DETAILED ACTION
This office action is responsive to the above identified application filed 3/27/2023. The application contains claims 1-20, all examined and rejected.
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 .
Information Disclosure Statement
The Information Disclosure Statement with references submitted 3/27/2023, 2/23/2024, 6/12/2024, 7/5/2024, 7/30/2024, 8/14/2024, 8/19/2024, 8/28/2024, 9/4/2024, 10/3/2024, 10/15/2024, 10/24/2024, 11/15/2024, 11/22/2024, 12/3/2024, 12/23/2024, 2/18/2025, 7/11/2025, 8/5/2025, and 10/21/2025 have been considered and entered into the file.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 9 and 17 are each directed to a statutory category, it recites a series of steps that appears to be directed to an abstract idea (mental process).
Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 9, and 15, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“identifying, based on the request, a qualification for the diagnostic data”, “processing sensed data obtained by the unmanaged device to obtain qualified sensed data, the sensed data being processed”, “based on the qualification; processing the qualified sensed data to obtain the diagnostic data, the qualified sensed data”, “the diagnostic data being rated for at least one of the diagnostic purposes” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“obtaining a request for diagnostic data from an unmanaged device” (insignificant extra-solution activity, MPEP 2106.05(g))
“using a first inference model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h))
“using a second inference model that discriminates for diagnostic purposes” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h));
“storing the diagnostic data in a data management system” (insignificant extra-solution activity, MPEP 2106.05(g));
“servicing data access requests for the at least one of the diagnostic purposes using the diagnostic data” (insignificant extra-solution activity, MPEP 2106.05(g)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“obtaining a request for diagnostic data from an unmanaged device” (Well Understood, Routine, Conventional Activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“using a first inference model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
“using a second inference model that discriminates for diagnostic purposes” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
“storing the diagnostic data in a data management system” (Well Understood, Routine, Conventional Activity, Storing and retrieving information in memory MPEP 2106.05(d)(II)(iv)).
“servicing data access requests for the at least one of the diagnostic purposes using the diagnostic data” (Well Understood, Routine, Conventional Activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 9 recites “A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations” configured to perform the same method as set forth in claim 1, the added element of “A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor to execute instruction stored on a non-transitory machine readable medium is not amount to a mere instruction to apply the judicial exception to a computer.
Claim 9 is therefore rejected according to the same findings and rationale as provided above.
Claim 17 recites a data processing system comprising “a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations” configured to perform the same method as set forth in claim 1, the added element of “at least one processor” and “at least one memory coupled to the processor” do not transform the judicial exception into a practical application because they are mount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with a processor and a memory is a mere instruction to apply the judicial exception to a computer.
Claim 17 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 9 and 17 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “first inference model is hosted by the unmanaged device, and the second inference model is hosted by the data management system” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 3 disclose “the sensed data is generated by a sensor of the unmanaged device, and the first inference model is adapted to discriminate portions of the sensed data that do not meet the qualification” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 4 disclose “wherein the qualification specifies a performance requirement for the sensor” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 5 disclose “the sensed data is generated by a sensor of the unmanaged device, the first inference model is adapted to generate derived data based on the sensed data, and the sensed data is unrecoverable from the derived data” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 6 disclose “wherein the second inference model is adapted to identifying diagnostic uses for portions of the qualified sensed data” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 7 disclose “wherein the second inference model comprises sub-models, and each of the sub-models is adapted to identify whether the portions of the qualified sensed data are usable for a corresponding diagnostic use of the diagnostic uses” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 8 disclose “processing the qualified sensed data comprises: dividing the qualified sensed data into the portions of the qualified sensed data” (Mental process, observation, evaluation and judgment); and “adding the qualified sensed data to processing queues for the sub-models” (insignificant extra-solution activity, MPEP 2106.05(g) that is Well Understood, Routine, Conventional Activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)).
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 10-16, 18-20,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6, 9-12, 14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis [US 2022/0059216 A1] in view of “Machine Learning-Based Time-Series Data Analysis in Edge-Cloud-Assisted Oil Industrial IoT System” published 1/2022 [hereinafter D1].
