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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action in response to communication received on 1/21/2026. Claims 1-22 are pending in this application.
Response to Amendment
Applicant’s amendments to claims 1, 6, 11, and 17 are acknowledged. Applicant’s addition of new claims 21-22 are acknowledged.
Response to Arguments
On Remarks page 11, Applicant argues the claims do not recite a mental process as the claims recite steps that cannot be performed in the human mind. While the Examiner understands Applicant’s arguments here the Examiner respectfully disagrees. MPEP 2106.04(a)(2), states that claims that require a computer may still recite a mental process, cited herein below:
C. A Claim That Requires a Computer May Still Recite a Mental Process
Claims can recite a metnal process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
As discussed in the 101 rejection below the claims are recited at such a high level of abstraction that the claims recite observations, evaluations, judgements, and opinions that could be performed by a human or humans, and the additional elements of that the model is “machine learning” and some of the steps are being performed by a “computing device” are recited at such a high level of generality that they merely result in apply it or generally linking it to the field of computers, resulting in “2)” and “3)” above under the above recited MPEP 2106.04(a)(2) section. Therefore the claims recite a mental process even though as argued by Applicant the claims recite a computer.
On remarks pages 11-12, Applicant argues Applicant’s new claims 21-22 do not recite a mental process. The Examiner respectfully disagrees as detailed in the updated 101 rejection below in view of Applicant’s amendments.
On Remarks page 11-12, Applicant argues the claims are similar to USPTO Example 39. The Examiner respectfully disagrees. USPTO example 39 was found eligible because the claims did not recite an abstract idea (judicial exception). The present application does as addressed above in section 3. The Document Subject Matter Eligibility Examples: Abstract Ideas page 1 from which Example 39 is found states the following “These examples should be interpreted based on the fact patterns set forth below as other fact patterns may have different eligibility outcomes.” Therefore since the fact pattern is different the Examiner does not find the argument persuasive.
The Examiner finds it important to note as claimed at this broad level of abstraction here in Applicant’s claims, training is a mental process and method of organizing human activity step (e.g. part of the abstract idea). Specifically it is a mental process or human activity step, as broadly recited in the claims, to create and update rules (e.g. models) over time based on known information or outcomes (e.g. when I see this, I perform this function or when I see this other fact pattern, I perform this other function). The additional element that as broadly recited in the claims the specific model is “machine learning” merely results in apply it or generally linking it to the field of computers as discussed in the 101 rejection below.
Applicant’s arguments with respect to the August 4, 2025 Memo on page 13 are acknowledged. Here the Memo is comparing and contrasting two different examples (USPTO example 39 and 27) and providing differences between claims that recite an exception and merely involve an exception, cited herein:
Distinguishing claims that recite a judicial exception from claims that merely involve a judicial exception: Examiners should be careful to distinguish claims that recite an exception (which require further eligibility analysis) from claims that merely involve an exception (which are eligible and do not require further eligibility analysis).9
Consider for example, the published USPTO examples 39, which illustrates claim limitations that merely involve an abstract idea, and 47, which shows limitations that recite an abstract idea.10 The claim limitation “training the neural network in a first stage using the first training set” of example 39 does not recite a judicial exception. Even though “training the neural network” involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. Contrast this with the limitation “training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” of claim 2 of example 47. This limitation requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception, namely an abstract idea.
Specifically, the Memo discusses how the limitation “training the neural network in a first stage using the first training set” does not recite a judicial exception in the context of USPTO example 39 and the limitation of “training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite a judicial exception in the context of USPTO Example 47.
It is noted that neither one of these above mentioned limitations with respect to machine learning training are actually recited in Applicant’s claims.
Here with respect to training and machine learning, Applicant recites updating a model over time based on specific user feedback with respect to accuracy of determinations (See claims 1, 21, and 22). As detailed in the 101 rejection as broadly recited in the claims, these limitations do recite mental process and human activity steps specifically creating a set of rules (e.g. model) and updating it over time (training) based on received specific feedback that relates to the accuracy of such determinations (e.g. when this happened, the correct determination was made or when this other thing happened the incorrect determination was made). This is all part of the abstract idea as the claims are recited at such a high level of abstraction related to models and training. The additional element that the model is broadly recited as being related to “machine learning” merely results in apply it or generally linking it to the field of computers as discussed in the 101 rejection below. 7. On Remarks pages 14-18, Applicant argues the claims recite an improvement to the functioning of a computer and the Ex Parte Desjardins decision. Further it is noted Applicant’s cites paragraphs 0002, 0016-0020, 0024, 0030-0031, 0044-0045, 0053, 0060, 0063-0064, 0080, 0083-0084, 0095, and 0099-0101, in support of this argument. The Examiner has reviewed Applicant’s arguments and the corresponding cited sections of Applicant’s specification however the Examiner respectfully disagrees.
MPEP 2106.05(a) states the following with respect to improvements to the functioning of a computer:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).
The Examiner has reviewed the specification at the cited sections but does not see an improvement discussed in the section similar to those above (or as additionally defined in MPEP 2106.05(a)). Specifically the Examiner does not find where in the specification it includes “a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” The Examiner does not find for example in the specification, nor has Applicant argued such, discussions with respect to improvements to machine learning, utility sensors or meters, or the computing devices (cited in the claims). Rather the Examiner finds recitation of such above elements being merely used to implement or apply the judicial exception of “determining if a property is vacant, determining if a utility has atypical use according to rules, performing a responsive action in response to those determinations, and updating rules based on a the result of those actions (e.g. false positive result, correct result, incorrect result, etc.).”
Therefore the Examiner respectfully disagrees.
