray teaches wherein the asset survival prediction is a Weibull distribution, Kaplan-Meier (KM) estimator, Cox-proportional hazard model, or Random Survival Forest model ([0074] and [0080] and [0086]).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Gray to the knowledge data source of Tennur Narayanan and Miller and Lerick to include wherein the asset survival prediction is a Weibull distribution, Kaplan-Meier (KM) estimator, Cox-proportional hazard model, or Random Survival Forest model
One would have been motivated to combine the teachings of Gray to Tennur Narayanan and Miller and Lerick to do so as it provides / allows for testing the reliability of complex systems, and includes the evaluation and optimisation of the availability of such systems (Gray, [0002]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wakim (US 9,007,229 B1) discusses User devices are used to access various forms of electronic content. Sensors in the user device or information about environmental data associated with the device, such as weather at the locale of the user device, may be used to determine the occurrence of physical events. Recommendations such as offers for sale of extended warranties, warranty replacement, and so forth may be provided based at least in part on the physical events. (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
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/ASFAND M SHEIKH/ Primary Examiner, Art Unit 3626