Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
IDS
The information disclosure statement (IDS) submitted on June 14, 2023 is being considered by the Examiner.
Drawing
The drawing filed on June 14, 2023 is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. §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.
In reference to claim 1-20: the instant claims are rejected under 35 U.S.C. §101 because the claimed invention is directed to judicial exception (i.e., abstract idea) without significantly more.
The requirement for subject matter eligibility test for products and processes
requires first, the claimed invention must be to one of the four statutory categories. 35 U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. The latter three categories define "things" or "products" while the first category defines "actions" (i.e., inventions that consist of a series of steps or acts to be performed).
Second, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. The judicial exceptions (also called "judicially recognized exceptions" or simply "exceptions") are subject matter that the courts have found to be outside of, or exceptions to, the four statutory categories of invention, and are limited to abstract ideas, laws of nature and natural phenomena (including products of nature).
In the first step, it is to be determined whether the patent claim under examination is directed to an abstract idea. If so, in the second step of analysis, it is to
be determined whether the patent adds to the idea "something more" or "significantly more" that embodies an "inventive concept."
In the instant case, claim 1 is representative and it is reproduced here with the limitations that are part of the abstract idea in bold:
Claim 1: A computer-implemented method comprising:
receiving a plurality of sensor data streams from a plurality of sensors;
identifying missing sensor data in a sensor data stream among the plurality of sensor data streams; and
predicting a value of the missing sensor data by running a machine learning model trained using sensor data determined based on at least one of a plurality of co-existence probabilities of the plurality of sensor data streams and a plurality of co-prediction accuracies of the plurality of sensor data streams.
Step 2A:
Prong I: The claim recites the steps of "sensor data streams from a plurality of sensors; identifying missing sensor data in a sensor data stream among the plurality of sensor data streams ", and " predicting a value of the missing sensor data …, based on at least one of a plurality of co-existence probabilities of the plurality of sensor data streams and a plurality of co-prediction accuracies of the plurality of sensor data streams." These limitations could be carried out as a purely mental process (at least in a some relatively simple situations) and/or they could amount to a mathematical calculation (for example, calculating co-existence probability). Therefore, the recited method falls in the abstract idea grouping of mental processes and/or mathematical concepts at Prong 1 of the §101 analysis.
Prong II:
This abstract idea is not integrated into a practical application at Prong 2 of the
§101 analysis because the claim does not recite sufficient additional elements to integrate the abstract idea into a practical application. The claim recites the method comprising the additional element steps of “a plurality of sensors” and " predicting a value of the missing sensor data by running a machine learning model trained using sensor data ". However, the “a plurality of sensors” and “machine learning” in the context of the instant application are considered generic sensors and ML. The sensors are generally considered generic hardware elements to gather data or attributes. The ML is generic because applying a standard algorithm to a new data without creating a specific non-conventional improvement fails to meet patentability and is considered a mathematical concept or mental process. Further, the machine learning (ML) is invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea (see MPEP 2106.05(b)).
The courts have found that adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea (such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)) is not enough to integrate the abstract idea into a particular practical application or make the claim qualify as "significantly more" (see MPEP § 2106.05(g)).
The claim does not recite applying the abstract idea with, or by use of, any particular machine, nor does the claim affect a real-world transformation or reduction of a particular article to a different state or thing. The claim amounts to manipulating data: predicting a value of the missing sensor data by running a machine learning model trained using sensor data. Therefore, the claimed invention does not appear to be limited to the use of the mental process or math in a particular practical application, but instead the claim appears to monopolize the mental process or math itself, in any practical application where it might conceivably be used.
Step 2B:
Finally, at Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea for the same reasons as discussed above with regard to Prong 2. Claim 1 is rejected as ineligible under 35 USC
§101.
Claims 9 and 16 are analogous to claim 1 of the method, except that claim 9: being directed to a system having “a memory” and “a processor” and claim 16 includes a software or some kind of program with a memory analysis. Reciting the processors, a computer program and the computer-readable storage medium or a memory are additional elements separate from the abstract idea that need to be considered at Prong 2 of the §101 analysis. However, these additional elements are merely generic computer processing components that are invoked as a tool to perform the abstract idea, which do not cause the claims as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Claims 6 and 16 are therefore rejected as ineligible under 35 USC §101 as well.