8. On remarks pages 18-21 with respect to the prior art, Applicant argues amendments to the independent claims. Specifically Applicant argues the limitation of “after determining that the location is not occupied, determining, with the machine learning model based on both a first portion of the metrology data indicating that consumption of a first utility commodity included in the plurality of utility commodities is above a first threshold during a first time period and a second portion of the metrology data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the first time period, that consumption of the first utility commodity is atypical, wherein the second utility commodity is different from the first utility commodity."
The Examiner has carefully considered Applicant’s arguments, amendments, and reconsidered the reference of Harvey et al. (United States Patent Application Publication Number: US 2024/0142065).
The Examiner still finds the reference of Harvey et al. to read on Applicant’s claims.
Harvey is directed towards a system that determines information like leaks based on home telematics data (see abstract), where the home telematics data is determined from numerous different types of sensors (see paragraphs 0083-0084). Harvey et al. goes to teach using trained machine learning (see paragraphs 0039 and 0087) to generate alerts of atypical usage where this information can be updated over time based on user feedback (e.g. a correct or incorrect alert (see paragraph 0087 and 0123)).
Harvey shows in one of its numerous different examples (as explained above Harvey can make abnormal determinations like water leaks based on numerous sensors) making a determination based on being below a threshold (temperature), the pipes likely contain water, and other thresholds like location and size (see paragraph 0100). It is noted here that the Examiner interprets “likely to contain” water to be above a threshold as broadly recited herein, whereas something like unlikely would be below a threshold. Paragraph 0088 discusses how water flow is sensed and paragraph 0020 discuss use of water sensors. The system can then send an alert to the user or remotely shut off the water valve (e.g. atypical usage of first utility), where the first utility is water as discussed in claim 2.
9. On remarks page 22, Applicant generally argues new claim 21-22. The Examiner has carefully considered such arguments however, the Examiner respectfully disagrees. These limitations are very broad in the claim. Claim 21 recites updating a model over time based on feedback regarding the accuracy of system determinations Further claim 22 recites training a model based on determinations of a typical usage and whether the determinations are accurate. Each are taught in Harvey below (see paragraphs 0087 and 0123) as Harvey clearly teaches updating the machine learning based on learned feedback from user responses related to activities determined by the system.
Claim Rejections - 35 USC § 101
10. 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.
11. Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10 and 21-22 recite a process as the claims recite a method. Claims 11-16 recite an article of manufacture as the claims recite a non-transitory computer readable medium. Claims 17-20 recite a machine as the claims recite a device including one or more processors executing instructions from a memory.
The claim(s) 1-22 recite(s) the idea of determining if a property is vacant, determining if a utility has atypical use according to rules, performing a responsive action in response to those determinations, and updating rules based on a the result of those actions (e.g. false positive result, correct result, incorrect result, etc.).
The claims are recited at such a high level of abstraction, that they recite observations, evaluations, judgements, and opinions that could be performed by a human or humans and accordingly the claims recite a mental process.
Further the claims recite limitations or functions a human or humans could perform by following rules specifically the claims recite managing personal or behavior or relationships or interactions between people including following rules or instructions. Managing personal or behavior or relationships or interactions between people including following rules or instructions is a certain method of organizing human activities.
Mental processes and certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(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 (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
Examiner notes that the Examiner has underlined and bolded the additional elements. Limitations that are not bolded and underlined are considered part of the abstract idea.
1. A method comprising:
receiving, by a computing device, metrology data indicating consumption of a plurality of utility commodities at a location;
determining, with a machine learning model associated with the computing device based on the metrology data, that the location is not occupied;
after determining that the location is not occupied, determining, with the machine learning model, based on both a first portion of the metrology data indicating that consumption of a first utility commodity included in the plurality of utility commodities is above a threshold during a first time period and a second portion of the metrology data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the first period of time, that consumption of the first utility commodity is atypical, wherein the second utility commodity is different from the first utility commodity;
and in response to determining that the location is not occupied and that consumption of the first utility commodity is atypical,
performing, by the computing device, a responsive action that comprises at least one of sending a message to a first computing device associated with the location or causing a supply of the first utility commodity to the location to be shut off,
receiving, by the computing device, a first user feedback response to the responsive action,
and training, by the computing device, the machine learning model to generate an updated machine learning model based on a first training data set that includes first user feedback response, the first portion of the metrology data, and the second portion of the metrology data.
2. The method of claim 1, wherein the first utility commodity is water and the plurality of utility commodities includes at least one of gas or electricity.
3. The method of claim 1, wherein the metrology data is received from one or more metering devices.
4. The method of claim 1, wherein determining that the location is not occupied comprises: providing, by the computing device, the metrology data as an input to the machine learning model; and receiving, by the computing device, an output from the machine learning model that indicates the location is not occupied.
5. The method of claim 1, wherein determining that consumption of the first utility commodity is atypical comprises: providing, by the computing device, the metrology data as an input to the machine learning model; and receiving, by the computing device, an output from the machine learning model that indicates consumption of the first utility commodity is atypical.
6. The method of claim 1, wherein determining that the metrology data indicates that the consumption of the first utility commodity is atypical comprises comparing an amount of the consumption of the first utility commodity during the first time period to the first threshold associated with consumption of the first utility commodity.
7. The method of claim 1, wherein determining that the location is not occupied comprises comparing an amount of the consumption of the second utility commodity of the plurality of utility commodities during a time period to a detection threshold for the second utility commodity.
8. The method of claim 1, wherein performing the responsive action comprises sending an instruction to a metering device to shut off a supply of the first utility commodity to the location.
9. The method of claim 1, wherein performing the responsive action comprises transmitting, by the computing device, a notification to a device associated with the location, the notification including an indication that atypical consumption of the first utility commodity has occurred when the location is unoccupied.