With regard to Claim 1,
Lewis teach a method for managing data collection for managed devices and unmanaged devices (¶18, “The management console or the system might then extend the healthcare system network to each customer local area network in a customer premises of the patient”), the method comprising:
obtaining a request for diagnostic data from an unmanaged device (¶64, “a healthcare data system(s) 165 associated with a healthcare provider 170 (e.g., a doctor, or the like) might subscribe to data from each of the one or more patient devices 125 associated with (or registered to) the patient 130”, ¶33, “sending the first patient data over the first network transport link to the at least one healthcare data system might comprise the first data collector publishing the first patient data via the computing system, and wherein the at least one healthcare data system subscribes to the first patient data”, subscription is a request. When the healthcare system subscribe to patients device data, it is obtaining a request for diagnostic data from an unmanaged device (patient’s health care device that are not on the healthcare network), ¶59, “ healthcare provider 170 (e.g., a doctor, or the like) might hand out, assign, or prescribe one or more first patient devices 125 (and perhaps a data collector 115 as well) to the patient 130”, prescribing devices and registering them constitute establishing a request for ongoing diagnostic data collection from unmanaged devices);
identifying, based on the request, a qualification for the diagnostic data (¶22, “the management console might provide patient device data to the at least one of the computing system or the data collector over the second network transport link, the patient device data comprising a list of authorized patient devices among the one or more patient devices that are associated with and assigned to the patient”, ¶63, “ the computing system 105 or the data collector 115 might prevent collection of data, or prevent communication of data (to the computing system 105 and/or to the data collector 115), from devices that are not listed in the patient device data as being authorized, based on subscription or registration system (request) system identify qualification for the diagnostic data: only data from authorized registered patient devices qualifies. The authorized device list is the qualification defining which devices data meets the criteria for acceptance into the system. Data from unauthorized or unregistered devices is rejected. qualification is based on request because it is established during device subscription or registration process);
processing sensed data obtained by the unmanaged device to obtain qualified sensed data, the sensed data being processed based on the qualification (¶63, “ the computing system 105 or the data collector 115 might prevent collection of data, or prevent communication of data (to the computing system 105 and/or to the data collector 115), from devices that are not listed in the patient device data as being authorized” data from unauthorized devices is filtered out as a first processing step before further processing);
storing the diagnostic data in a data management system (¶21, “The management console might store the first patient data in the at least one healthcare data system”, ¶70, “healthcare data system might be a large data store or analytical database. Data may be replicated from the data collector directly into the data store”); and
servicing data access requests for the at least one of the diagnostic purposes using the diagnostic data (¶64, “ healthcare provider 170 can remotely monitor the health of the patient 130, and the one or more patient devices 125 can alert the healthcare provider 170 if sensors on the one or more patient devices 125 are triggered to send an alert message when the patient's physiological condition worsens or when the patient suffers an injury, stroke, heart attack, or other serious condition”, “continuous monitor or as an event-based monitor“).
Lewis does not explicitly teach using a first inference model; and processing the qualified sensed data to obtain the diagnostic data, the qualified sensed data being processed using a second inference model that discriminates for diagnostic purposes, and the diagnostic data being rated for at least one of the diagnostic purposes.
D1 teach identifying, based on the request, a qualification for the diagnostic data (P. 5, Col. 2, ¶2, “We use the anomaly scores s(x,ψ) to assess whether a data point is anomalous. If instances return values of s(x,ψ) very close to unity, then they are anomalies”, anomaly score threshold is qualification criteria);
data being processed using a first inference model based on the qualification (P. 3, Col. 1-2, “two machine learning algorithms for time-series data anomaly analysis were deployed at the edge layer”, P. 4, “4.1. RRCF Method. RRCF is an unsupervised method used for the detection of anomalies in dynamic data streams, which is efficient in processing vast amounts of data streams and suitable for high-dimensional data”, P.5, Col. 1, “4.2. Isolation Forest Method. (e isolation forest algorithm randomly samples the dataset and constructs a random binary tree” , both algorithms process raw data and produce anomaly scores that qualify or disqualify data points);
processing the qualified sensed data to obtain the diagnostic data, the qualified sensed data being processed using a second inference model that discriminates for diagnostic purposes, and the diagnostic data being rated for at least one of the diagnostic purposes (P. 7, Col. 2, 5.1, “we use an extensively applied LSTM to perform time-series data analysis, which can both predict trends and fill in missing data”, P. 10, Table 3, LSTM second inference model used to diagnose data for diagnostic purposes (predict trends and fill in missing data). These are diagnostic purposes as it diagnose the health and operational of oil production system using accuracy metrics (Table 3) to rate diagnostic data for fitness).
