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 .
Remarks
This action is in response to the application received on 7/16/26. Claims 1-20 are pending in the application. Applicants arguments are carefully and respectfully considered.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-4, 9-12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ellis et al. (US 2022/0284994), and further in view of Alvarez et al. (US 20240119847) and Levinson et al. (US 11,301,767).
Claims 5-8, 13-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ellis in view of Alvarez and Levinson, and further in view of Elsaid et al. (US 2024/0411816).
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 an abstract idea without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claims 1, 10, and 17, the limitation of “parse … metadata from each device in the plurality of data collection devices, identify a plurality of instrument tuning files…, determine the calibration parameters using the instrument tuning file”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, these limitations encompass the user analyzing data. Similarly, the limitation of “aggregate the data into a single file using the calibration parameters, extract experimental data from the aggregated data using the calibration parameters and the vendor independent libraries, and convert the experimental data from respective vendor formats into a unified markup format” and “normalize the converted data… and generate an image from the normalized data; … generate a classification for the image…; and … determine, based on the image and the classification, whether an error is likely present in the data”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the human mind, or by a human using a pen and paper, but for the recitation of generic computer components. For example, these limitations encompass the user analyzing and compiling data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims recite a plurality of data collection devices, wherein a first data collection device in the plurality of data collection devices comprises: a sensor device configured to sense data from an environment, a data collection device processor, a data collection device memory, a data collection device controller, a data harmonizer device, however, this is recited as a high-level of generality (i.e., as generic memory) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim “receive data from a plurality of data collection devices.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “receive data from a plurality of data collection devices”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2, 11, and 18, the limitations are directed towards “request the instrument tuning file from a corresponding data collection device,” which the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to claims 3, 12, and 19, the limitations are directed towards “identify the instrument tuning file in the data,” which, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, these limitations encompass the user analyzing data.
With respect to claims 4-9 and 13-16, the limitations further define components addressed above and do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 20, the limitations are directed towards “locating the instrument tuning file in the corresponding memory,” which, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, these limitations encompass the user analyzing data.
Claim Rejections - 35 USC § 103
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, 9-12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ellis et al. (US 2022/0284994), and further in view of Levinson et al. (US 11,301,767).
With respect to claim 1, A system, comprising:
a plurality of data collection devices, wherein a first data collection device in the plurality of data collection devices comprises:
a sensor device configured to sense data from an environment, a data collection device processor, a data collection device memory, and a data collection device controller (Ellis, pa 0032, The devices include a user device such as a smartphone 104 or other computing/communications device, running a mobile application 106 for performing data collection and interacting with the central study management system 100. In this example, the user additionally uses a smartwatch 108 with one or more sensor(s) for acquiring health data. Researchers (e.g. 110, 114) also interact with the central study management system using respective user devices 112, 116 such desktop/laptop/tablet personal computers.); and
a data harmonizer device in communication with the plurality of data collection devices (Ellis, pa 0032, a central study management system 100, which interacts with devices associated with a study subject or patient 102.), the data harmonizer device, comprising:
a memory storing vendor independent libraries corresponding to respective data collection devices in the plurality of data collection devices (Ellis, pa 0172, Configuration is based on provision of the modular data ingestion subsystem and modular data processing subsystem described above (step 900). Additionally, a database is maintained storing a catalogue of data collection activities (901). This includes task definitions (discussed further below) defining data collection activities which may be performed at user devices)
a receiver configured to receive data from the plurality of data collection devices (Ellis, Fig. 3, data processor gateway 310),
a data converter (Ellis, Fig. 3, data processor 316) configured to:
parse, using the vendor independent libraries stored in the memory, metadata from each data collection device in the plurality of data collection devices (Ellis, pa 0058, If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243).),
identify a plurality of instrument tuning files from respective data collection devices in the plurality of data collection devices using the metadata (Ellis, pa 0059-0060, Once the raw assignment data has been extracted from the response received it is checked for quality in step 244. … a sensor data file is received for a walking assignment. The walking assignment requires the patient to walk for 6 minutes), wherein the plurality of instrument tuning files comprise respective calibration parameters that provide metrics for accuracy of measurements for the respective data collection devices in the plurality of data collection devices (Ellis, pa 0239, The assignment-level configuration provides instructions on the flow of a given 'virtual visit'. It consists of assignment flows, which provide the configuration for a state-engine that runs the assignments for the day. Each assignment contains: … pa 0244-0245, Compliance Metrics which set additional rules on how the assignments can be completed. Some examples could be a time limit that begins once the first assignment is started, or readings that would indicate that a user took or didn't take medication before an assignment when it is required. [0245] A task list which links to the task definitions that should be available for completion within the launch due window. For example, the tasks list could include a walk test and a symptom questionnaire.),
determine the calibration parameters using the plurality of instrument tuning files (Ellis, pa 0065, In step 246 assignment compliance is calculated. This may e.g. involve determining whether all necessary measurements and other inputs for an assignment have been carried out. For example, if an assignment includes multiple data collection activities ( e.g. a walking assignment, a breathing assignment, and a questionnaire), and one of the activities has not been completed, this may be flagged as non-compliant.),
aggregate the data into a single file using the calibration parameters (Ellis, pa 0069-0070, In the final stage, data from all assignments and patients/devices is compiled into a study dataset by a study dataset processing module 126. The process is illustrated in FIG. 2E. … This dataset contains all of the measurements recorded for all of the completed subject assignments.),
extract experimental data from the aggregated data using the calibration parameters and the vendor independent libraries (Ellis, pa 0072, which may include removal of duplicates or removal of records relating to missed or incomplete assignments/tasks depending upon the requirements of the researcher. Examiner note: data left after removal of records is “experimental data” & pa 0190, After selection from the catalogue, the configuration may be generated directly from the task definition or the task definition may be customized by the researcher if needed.), and
convert the experimental data from respective vendor formats into a unified markup format using the vendor independent libraries, thereby generating converted data (Ellis, pa 0072, After completion of quality checks the data is output in the file format required by the researcher in step 256 and then transmitted to the researcher in step 258).