10. The method of claim 9, further comprising updating the machine learning model or a detection threshold based on a response to the notification.
11. One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors of a first computing device, cause the one or more processors to perform operations comprising:
receiving consumption data for a plurality of utility commodities at a premises from one or more utility meters;
detecting, with a machine learning model associated with the first computing device, that the premises is not occupied based on the consumption data;
after detecting that the premises is not occupied, detecting, with the machine learning model, that usage of a first utility commodity of the plurality of utility commodities is atypical based on both a first portion of the consumption data indicating that consumption of the first utility commodity included in the plurality of utility commodities is above a first threshold during a first time period and a second portion of the consumption data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the first time period; and
in response to detecting that the premises is not occupied and that usage of the first utility commodity is unexpected,
performing, by the first computing device, a responsive action that comprises at least one of sending a message to a second computing device associated with the premises or causing a supply of the first utility commodity to the premises to be shut off,
receiving, by the first computing device, a first user feedback response to the responsive action,
and training, by the first computing device, the machine learning model to generate an updated machine learning model based on a first training data set that includes the first user feedback response, the first portion of the consumption data, and the second portion of the consumption data
12. The one or more non-transitory computer readable media of claim 11, wherein the first utility commodity is water and the plurality of utility commodities includes at least one of gas or electricity.
13. The one or more non-transitory computer readable media of claim 11, wherein the operations further comprise: receiving one or more detection parameters; and detecting that the premises is not occupied further based on the detection parameters.
14. The one or more non-transitory computer readable media of claim 11, wherein the operations further comprise: receiving one or more detection parameters; and detecting that the usage of the first utility commodity is atypical further based on the detection parameters.
15. The one or more non-transitory computer readable media of claim 12, wherein detecting that usage of the first utility commodity is atypical comprises determining that a pattern of usage of the first utility commodity during a time when the first utility commodity is used diverges from an expected pattern of consumption of the first utility commodity.
16. The one or more non-transitory computer readable media of claim 11, wherein the responsive action comprises notifying a user associated with the premises that unexpected consumption of the first utility commodity is occurring while the premises is unoccupied.
17. A network device, comprising:
one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to:
receive metrology data indicating consumption of a plurality of utility commodities at a property from a second network device and a third network device;
determine, with a machine learning model associated with the network device, that the property is not occupied based on the metrology data;
after determining that the property is not occupied, determine with the machine learning model, that undesired consumption of a first utility commodity is occurring based on both a first portion of the metrology data indicating that consumption of first utility commodity included in the plurality of commodities is above a first threshold during a first time period and a second portion of the metrology data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the first time period, wherein the second utility commodity is different form the first utility commodity; and
and in response to determining that the property is not occupied and that undesired consumption of the first utility commodity is occurring,
performing, by the network device, a responsive action that comprises at least one of sending a message to a first computing device associated with the property or causing a supply of the first utility commodity to the property to be shut off,
receiving, by the network device a first user feedback response to the responsive action,
and training, by the network device, the machine learning model to generate an updated machine learning model based on a first training data set that includes the first user feedback response, the first portion of the metrology data, and the second portion of the metrology data
18. The network device of claim 17, wherein the first utility commodity is water and the plurality of utility commodities includes at least one of gas or electricity.
19. The network device of claim 17, wherein: the second network device is a metering device configured to monitor consumption of the first utility commodity; and the third network device is a metering device configured to monitor consumption of a second utility commodity of the plurality of utility commodities.
20. The network device of claim 17, wherein the network device is a metering device configured to monitor consumption of a third utility commodity of the plurality of utility commodities, a home automation system, or a server of a utility service provider.
21. The method of claim 1, wherein the first training data set further includes first accuracy information indicating whether the determination that the location is not occupied is accurate, the first user feedback response includes second accuracy information indicating whether the determination that the consumption of the first utility commodity is atypical is accurate, and training the machine learning model comprises modifying at least one parameter or threshold included in the machine learning model based on the first accuracy information and the second accuracy information.
22. The method of claim 21, wherein the machine learning model is previously trained based on a plurality of determinations whether consumption of the first utility commodity is atypical and a plurality of training labels indicating whether each of the plurality of determinations is accurate.
As per claim 1, the claims recite limitations a human or humans could perform. Specifically a human could receive data from one or more devices (for example looking at the device and writing information down), determine based on that information according to rules (model) that the location is not occupied, further determine based on that according to rules (model) information that includes threshold from multiple utilities that the utility is atypical and in response to those determinations perform an action. Further a human could collect responses based on decisions and update the rules (or model) according to correct or incorrect responses to make better decisions in the future.
The additional elements that these limitations that could be performed by a human or humans are instead recited as being performed by a “computing device” and being sent to a “first computing device” merely results in “apply it.” Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 does not integrate a judicial exception into a practical application or provide significantly more. Further the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it.” The additional limitations provide only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it.” Further limitations that could be performed by a humans or humans that are instead broadly recited as being performed by “a computing device” and being sent to a “first computing device” merely results in generally linking the use of the judicial exception to the field of computers.
Further with respect to the additional element that the rules (or model) is a “machine learning” model results in no more than “apply it.” Here there are no details about a particular machine learning model or how it operates to determine the information other than it being used to determine or predict the outcome. The machine learning model is used to generally apply the abstract idea without placing any limitation on how the machine learning model operates to derive the information. In addition, the limitation recites only the idea of determining information using a machine learning model without details on how this accomplished. The claim omit any details as to how the machine learning model solves a technical problem, and instead recites only the idea of a solution or outcome. Also the claim invokes a generic machine learning model as a tool for making the recited determination rather than purporting to improve the technology or a computer. Therefore this limitation represents no more than mere instructions to apply the judicial exception on a computer. This can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers (see USPTO Example 48).
