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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 22 December 2025 has been entered.
Status of Claims
This is a non-final office action in response to the request for continued examination filed 22 December 2025. Claims 1, 6, and 11 have been amended. Claims 2-4, 7-9, 12-14 have been canceled. Claims 24-29 are newly added. Claims 1, 5-6, 10-11, and 15-29 are pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 6, and 11, and addition of new claims 24-29 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Regarding the 35 U.S.C. 101 rejection, Applicant asserts that the claims are not directed to an abstract idea, and even if they were, recite significantly more than the alleged abstract idea. Applicant asserts that the amendment herein transforms the claim from a mere data analysis method into a specific, practical application because the computer uses the generated Betti series to perform a specific function for accurate topological analysis such that the attractor improves the computer’s ability to process sparce time-series data by synthesizing new data points to improve the quality of the downstream analysis structure (the attractor). Lastly, Applicant asserts that the combination of limitations amounts to an inventive concept that is significantly more because the specifically claimed data augmentation technique is a specific technical improvement in data processing that yields tangible, practical results. Examiner respectfully disagrees.
The amended limitations for analyzing the time-series data by a persistent homology conversion on the attractor to generate a Betti series; comparing the generated Betti series to a predetermined Betti series pattern associated with a specific physical state; and in response to determining that the generated Betti series matches the predetermined Betti series pattern, generating an alert signal indicating a detection of the specific physical state, are additional abstract ideas recited as steps for processing data to determine whether to generate an alert signal. Persistent homology is a construction in mathematics (algebraic topology and homological algebra) for applied data analysis, therefore the step for analyzing the time-series data falls within the mathematical concepts grouping of abstract ideas. The step for comparing the generated Betti series to a predetermined Betti series is an observation and falls within the mental processes grouping of abstract ideas. The set for generating an alert signal upon the occurrence of a match is construed as an applying a rule upon the occurrence of a predetermined condition and falls within the mental processes grouping of abstract ideas. The claim fails recite specific functions that are performed to control the sensor generating the data for analysis or the environment related to the sensor data in a meaningful way that goes beyond the collection, analysis, and output of a result of the collection and analysis of the sensor data. Generating an alert is insignificant post-solution activity equivalent to transmitting or communicating information and cannot transform an abstract idea into patent eligible subject matter because it merely generates the data analysis output (alert signal) to a user for further decision making. See MPEP 2106.05(g). The claimed steps are analogous to those of Claim 2 of Example 40 wherein the claims are directed to mere data gathering steps that automate the comparison of data without significantly more than the recited insignificant extra solution activity and mere instructions to apply the exception using generic computer components. Therefore, the independent claims, as amended, recite an abstract idea, do not provide a practical application of the recited abstract idea, and when considered as a whole do not amount to significantly more than the recited abstract idea.
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, 5-6, 10-11, and 15-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of collecting environmental data, analyzing it, and generating an alert upon the occurrence of an event as an output, without significantly more. Independent claim 1 recites a product, independent claim 6 recites a process, and independent claim 11 recites a device for time-series data analysis. Independent claims 1, 6, and 11 recite substantially similar limitations.
Taking independent claim 1 as representative, claim 1 recites the following limitations:
reading out time-series data to be analyzed from a memory, the time-series data comprising wearable sensor data, or natural environment data that indicate temperature, humidity, or carbon dioxide concentration;
determining numerical values indicating features at respective timings having a predetermined time interval with respect to time-series data by determining measured values of a highest point and a lowest point included in the time-series data within time intervals corresponding to the respective timings and measured values of interpolation points between the highest point and the lowest point, wherein a number of the interpolation points is the same as a designated number for each of the respective timings, the measured values being projected at each timing at the predetermined time interval as the interpolation points, wherein when the number of interpolation points is larger than the designated number, the interpolation points are randomly selected to match the designated number of interpolation points and when the number of interpolation points is less than the designated number, the number of the interpolation points is interpolated to match the designated number of interpolation points, such that each timing has a same number of interpolation points;
generating an attractor related to the time-series data based on the determined numerical values;
analyzing the time-series data by persistent homology conversion on the attractor to generate a Betti series;
comparing the generated Betti series to a predetermined Betti series pattern associated with a specific physical state;
in response to determining that the generated Betti series matches the predetermined Betti series pattern, generating an alert signal indicating a detection of the specific physical state.
Under Step 1 independent claims 1, 6, and 11 recite at least one step or act, including generating an attractor. Thus the claims fall within one of the statutory categories on invention. See MPEP 2106.03.