Lewis and D1 are analogous art to the claimed invention because they are from a similar field of endeavor of processing sensor data using machine learning models for analysis and monitoring. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Lewis resulting in resolutions as disclosed by D1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Lewis as described above to solve the problem of inefficient due to latency and limited cloud resources transfer of data to remote servers due to the vast amount of time series data by performing machine learning analysis at the edge before transmitting data which improve the responsive and efficiency of sensor data analysis (D1, Introduction, “(The voluminous time-series data that which are generated. continuously pose a significant challenge to IIoT for efficient data processing and analysis. Analyzing and processing all the raw data in the cloud server remotely is impractical and has very low efficiency owing to the limited network latency and cloud computing resources”, “Edge computing and cloud computing provide an efficient alternative to tackle the aforementioned problems. Edge computing is a new computing pattern that offloads computation and storage capacity from the cloud to the edge and only interacts with the cloud off the critical path”).
With regard to Claim 2,
Lewis-D1 teach the method of claim 1, wherein the first inference model is hosted by the unmanaged device (D1, Abstract, “robust random cut forest and isolation forest algorithms are employed at the edge gateway to the collected data for the detection of anomalously changing data”, P. 3, Col. 1, “Edge computing is a new computing paradigm that offloads computing and storage resources from the cloud to the edge, which is close to the data source, enabling sensor access management and data preprocessing at the edge “, P. 3, Col. 2, “An edge computing node carries device access and data preprocessing functions. It usually refers to a small device with computing and storage capabilities”), and the second inference model is hosted by the data management system (D1, Abstract, “Subsequently, these preprocessed time series data are transmitted to cloud services for data trend prediction and missing data completion using the long short-term memory recurrent neural network method”, P. 3, Col. 2, “LSTM recurrent neural network algorithm was embedded in the cloud computing layer for future trend prediction”, P. 5, Col. 2, ¶2, “We use the anomaly scores s(x,ψ) to assess whether a data point is anomalous. If instances return values of s(x,ψ) very close to unity, then they are anomalies”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 3,
Lewis-D1 teach the method of claim 2, wherein the sensed data is generated by a sensor of the unmanaged device, and the first inference model is adapted to discriminate portions of the sensed data that do not meet the qualification (Lewis, ¶49, “one or more health monitoring devices 125 a, the one or more personal tracking devices 125 d, and/or the one or more other patient devices 125 i might include, without limitation, at least one of a heart rate monitor, a pulse oximeter, an oximeter, a blood glucose monitor …”, ¶52, D1, P. 6, Col. 1-2, “the isolation forest algorithm can monitor a higher number of anomalous data values”, P. 5, Col. 2, ¶2, “We use the anomaly scores s(x,ψ) to assess whether a data point is anomalous. If instances return values of s(x,ψ) very close to unity, then they are anomalies”, P. 3, Col. 1-2, “two machine learning algorithms for time-series data anomaly analysis were deployed at the edge layer”, P. 4, “4.1. RRCF Method. RRCF is an unsupervised method used for the detection of anomalies in dynamic data streams, which is efficient in processing vast amounts of data streams and suitable for high-dimensional data”, P.5, Col. 1, “4.2. Isolation Forest Method. (e isolation forest algorithm randomly samples the dataset and constructs a random binary tree” , both algorithms process raw data and produce anomaly scores that qualify or disqualify data points). The same motivation to combine for claim 2 equally applies for current claim.