As per claim 2, the claims recite limitations a human or humans could perform. Specifically a human could receive or collect data from a gas, water, or electricity meter or utility. There are no additional elements beyond those recited above with respect to claim 1.
As per claim 3, the claims recite limitations a human or humans could perform. Specifically a human could receive or collect data from multiple metering devices. There are no additional elements beyond those recited above with respect to claim 1.
As per claim 4, the claims recite limitations a human or humans could perform. Specifically a human could collective or receive data, and use a model to make a prediction or outcome.
The fact that these limitations that could be performed by a human or humans are instead recited as being performed by “a computing device” and model or rules is “machine learning” results in merely apply it or generally linking it to the field of computers as previously addressed in claim 1.
As per claim 5, the claims recite limitations a human or humans could perform. Specifically a human could collective or receive data, and use a model to make a prediction or outcome.
The fact that these limitations that could be performed by a human or humans are instead recited as being performed by “a computing device” and model or rules is “machine learning” results in merely apply it or generally linking it to the field of computers as previously addressed in claim 1.
As per claim 6, the claims recite limitations a human or humans could perform. Specifically a human could compare an amount of consumption during a time period to a threshold to make a determination of something being atypical. There are no additional elements beyond those previously recited in claim 1.
As per claim 7, the claims recite limitations a human or humans could perform. Specifically a human could compare an amount of consumption during a time period to a threshold to make a determination of something being unoccupied. There are no additional elements beyond those previously recited in claim 1.
As per claim 8, the claims recite limitations a human or humans could perform. Specifically a human could make a determination then then send an instruction to another person or colleague for example to shut off the utility. The additional elements that these limitations that could be performed by a human or humans are instead recited as being performed by a “metering device” merely results in “apply it.” Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 does not integrate a judicial exception into a practical application or provide significantly more. Further the claims recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it.” The additional limitations provide only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it.” Further limitations that could be performed by a humans or humans that are instead broadly recited as being performed by “metering device” merely results in generally linking the use of the judicial exception to the field of computers.
As per claim 9, the claims recite limitations a human or humans could perform. Specifically a human could make a determination that there is atypical consumption and the location is unoccupied and provide a notification to another user or colleague for example. The additional elements that these limitations that could be performed by a human or humans are instead recited as being performed by a “computing device” and instead of being provided to another human are being provided to another “device” merely results in “apply it.” Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 does not integrate a judicial exception into a practical application or provide significantly more. Further limitations that could be performed by a humans or humans that are instead broadly recited as being performed by a “computing device” and instead of being provided to another human are being provided to another “device” merely results in generally linking the use of the judicial exception to the field of computers.
As per claim 10, the claims recite limitations a human or humans could perform. Specifically a human could update a model or a threshold based on previous determinations. The additional element that the model is a “machine learning” model results in no more than “apply it” or generally linking it to the field of computers as discussed above in claim 1.
As per claim 11, the claims recite limitations a human or humans could perform. Specifically a human could receive data from one or more devices (for example looking at the device and writing information down), determine based on that information according to rules (model) that the location is not occupied, further determine based on that according to rules (model) information that includes thresholds from multiple utilities that the utility is atypical and in response to those determinations perform an action. Further a human could collect responses based on decisions and update the rules (or model) according to correct or incorrect responses to make better decisions in the future.
The additional elements that these limitations that could be performed by a human or humans are instead recited as being performed by software running on a computer or more specifically as recited in the claims “one or more non-transitory computer readable media storing instructions which when executed by one or more processors of a first computing device cause the one or more processors to perform operations comprising” or “by the first computing device”, and being sent to a “second computing device” merely results in “apply it.” Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 does not integrate a judicial exception into a practical application or provide significantly more. Further the claims recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it.” The additional limitations provide only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it.” Further limitations that could be performed by a humans or humans that are instead broadly recited as being performed by software running on a computer or more specifically as recited in the claims “one or more non-transitory computer readable media storing instructions which when executed by one or more processors of a first computing device cause the one or more processors to perform operations comprising” or “by the first computing device”, and being sent to a “second computing device” merely results in generally linking the use of the judicial exception to the field of computers.
The additional element that the model is a “machine learning” model results in no more than “apply it.” Here there are no details about a particular machine learning model or how it operates to determine the information other than it being used to determine or predict the outcome. The machine learning model is used to generally apply the abstract idea without placing any limitation on how the machine learning model operates to derive the information. In addition, the limitation recites only the idea of determining information using a machine learning model without details on how this accomplished. The claim omit any details as to how the machine learning model solves a technical problem, and instead recites only the idea of a solution or outcome. Also the claim invokes a generic machine learning model as a tool for making the recited determination rather than purporting to improve the technology or a computer. Therefore this limitation represents no more than mere instructions to apply the judicial exception on a computer. This can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers (see USPTO Example 48).
As per claim 12, the claims recite limitations a human or humans could perform. Specifically a human could receive or collect data from a gas, water, or electricity meter or utility. There are no additional elements beyond those recited above with respect to claim 11.
As per claim 13, the claims recite limitations a human or humans could perform. Specifically a human could receive one or more detection parameters and determine that the premises is not occupied based on the detection parameters. There are no additional elements beyond those recited above with respect to claim 11.
As per claim 14, the claims recite limitations a human or humans could perform. Specifically a human could receive one or more detection parameters and determine that the utility is atypical based on the detection parameters. There are no additional elements beyond those recited above with respect to claim 11.
As per claim 15, the claims recite limitations a human or humans could perform. Specifically a human could detect that the usage of the first utility is a typical based on a pattern of usage of the first utility during a time is used diverges from an expected pattern of consumption of the utility. There are no additional elements beyond those recited above with respect to claim 11.