Under Step 2A Prong One claims 1, 6, and 11 are substantially drawn to mathematical concepts, and fall within the mathematical concepts grouping of abstract ideas. The recited step for “determining numerical values” per the specification at paragraph [0022] relates to obtaining a Betti series by applying the persistent homology by data analysis using topological data analysis, which is similar to generating number sequences using math, particularly in view of Fig. 1 and 9 of the drawings. The specification at paragraph [0046], with reference to Fig. 5 of the drawings, further explains that “the attractor generation unit 33 generates virtual time-series data by introducing a characteristic time shift term (T) every dimension with each of the plurality of numerical values (the highest point (xh), the lowest point (xi), and the interpolation points (xini, xin2, ---)).” Persistent homology is a construction in mathematics (algebraic topology and homological algebra) for applied data analysis, therefore the step for analyzing the time-series data falls within the mathematical concepts grouping of abstract ideas. The step for comparing the generated Betti series to a predetermined Betti series is an observation and falls within the mental processes grouping of abstract ideas. See MPEP § 2106.04(a)(2). The set for generating an alert signal upon the occurrence of a match is construed as an applying a rule upon the occurrence of a predetermined condition and falls within the mental processes grouping of abstract ideas.
Applying the broadest reasonable interpretation of the claim language in view of the specification, the limitations of claim 1 fall within the mathematical concepts and metal processes groupings and recite and abstract ideas of collecting sensor data, analyzing it with mathematical concepts, and generating an output for human decision making.
The claim fails recite specific functions that are performed to control the sensor generating the data for analysis or the environment related to the sensor data in a meaningful way that goes beyond the collection, analysis, and output of a result of the collection and analysis of the sensor data. Generating an alert is insignificant post-solution activity equivalent to transmitting or communicating information and cannot transform an abstract idea into patent eligible subject matter because it merely generates the data analysis output (alert signal) to a user for further decision making. The recited step for reading out time-series data from a memory is receiving data, and constitutes insignificant post-solution activity. See MPEP § 2106.05(g).
Under Step 2A Prong Two the judicial exception of claim 1 is not integrated into a practical application. In particular, the claims only recite a processor and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraphs [0066-0068] state: “all or a part of various types of processing functions performed by the data analysis device 1 may be executed on a CPU (or a microcomputer such as an MPU or an MCU (Micro Controller Unit)).” The claimed steps are analogous to those of Claim 2 of Example 40 wherein the claims are directed to mere data gathering steps that automate the comparison of data without significantly more than the recited insignificant extra solution activity and mere instructions to apply the exception using generic computer components.
While the claim recites limitations that place conditions on how the interpolation points are used, the underlying mathematical relationship is not analogous to the improved learning method of Example 48, claim 2. Additionally, the generated attractor is not used in a meaningful way that goes beyond generation of an output that is used for human decision making. Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim fails to integrate the recited mathematical concepts abstract idea into a practical application.
Under Step 2B claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 5, 10, and 15 through 29 include the abstract ideas of the independent claims. The claims merely narrow the mathematical concept/mental process abstract idea by describing how the data analysis is performed using additional mathematical relationships and data manipulations using interpolation points to generate a result of the data collection and analysis steps. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly independent claims 6 and 11 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore claims 1, 5-6, 10-11, and 15-29 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Cook et al. (US 2016/0314255) - methods, techniques, and systems for using sensor-based data to determine and model activities of daily living, to predict cognitive assessment, and/or to detect change in functional cognitive abilities over time. AR identifies activity labels in real time as sensor event sequences are observed. Each event within the window is weighted based on its time offset and mutual information value relative to the last event in the window. A feature vector is calculated using accumulated sensor events in a window from the labeled sensor data collected over a month. The feature vector contains information such as time of the first and last sensor events, temporal span of the window, and influences of all other sensors on the sensor generating the most recent event based on mutual information.
Genov et al. (US 2020/0397383) - system comprising one or more processors and one or more memory units, the one or more processors in communication with the one or more memory units, the one or more processors configured to execute: a feature extraction module to implement an extraction approach to extract one or more features from the one or more physiological recording signals; a machine learning module to apply a machine learning model based on input data to detect a physiological event or condition for classification, the input data comprising the extracted features, the machine learning model trained using a training set comprising feature vectors of time-series data labelled with known occurrences of the physiological event or condition; and an output module to output the classification of the machine learning module.
Zhang (US 11,444,957) - Systems and methods are described for detection and classification of malware using an artificial intelligence (AI) based approach. According to one embodiment, a central training node (T-node) of a cybersecurity fabric maintains a sample library having multiple samples. The samples include virus samples and benign samples. The extraction of features from the samples can be performed by: reading, by the T-node, using multiple buffers arranged in parallel topology, binary files associated with the samples such that each binary file is stored within one of the multiple buffers; and processing, by the T-node, each of the binary files using a feature extraction technique to yield an m-dimensional feature vector, wherein each dimension of the m-dimensional feature vector corresponds to an extracted feature of respective binary file. The artificial intelligence learning unit is triggered to perform learning based on any or a combination of features stored in the feature depository when the number of samples stored in the sample library exceeds a configurable or predetermined threshold.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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, Rutao WU can be reached at 571-272-6045. 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.
/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623