With regard to Claim 4,
Lewis-D1 teach the method of claim 3, wherein the qualification specifies a performance requirement for the sensor (D1, P. 6, Col. 1-2, “the isolation forest algorithm can monitor a higher number of anomalous data values”, P. 5, Col. 2, ¶2, “We use the anomaly scores s(x,ψ) to assess whether a data point is anomalous. If instances return values of s(x,ψ) very close to unity, then they are anomalies”, P. 3, Col. 1-2, “two machine learning algorithms for time-series data anomaly analysis were deployed at the edge layer”, P. 4, “4.1. RRCF Method. RRCF is an unsupervised method used for the detection of anomalies in dynamic data streams, which is efficient in processing vast amounts of data streams and suitable for high-dimensional data”, P.5, Col. 1, “4.2. Isolation Forest Method. (e isolation forest algorithm randomly samples the dataset and constructs a random binary tree”, anomaly scores is qualification criterion that specifies the performance requirement for the sensor. Sensor data must satisfy the anomaly score threshold to be considered normal and acceptable for further processing). The same motivation to combine for claim 3 equally applies for current claim.
With regard to Claim 6,
Lewis-D1 teach the method of claim 2, wherein the second inference model is adapted to identifying diagnostic uses for portions of the qualified sensed data (Lewis, ¶64, “continuous monitor or as an event-based monitor (e.g., in response to sensors detecting when the patient's physiological condition worsens or when the patient suffers an injury, stroke, heart attack, or other serious condition, or the like), one or more patient devices 125 might publish sensor data associated with the health condition of the patient “, P. 7, Col. 2, 5.1, “we use an extensively applied LSTM to perform time-series data analysis, which can both predict trends and fill in missing data”, P. 10, Table 3, LSTM second inference model used to diagnose data for diagnostic purposes (predict trends and fill in missing data). These are diagnostic purposes as they diagnose the health and operational of oil production system using accuracy metrics (table 3) to rate diagnostic data for fitness). The same motivation to combine for claim 2 equally applies for current claim.
With regard to Claim 9,
Claim 9 is similar in scope to claim 1 therefore it is rejected under similar rationale. Lewis further teach a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations (¶38, “computing system might comprise at least one first processor and a first non-transitory computer readable medium communicatively coupled to the at least one first processor. The first non-transitory computer readable medium might have stored thereon computer software comprising a first set of instructions that, when executed by the at least one first processor”, claim 17).
With regard to Claim 10,
Claim 10 is similar in scope to claim 2 therefore it is rejected under similar rationale.
With regard to Claim 11,
Claim 11 is similar in scope to claim 3 therefore it is rejected under similar rationale. With regard to Claim 12,
Claim 12 is similar in scope to claim 4 therefore it is rejected under similar rationale.
With regard to Claim 14,
Claim 14 is similar in scope to claim 6 therefore it is rejected under similar rationale.
With regard to Claim 17,
Claim 17 is similar in scope to claim 1 therefore it is rejected under similar rationale. Lewis further teach data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations (¶38, “computing system might comprise at least one first processor and a first non-transitory computer readable medium communicatively coupled to the at least one first processor. The first non-transitory computer readable medium might have stored thereon computer software comprising a first set of instructions that, when executed by the at least one first processor”, ¶107, “computer or hardware system 500 also may comprise software elements, shown as being currently located within the working memory 535, including an operating system 540, device drivers, executable libraries, and/or other code, such as one or more application programs”, claim 17).
With regard to Claim 18,
Claim 10 is similar in scope to claim 2 therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 10 is similar in scope to claim 3 therefore it is rejected under similar rationale.
With regard to Claim 20,
Claim 10 is similar in scope to claim 4 therefore it is rejected under similar rationale.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis [US 2022/0059216 A1] in view of “Machine Learning-Based Time-Series Data Analysis in Edge-Cloud-Assisted Oil Industrial IoT System” published 1/2022 in view of Kang, James Jin, et al. "A privacy-preserving data inference framework for internet of health things networks." 2020 IEEE 19th international conference on trust, security and privacy in computing and communications (trustcom). IEEE, 2020 [hereinafter D2].