As per claim 16, the claims recite limitations a human or humans could perform. Specifically a human could notify another user associated with the premises that the unexpected consumption of the utility if occurring while the premises is unoccupied. There are no additional elements beyond those recited above with respect to claim 11.
As per claim 17, the claims recite limitations a human or humans could perform. Specifically a human could receive data from one or more devices(for example looking at the device and writing information down), determine based on that information according to rules (model) that the location is not occupied, further determine based on that according to rules (model) information that includes thresholds from multiple utilities that the utility is atypical and in response to those determinations perform an action. Further a human could collect responses based on decisions and update the rules (or model) according to correct or incorrect responses to make better decisions in the future.
The additional elements that these limitations that could be performed by a human or humans are instead recited as being performed by software running on a computer or more specifically as recited in the claims “a network device, comprising: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to:” or “by the network device”, and being sent to a “first computing device” merely results in “apply it.” Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 does not integrate a judicial exception into a practical application or provide significantly more. Further the claims recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it.” The additional limitations provide only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it.” Further limitations that could be performed by a humans or humans that are instead broadly recited as being performed by software running on a computer or more specifically as recited in the claims “a network device, comprising: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to:” or “by the network device”, and being sent to a “first computing device” merely results in generally linking the use of the judicial exception to the field of computers.
The additional element that the model is a “machine learning” model results in no more than “apply it.” Here there are no details about a particular machine learning model or how it operates to determine the information other than it being used to determine or predict the outcome. The machine learning model is used to generally apply the abstract idea without placing any limitation on how the machine learning model operates to derive the information. In addition, the limitation recites only the idea of determining information using a machine learning model without details on how this accomplished. The claim omit any details as to how the machine learning model solves a technical problem, and instead recites only the idea of a solution or outcome. Also the claim invokes a generic machine learning model as a tool for making the recited determination rather than purporting to improve the technology or a computer. Therefore this limitation represents no more than mere instructions to apply the judicial exception on a computer. This can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers (see USPTO Example 48).
As per claim 18, the claims recite limitations a human or humans could perform. Specifically a human could receive or collect data from a gas, water, or electricity meter or utility. There are no additional elements beyond those recited above with respect to claim 17.
As per claim 19, the claims merely recite or describe the devices from which the information is collected or received, therefore the claims recite limitations a human or humans could perform. Specifically a human could receive or collect data from a device used to measure a utility, for example by looking at the scale or gauges on the meter. There are no additional elements beyond those recited above with respect to claim 17.
As per claim 20, the claims merely recite or describe the devices the network device in claim 17 could be like a server, a home automation system, or a metering device. Further the fact that the network device is a server, a home automation system, or a metering device merely results in the same analysis of “apply it” or generally linking it to the field of computers as discussed above in claim 17 There are no additional elements beyond those recited above with respect to claim 17.
As per claim 21, the claims merely recite mental process steps and methods of organizing human activities of using various feedbacks (e.g. the determination was accurate, or inaccurate) to update a set of rules (e.g. model) over time. This is part of the abstract idea. The additional element that the model is merely recited as being a “machine learning” model merely results in apply it or generally linking it to the field of computers as discussed above in claim 1.
As per claim 22, the claims merely recite mental process steps and methods of organizing human activities of using various feedbacks (e.g. the determination was accurate, or inaccurate) to create a set of rules (e.g. model) This is part of the abstract idea. The additional element that the model is merely recited as being a “machine learning” model merely results in apply it or generally linking it to the field of computers as discussed above in claim 1.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(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 (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed under the practical application step.
Claim Rejections - 35 USC § 102
12. 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.
13. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
14. Claim(s) 1-6 and 8-22 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Harvey et al. (United States Patent Application Publication Number: US 2024/0142065).
As per claim 1, Harvey et al. teaches A method comprising: (see title and abstract, Examiner’s note: method).
receiving, by a computing device, metrology data indicating consumption of a plurality of utility commodities at a location; determining, with a machine learning model associated with the computing device based on the metrology data, that the location is not occupied; (see paragraphs 0010-00111, 0013, 0018, 0024, 0079, 0097, and 0109, Examiner’s note: determining that the property is not occupied where the system and method in Harvey et al are performed by software (instructions) executed by a computer (see paragraphs 0010-00111, 0013, 0018, 0024, 0097, and 0109). Further Harvey et al. occupancy is determined by machine learning (see paragraph 0079)).
After determining that the location is not occupied, determining with the machine learning model based on both a first portion of the metrology data indicating that consumption of the first utility commodity included in the plurality of utility commodities is above a first threshold during a first time period and a second portion of the metrology data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the time first time period, that consumption of the first utility commodity is atypical, wherein the second utility commodity is different from the first utility commodity; (paragraphs 0020, 0039, 0088, and 0100, Examiner’s note: teaches making a determination based on being below a threshold (temperature) and the pipes likely contain water, and other thresholds like location and size (see paragraph 0100). It is noted here that the Examiner interprets “likely to contain” water to be above a threshold as broadly recited herein, whereas something like unlikely would be below a threshold. Paragraph 0088 discusses how water flow is sensed and paragraph 0020 discuss use of water sensors. Teaches using machine learning to determine when irregularities occur (see paragraph 0039)).
and in response to determining that the location is not occupied and that consumption of the first utility commodity is atypical, performing, by the computing device a responsive action (see paragraphs 0093, 0105, 0113, and 0115-0116, Examiner’s note: performing operations or actions in response to determining a utility is atypical like leaking or below a temperature).
That comprises at least one sending a message to a first computing device associated with the location or causing a supply of the first utility commodity to the location be shut off (see paragraphs 0014, 0035-0036, 0103, 0106, and 0131, Examiner’s note: teaches shutting off a value (see paragraphs 0103, 0116) further teaches presenting an action command to a user (see paragraph 0014, 0035-0036, and 0131). It is noted additionally that only one of the alternatives is actually required by the claim).