Dependent claims 2, 10, and 17: the instant claims are generally directed to a computational analysis of tracking a co-existence event and are considered a mathematical concept or mental process.
Dependent claims 3, 5, 6, 11, 12, 15, 18 and 19: the instant claims are directed to determining correlations between every pair of the stored historical sensor data and updating the plurality of co-prediction accuracies based on the determined correlations and would be considered a mathematical concept or mental process
Dependent claim 4: the instant claim is related to the definition of Pearson correlation type and is considered a mathematical concept or mental process.
Dependent claim 13: the instant claim is directed to training a plurality of proxy sensor models using sensor data from the set of sensors; and considered a mathematical concept or mental process.
Dependent clams 7, 8, 14 and 20: the instant claims are generally directed to retraining the plurality of proxy sensor models periodically at a first- and second-time intervals where the second time interval is greater than the first-time interval, and further re-training the plurality of proxy sensor models periodically using the plurality of co-existence probabilities and the plurality of co-prediction accuracies; where it would be considered a mathematical concept or mental process.
In reference to claims 16-20: the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the instant claim 16 is directed to “a computer program product comprising a computer readable storage medium having a program instruction embodied therewith…”, Further, the broadest reasonable interpretation (BRI) covers both statutory and non-statutory embodiments, and the BRI of “computer-readable storage medium” can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se.
See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. §101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves).
The remaining claims 17-20 depend on claim 16 of the instant application and include similar issues and/or inherit the attributes of claim 16.
Art of Interest
In reference to claims 1-20: Wu et al. (IEEE Publication, “Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data”, hereon Wu) discloses a computer-implemented method (see Wu, page 10671, Abstract and Introduction) comprising: receiving a plurality of sensor data streams from a plurality of sensors (see Wu, page 10,678, experiment results, second column, array of in situ sensors); identifying missing sensor data in a sensor data stream among the plurality of sensor data streams (see Wu, Fig. 3, sensor data with missing data); and predicting a value of the missing sensor data by running a machine learning model trained using sensor data (see Wu, page 10,677, Reshape Method 3, split data into normal and extreme values which helped machine learning models to treat data with different distribution differently).
Unlike Wu or any of the references considered, the instant application implements or uses a different prediction method, i.e., “predicting a value of the missing sensor data by running a machine learning model trained using sensor data determined based on at least one of the pluralities of co-existence probabilities of the plurality of sensor data streams and a plurality of co-prediction accuracies of the plurality of sensor data streams”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nikolas (U.S. PAP 2022/0327332) discloses a computer-implemented method for determining a classification and/or a regression result based on the plurality of sensor values. The method includes a plurality of hypotheses regarding a missing sensor value by means of a machine learning system; and a plurality of outputs.
Wang et al. (U.S. Patent No. 11,775,873) discloses a method for preprocessing time-series sensor data by filling in missing values with corresponding imputed values, including obtaining the time-series sensor data, which was gathered from sensors in a monitored system during operation of the monitored system; identifying missing values in the time-series sensor data; and filling in the missing values in the time-series sensor data with interpolated values through interpolation.
Bandyopadhyay et al. (U.S. Patent No. 11,113,337) discloses a method for imputing sensor data, in a sensor data sequence with missing data based on the semantics learning, where semantics is defined by the constraints of the sensor data features. A candidate value for imputation is determined based on sensor data of corresponding instances of time instants of the sensor data sequence using learning based on semantics.
Jin et al (IJODSN Publication, “A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment,” discloses a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN (Wireless Body Area Network) environment. The proposed algorithm jointly applies PRR (packet reception ratio) and SINR (signal to interference plus noise ratio), which are commonly used in wireless communication as a way to measure the quality of wireless connections.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIAS DESTA whose telephone number is (571)272-2214. The examiner can normally be reached M-F: 8:30 to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew M Schechter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ELIAS DESTA/
Primary Examiner, Art Unit 2857