With regard to Claim 5,
Lewis-D1 teach the method of claim 2, wherein the sensed data is generated by a sensor of the unmanaged device (Lewis, ¶49, “one or more health monitoring devices 125 a, the one or more personal tracking devices 125 d, and/or the one or more other patient devices 125 i might include, without limitation, at least one of a heart rate monitor, a pulse oximeter, an oximeter, a blood glucose monitor …”), the first inference model is adapted to generate derived data based on the sensed data (D1, P. 3, Col. 1-2, “two machine learning algorithms for time-series data anomaly analysis were deployed at the edge layer”, P. 4, “4.1. RRCF Method. RRCF is an unsupervised method used for the detection of anomalies in dynamic data streams, which is efficient in processing vast amounts of data streams and suitable for high-dimensional data”, P.5, Col. 1, “4.2. Isolation Forest Method. (e isolation forest algorithm randomly samples the dataset and constructs a random binary tree”), and
Lewis-D1 does not explicitly teach sensed data is unrecoverable from the derived data.
D2 teach sensed data is generated by a sensor of the unmanaged device (P. 1, Introduction, “wireless body area networks (WBAN), which consist of sensors and smartphones. These devices, such as physiological sensors and monitoring devices”, P. 1, Col. 1, ¶1, “sensors interacting with IoHT devices”, P.2, II, “IoHT could include any health devices attached on or within a user’s body, and is battery driven with the ability to sense certain health or physiological data”), the first inference model is adapted to generate derived data based on the sensed data (Abstract, “this paper proposes a privacy-preserving two-tier data inference framework”, P. 1, Col. 2, ¶2, “The first tier infers data processing of sensors to reduce transactions from sensors to smartphones and IoHT networks”, P. 2, “A. The first tier: data reduction using a data inference algorithm”, “it is proposed to analyze the differences between the original and inferred data”, P. 3, Col. 2, ¶1, “The sampled data will then be encrypted”, P. 1, Col. 2, ¶2, “sensor data is first reduced”), and the sensed data is unrecoverable from the derived data (P. 1, Col. 2, ¶2, “less sensitive information is transferred and encrypted”, “sensor data is first reduced, and the encrypted sensor data is then transmitted to the edge servers”, Abstract, “this can … protect the sensitive data from leakage to adversaries”, P. 2, Col. 2, ¶2, “Privacy preservation is achieved at the edge servers deployed in each home (the second tier)”, “The two-tier approach is created specifically for IoHT applications where privacy in the underlying sensor data is protected by a privacy-preserving workflow”, P. 3, Col. 2, ¶1, “what extent that inferred data are different from original data. The sampled data will then be encrypted using symmetric key encryption (SKE), or attribute based encryption (ABE)”, P. 3, Col. 2, ¶2, “Differential privacy is a technique that ensures protection against attackers to infer private information”, P. 5, Col. 2, ¶4, “The purpose of adding Laplace Noise is to ensure the availability of data while protecting user privacy”).
Lewis-D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of collecting data for analysis and monitoring. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Lewis-D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Lewis-D1 as described above to ensure the availability of data while protecting user privacy (D2, Abstract, “this can … protect the sensitive data from leakage to adversaries”, P. 3, Col. 2, ¶2, “Differential privacy is a technique that ensures protection against attackers to infer private information”, P. 5, Col. 2, ¶4, “The purpose of adding Laplace Noise is to ensure the availability of data while protecting user privacy”).
With regard to Claim 13,
Claim 13 is similar in scope to claim 5 therefore it is rejected under similar rationale.
Claims 7-8 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis [US 2022/0059216 A1] in view of “Machine Learning-Based Time-Series Data Analysis in Edge-Cloud-Assisted Oil Industrial IoT System” published 1/2022 in view of Lie et al. [US 2020/0293830 A1, Liu].