Receiving, by the computing device, a first user feedback response to the responsive action; and training, by the computing device, the machine learning model to generate an updated machine learning model based on a first training data set that includes the first user feedback response, the first portion of the metrology data, and the second portion of the metrology data responsive action (see paragraph 0072, 0079, 0087, and 0123, Examiner’s note: training the model based on the home telematics and determinations (See paragraph 0072). Further training while in operation with incident data (see paragraph 0079). Further teaches updating training based on previous determinations (see paragraph 0087). Further paragraph 0123 teaches a user may interact with an interface to provide an indication that the system should use or discard the data as retraining data for the model determining whether irregular activity occurs in that this behavior is expected).
As per claim 2, Harvey et al. teaches
wherein the first utility commodity is water (see paragraphs 0007, 0013, Examiner’s note: water sensors).
and the plurality of utility commodities includes at least one of gas or electricity. (see paragraphs 0013 and 0037, Examiner’s note: electricity sensors, examiner notes that only one of the alternatives is required by the claims).
As per claim 3, Harvey et al. teaches
wherein the metrology data is received from one or more metering devices (see paragraphs 0013, 0020, 0047, and 0095-0096 Examiner’s note: teaches many different types of metering devices).
As per claim 4, Harvey et al. teaches
wherein determining that the location is not occupied comprises: providing, by the computing device, the metrology data as an input to the machine learning model; and receiving, by the computing device, an output from the machine learning model that indicates the location is not occupied (see paragraphs 0096-0097, and 0109, Examiner’s note: teaches using a machine learning model to determine whether a home is occupied).
As per claim 5, Harvey et al. teaches
wherein determining that consumption of the first utility commodity is atypical comprises: providing, by the computing device, the metrology data as an input to the machine learning model; and receiving, by the computing device, an output from the machine learning model that indicates consumption of the first utility commodity is atypical (see paragraphs 0072-0080, Examiner’s note: here shows numerous examples of how machine learning is used to determine particular data for example a pipe is leaking or a level of risk from, data of the home telematic data).
As per claim 6, Harvey et al. teaches
wherein determining that the metrology data indicates that the consumption of the first utility commodity is atypical comprises comparing an amount of the consumption of the first utility commodity during the first time period to the first threshold associated with consumption of the first utility commodity (see paragraph 0088 and 0100, Examiner’s note: determining water flow and in relation to temperature to make a determination, it is noted as discussed in claim 1 above, the Examiner interprets likely to contain water to be a threshold as broadly recited in the claims).
As per claim 8, Harvey et al. teaches
wherein performing the responsive action comprises sending an instruction to a metering device to shut off a supply of the first utility commodity to the location (see paragraphs 0037, 0116, 0125, 0131, Examiner’s note: teaches shutting off water to a location or a valve),
As per claim 9, Harvey et al. teaches
wherein performing the responsive action comprises transmitting, by the computing device, a notification to a device associated with the location, the notification including an indication that atypical consumption of the first utility commodity has occurred when the location is unoccupied (see paragraphs 0014-0015, 0021-0022, 0035-0036, 0040, and 0092-0093, Examiner’s note: here shows numerous ways the system generates alerts to users based on determinations).
As per claim 10, Harvey et al. teaches
further comprising updating the machine learning model or a detection threshold based on a response to the notification. (see paragraph 0072, 0079, 0087, and 0123, Examiner’s note: Only one of the above is required by the claims. training the model based on the home telematics and determinations (See paragraph 0072). Further training while in operation with incident data (see paragraph 0079). Further teaches updating training based on previous determinations (see paragraph 0087). Further paragraph 0123 teaches a user may interact with an interface to provide an indication that the system should use or discard the data as retraining data for the model determining whether irregular activity occurs in that this behavior is expected)).
As per claim 11, Harvey et al. teaches One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors of a first computing device, cause the one or more processors to perform operations comprising: (see paragraphs 0010-011, Examiner’s note: non transitory computer readable medium coupled to processors to execute instructions).
receiving consumption data for a plurality of utility commodities at a premises from one or more utility meters; detecting, with a machine learning model associated with the first computing device, that the premises is not occupied based on the consumption data; (see paragraphs 0010-00111, 0013, 0018, 0024, 0079, 0097, and 0109, Examiner’s note: determining that the property is not occupied where the system and method in Harvey et al are performed by software (instructions) executed by a computer (see paragraphs 0010-00111, 0013, 0018, 0024, 0097, and 0109). Further Harvey et al. occupancy is determined by machine learning (see paragraph 0079)).
After detecting that the premises in not occupied, detecting, with the machine learning model, that usage of a first utility commodity of the plurality of utility commodities is atypical based on both a first portion of the consumption data indicating that consumption of the first utility commodity included in the plurality of utility commodities is above a first threshold during a first time period and a second portion of the consumption data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the first time period, wherein the second utility commodity is different from the first utility commodity; and (paragraphs 0020, 0039, 0088, and 0100, Examiner’s note: teaches making a determination based on being below a threshold (temperature) and the pipes likely contain water, and other thresholds like location and size (see paragraph 0100). It is noted here that the Examiner interprets “likely to contain” water to be above a threshold as broadly recited herein, whereas something like unlikely would be below a threshold. Paragraph 0088 discusses how water flow is sensed and paragraph 0020 discuss use of water sensors. Teaches using machine learning to determine when irregularities occur (see paragraph 0039)).
and in response to detecting that the premises is not occupied and that consumption of the first utility commodity is unexpected, performing, by the first computing device, a responsive action (see paragraphs 0093, 0105, 0113, and 0115-0116, Examiner’s note: performing operations or actions in response to determining a utility is atypical like leaking or below a temperature).