With regard to Claim 7,
Lewis-D1 teach the method of claim 6, wherein the second inference model adapted to identify whether the portions of the qualified sensed data are usable for a corresponding diagnostic use of the diagnostic uses (Lewis, ¶64, “continuous monitor or as an event-based monitor (e.g., in response to sensors detecting when the patient's physiological condition worsens or when the patient suffers an injury, stroke, heart attack, or other serious condition, or the like), one or more patient devices 125 might publish sensor data associated with the health condition of the patient “, D1, P. 7, Col. 2, 5.1, “we use an extensively applied LSTM to perform time-series data analysis, which can both predict trends and fill in missing data”, P. 10, Table 3, LSTM second inference model used to diagnose data for diagnostic purposes (predict trends and fill in missing data). These are diagnostic purposes as they diagnose the health and operational of oil production system using accuracy metrics (table 3) to rate diagnostic data for fitness).
Lewis-D1 does not explicitly disclose that the second inference model comprises sub-models, and each of the sub-models is adapted to task.
Liu teach second inference model comprises sub-models, and each of the sub-models is adapted to task (Fig. 4-5, ¶5, “ detection model includes a first sub-model and a second sub-model, the first sub-model identifies respective features of each image, a feature processing result of each image is input to the second sub-model, the second sub-model performs time series analysis on the feature processing result to determine the damage detection result “, ¶20, “first sub-model can be any machine learning model, and an advantageous result usually can be achieved by using an algorithm that is suitable for feature extraction and processing, for example, a deep convolutional neural network (DCNN). The second sub-model can be any machine learning model that can perform time series analysis, for example, a recurrent neural network (RNN), a long short-term memory (LSTM) network, etc. When the second sub-model is the LSTM network, a more accurate damage detection result can be determined if the LSTM network also employs an attention mechanism”, ¶64, “deep convolutional neural network sub-model inputs the damage classification result of the single image to the LSTM sub-model in time order. The LSTM sub-model performs time series analysis on damage classification results“).
Lewis-D1 and Liu are analogous art to the claimed invention because they are from a similar field of endeavor of processing sensor data using machine learning models for analysis and monitoring. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Lewis-D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Lewis-D1 as described above to improves the accuracy and completeness of diagnostic results by combining multiple sub-model, such as a convolutional network for feature extraction and an LSTM model for time series analysis (Liu, ¶10, “damage detection accuracy can be greatly improved in the implementations of the present specification”).
With regard to Claim 8,
Lewis-D1-Liu teach the method of claim 7, wherein processing the qualified sensed data comprises:
dividing the qualified sensed data into the portions of the qualified sensed data (D1, P. 3, Col. 1-2, “two machine learning algorithms for time-series data anomaly analysis were deployed at the edge layer”, P. 7, Col. 1, “detecting a slice of data anomaly at the edge “, P. 8, 5.2, ¶2,” first length of 50% of a slice of time-series data, approximately 3200 two dimensional data for model training, the middle 25% of the data for testing, and the latter 25% slice for validation)”; and
adding the qualified sensed data to processing queues for the sub-models (Liu, Fig. 4-5, ¶5, “ detection model includes a first sub-model and a second sub-model, the first sub-model identifies respective features of each image, a feature processing result of each image is input to the second sub-model, the second sub-model performs time series analysis on the feature processing result to determine the damage detection result “, ¶20, “first sub-model can be any machine learning model, and an advantageous result usually can be achieved by using an algorithm that is suitable for feature extraction and processing, for example, a deep convolutional neural network (DCNN). The second sub-model can be any machine learning model that can perform time series analysis, for example, a recurrent neural network (RNN), a long short-term memory (LSTM) network, etc. When the second sub-model is the LSTM network, a more accurate damage detection result can be determined if the LSTM network also employs an attention mechanism”, ¶64, “deep convolutional neural network sub-model inputs the damage classification result of the single image to the LSTM sub-model in time order. The LSTM sub-model performs time series analysis on damage classification results“). The same motivation to combine for claim 7 equally applies for current claim.
With regard to Claim 15,
Claim 15 is similar in scope to claim 7 therefore it is rejected under similar rationale.
With regard to Claim 16,
Claim 16 is similar in scope to claim 8 therefore it is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
“Hybrid cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks” that disclose first edge side model to filter sensor data and second cloud side model to perform analysis See at least Abstract, “mainly consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud”.
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148