That comprises at least one sending a message to a second computing device associated with the premises or causing a supply of the first utility commodity to the premises be shut off, (see paragraphs 0014, 0035-0036, 0103, 0116, and 0131, Examiner’s note: teaches shutting off a value (see paragraphs 0103, 0116) further teaches presenting an action command to a user (see paragraphs 0014, 0035-0036, 0131). It is noted additionally that only one of the alternatives is actually required by the claim).
Receiving, by the first computing device, a first user feedback response to the responsive action; and training, by the first computing device, the machine learning model to generate an updated machine learning model based on a first training data set that includes the first user feedback response, the first portion of the consumption data, the second portion of the consumption data (see paragraph 0072, 0079, 0087, and 0123, Examiner’s note: training the model based on the home telematics and determinations (See paragraph 0072). Further training while in operation with incident data (see paragraph 0079). Further teaches updating training based on previous determinations (see paragraph 0087). Further paragraph 0123 teaches a user may interact with an interface to provide an indication that the system should use or discard the data as retraining data for the model determining whether irregular activity occurs in that this behavior is expected)).
As per claim 12, Harvey et al. teaches
wherein the first utility commodity is water (see paragraphs 0007, 0013, Examiner’s note: water sensors).
and the plurality of utility commodities includes at least one of gas or electricity. (see paragraphs 0013 and 0037, Examiner’s note: electricity sensors, examiner notes that only one of the alternatives is required by the claims).
As per claim 13, Harvey et al. teaches
wherein the operations further comprise: receiving one or more detection parameters; and detecting that the premises is not occupied further based on the detection parameters. (see paragraphs 0018, 0096-0097, and 0109, Examiner’s note: teaches using a machine learning model to determine whether a home is occupied).
As per claim 14,Harvey et al. teaches
wherein the operations further comprise: receiving one or more detection parameters; and detecting that the usage of the first utility commodity is atypical further based on the detection parameters. (see paragraphs 0072-0080, Examiner’s note: here shows numerous examples of how machine learning is used to determine particular data for example a pipe is leaking or a level of risk from, data of the home telematic data).
As per claim 15, Harvey et al. teaches
wherein detecting that usage of the first utility commodity is atypical comprises determining that a pattern of usage of the first utility commodity during a time when the first utility commodity is used diverges from an expected pattern of consumption of the first utility commodity. (see paragraphs 0091, 0100, and 0115, Examiner’s note: teaches using multiple utility sensors to determine an abnormal condition exists, where an expected pattern of usage could be merely when the system doesn’t generate an alert like the pipe doesn’t contain water but when it does and the house is below a certain temperature providing an alert, as broadly recited in the claims. There is no recitation here how the expected pattern of consumption is determined this could be merely an assumption that there is no water present and when there is water generating an alert based on other detected or determined factors).
As per claim 16, Harvey teaches
wherein the responsive action comprises notifying a user associated with the premises that unexpected consumption of the first utility commodity is occurring while the premises is unoccupied. (see paragraphs 0014-0015, 0021-0022, 0035-0036, 0040, and 0092-0093, Examiner’s note: here shows numerous ways the system generates alerts to users based on determinations).
As per claim 17, Harvey teaches A network device, comprising: (see paragraphs 0010-011, Examiner’s note: non transitory computer readable medium coupled to processors to execute instructions).
one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to: (see paragraphs 0010-011, Examiner’s note: non transitory computer readable medium coupled to processors to execute instructions).
receive metrology data indicating consumption of a plurality of utility commodities at a property from a second network device and a third network device; determine with a machine learning model associated with the network device, the property is not occupied based on the metrology data; (see paragraphs 0010-00111, 0013, 0018, 0024, 0079, 0097, and 0109, Examiner’s note: determining that the property is not occupied where the system and method in Harvey et al are performed by software (instructions) executed by a computer (see paragraphs 0010-00111, 0013, 0018, 0024, 0097, and 0109). Further Harvey et al. occupancy is determined by machine learning (see paragraph 0079)).
After determining that the property is not occupied, determine , with the machine learning model, that undesired consumption of a first utility commodity is occurring based on both a first portion of the metrology data including that consumption of first utility commodity included in the plurality of utility commodities is above a first threshold during a first time period and a second portion of the metrology data indicating that consumption of a second utility commodity included in the plurality of utility commodities is below a second threshold during the first time period, wherein the second utility commodity is different from the first utility commodity; (paragraphs 0020, 0039, 0088, and 0100, Examiner’s note: teaches making a determination based on being below a threshold (temperature) and the pipes likely contain water, and other thresholds like location and size (see paragraph 0100). It is noted here that the Examiner interprets “likely to contain” water to be above a threshold as broadly recited herein, whereas something like unlikely would be below a threshold. Paragraph 0088 discusses how water flow is sensed and paragraph 0020 discuss use of water sensors. Teaches using machine learning to determine when irregularities occur (see paragraph 0039)).
and in response to determining that the location is not occupied and that consumption of the first utility commodity is occurring, performing, by the network device, a responsive action (see paragraphs 0093, 0105, 0113, and 0115-0116, Examiner’s note: performing operations or actions in response to determining a utility is atypical like leaking or below a temperature).
That comprises at least one of sending a message to a first computing device associated with the property or causing a supply of the first utility commodity to the property to be shut off (see paragraphs 0014, 0035-0036, 0103, 0116, and 0131, Examiner’s note: teaches shutting off a value (see paragraphs 0103, 0116) further teaches presenting an action command to a user (see paragraph 0014, 0035-0036, 0131). It is noted additionally that only one of the alternatives is actually required by the claim).
Receiving, by the network device, a first user feedback response to the responsive action; and training, by the network device, the machine learning model to generate an updated machine learning model based on a first training data set that includes the first user feedback response, the first portion of the metrology data, and the second portion of the metrology data (see paragraph 0072, 0079, 0087, and 0123, Examiner’s note: training the model based on the home telematics and determinations (See paragraph 0072). Further training while in operation with incident data (see paragraph 0079). Further teaches updating training based on previous determinations (see paragraph 0087). Further paragraph 0123 teaches a user may interact with an interface to provide an indication that the system should use or discard the data as retraining data for the model determining whether irregular activity occurs in that this behavior is expected or an irregularity)).
As per claim 18, Harvey teaches
wherein the first utility commodity is water (see paragraphs 0007, 0013, Examiner’s note: water sensors).
and the plurality of utility commodities includes at least one of gas or electricity. (see paragraphs 0013 and 0037, Examiner’s note: electricity sensors, examiner notes that only one of the alternatives is required by the claims).
As per claim 19, Harvey teaches
wherein: the second network device is a metering device configured to monitor consumption of the first utility commodity; and the third network device is a metering device configured to monitor consumption of the second utility commodity of the plurality of utility commodities. (see paragraphs 0020, 0043-0044, and 0047, Examiner’s note: sensors monitoring temperatures (see paragraphs 0043-0044) and sensors monitoring water (see paragraphs 0020 and 0047)).
As per claim 20, Harvey teaches
wherein the network device is a metering device configured to monitor consumption of a third utility commodity of the plurality of utility commodities, a home automation system, or a server of a utility service provider. (see paragraphs 0013, 0020, 0042-0043, 0047, and 0095-0096 Examiner’s note: teaches many different types of metering devices, that are connected via a network. Further it is noted that only one of the alternatives is required by the claims).
As per claim 21, Harvey teaches
Wherein the first training data set further includes first accuracy information indicating whether the determination that the location is not occupied is accurate, the first user feedback responses includes second accuracy information indicating whether the determination that the consumption of the first utility commodity is atypical is accurate, and training the machine learning model comprises modifying at least one parameter or threshold included in the machine learning model based on the first accuracy information and the second accuracy information (see paragraph 0079, 0087, and 0123, Examiner’s note: Further teaches updating training based on previous determinations (see paragraph 0087). Further paragraph 0123 teaches a user may interact with an interface to provide an indication that the system should use or discard the data as retraining data for the model determining whether irregular activity occurs in that this behavior is expected or an irregularity)).
As per claim 22, Harvey teaches
Wherein the machine learning model is previously trained based on a plurality of determinations whether consumption of the first utility commodity is atypical and a plurality of training labels indicating whether each of the plurality of determinations is accurate (see paragraph 0079, 0087, and 0123, Examiner’s note: Further teaches updating training based on previous determinations (see paragraph 0087). Further paragraph 0123 teaches a user may interact with an interface to provide an indication that the system should use or discard the data as retraining data for the model determining whether irregular activity occurs in that this behavior is expected or an irregularity)).
Claim Rejections - 35 USC § 103
15. 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.
16. Claim 7 is rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Harvey et al. (United States Patent Application Publication Number: US 2024/0142065) further in view of Correnti (United States Patent Application Publication Number: US 2018/0350219).
As per claim 7, Harvey et al. teaches
wherein determining that the location is not occupied comprises comparing an amount of the consumption of a second utility commodity of the plurality of utility commodities (see paragraphs 0097, Examiner’s note: determining occupancy status based on power use).
Harvey does not expressly expressly teach to determine if a location is not occupied by comparing utility consumption during a time period to a detection threshold for the second utility commodity.
Correnti which is in the art of occupancy for property simulation (see abstract) teaches determine if a location is not occupied by comparing utility consumption during a time period to a detection threshold for the second utility commodity (see paragraphs 0005, 0008-0009, 0029, 0059-0062, and claim 5, Examiner’s note: teaches determining occupancy based on comparing sensor information to a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Harvey with the aforementioned teachings from Correnti with the motivation of making an occupancy determination by comparing determined information to a threshold (see Corrent paragraphs 0005, 0008-0009, 0059-0062), when comparing information to determine an occupancy status (see Harvey paragraphs 0097 and 0109) and determining information to a threshold to make a determination (see Harvey paragraphs 0098-0100 and 0024) are both known.
Conclusion
17. 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.
18. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bowman (United States Patent Application Publication Number: US 2011/0178937) teaches determining unexpected utility usage based on different
sensors and thresholds (see abstract and Figure 2)
Klicpera (United States Patent Application Publication Number: US 2019/0234786) teaches a water meter leak detection (see abstract)
Schlucz et al. (United States Patent Number: US 10847009) teaches remote monitoring of a house to determine an action (see Figure 1)
`Clark (United States Patent Application Publication Number: US 2022/0128428) teaches determining a gas leak based on other information like the house being unoccupied, and other sensors like power being on or off, temperature motion etc. (See paragraphs 0115-0130) and then the system provides one or more
monitoring actions (See Figure 2)
Enev et al. (United States Patent Application Publication Number: US 2017/0131174) teaches updating a machine learning model or a detection threshold based on a response to the notification. (see paragraphs 0082-0083, 0085, 0099, 0137, Examiner’s note: here teaches the machine learning learns based on feedback from a user regarding a water leak).
Rudd et al. (United States Patent Application Publication Number: US 2020/0393324) which is in the art of determining a leak and machine learning teaches retraining the machine learning based on responses, e.g. that the predicted leak is not happening (See paragraph 0051)
19. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIERSTEN SUMMERS whose telephone number is (571)272-6542. The examiner can normally be reached Monday - Friday 7am-3:30pm.
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626