Ellis doesn't expressly discuss convert the experimental data from respective vendor formats into a unified markup format using the vendor independent libraries, thereby generating converted data; an image generator configured to generate an image from the converted data; a data classifier configured to generate a classification for the image, wherein the classification identifies the image as belonging to a label; and an error detector configured to determine, based on the image and the classification, whether an error is likely present in the data.
Alveraz teaches convert the experimental data from respective vendor formats into a unified markup format using the vendor independent libraries, thereby generating converted data (Alveraz, pa 0015, One particular model for handling increasingly complex data flows is the Aeronautical Information Exchange Model (“AIXM”). AIXM is a model that describes the entities and relationships for aeronautical features (e.g., airports, designated points, runways, airspaces, volumes, navaids, terminal procedures, etc.). AIXM describes Extensible Markup Language (“XML”) messages and features used to exchange information about the aeronautical data. & pa 0031, the processor(s) 106 can also be configured to convert the first data set 116 from the first data structure to the common data structure 118 to generate a first converted data set.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Ellis with the teachings of Alvarez because it enables consumption of data from different data providers (Alvarez, pa 0016-0017).
Levinson teaches an image generator configured to: normalize the converted data using the … format, thereby generating normalized data, and generate an image from the normalized data (Levinson, Col. 41 Li. 17-29, In example 4091, sensor data from an autonomous vehicle may be used to generate data representing heat maps 4021. … Similarly, in examples 4093 and 4095, sensor data (e.g., frames of sensor data) from one or more sensors and/or different types of sensors may be used to generate data representing heat maps 4031 and 4041, respectively. & Col. 42 Li. 30-33, Sensor data 4131-4135 may be communicated to a heat map generator 4140 configured to receive the sensor data 4131-4135, generate heat map data representative of the sensor data and output heat map data 4141),
a data classifier configured to generate a classification for the image, wherein the classification identifies the image as belonging to a label (Levinson, Col. 41 Li. 66 – Col. 42 Li. 6, The heat map data may be used to infer semantic classification of objects or a region (e.g., an un-marked pedestrian cross-walk or trail) that exists in the region based on the heat map data indicating usage or patterns of usage in the region based on behavior associated with detected objects in the region (e.g., objects classified as pedestrians transiting the region that may constitute the un-marked trail or cross-walk). ), and
an error detector configured to determine, based on the image and the classification, whether an error is likely present in the data (Levinson, Col. 42 Li. 6-9, Updated or otherwise amended or changed data 4097 (e.g., map data, map tiles, route data, RNDF, etc.) may be generated based on inferences that may be drawn from the heat map data.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Ellis with the teachings of Levinson because it provides efficient analysis of the sensed data (Col. 40 Li. 40-51).
With respect to claim 2, Ellis in view of Alvaraz and Levinson teaches the system of claim 1, wherein the data converter is further configured to: request the instrument tuning file from a corresponding data collection device in the plurality of data collection devices (Ellis, pa 0057, data from the patient's completed assignment is initially stored on the smartphone and then transmitted to the central system as and when a network connection is available. Upon receipt of the data, the central study management system first determines the current patient, status and study which is associated with the data).
With respect to claim 3, Ellis in view of Alvaraz and Levinson teaches the system of claim 1, wherein the data converter is further configured to: identify an instrument tuning file in the plurality of instrument tuning files as belonging to a corresponding data collection device in the plurality of data collection devices (Ellis, pa 0058, If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file.).
With respect to claim 4, Ellis in view of Alvaraz and Levinson teaches the system of claim 1, wherein the metadata identifies an analytical method used by a corresponding data collection device in the plurality of data collection devices (Ellis, pa 0038, assignments can include any of: device based assignments, e.g. using sensors (such as an accelerometer) incorporated into or connected to the user device 104, patient diaries, standard clinical questionnaires, functional status assignments etc., all of which employ the mobile application to collect and forward data to the central system.).
With respect to claim 9, Ellis in view of Alvaraz and Levinson teaches the data harmonizer device of claim 1, wherein the calibration parameters provide metrics for respective accuracies of respective measurements in the data (Ellis, pa 0060, a sensor data file is received for a walking assignment. The walking assignment requires the patient to walk for 6 minutes … an assignment is flagged as "incomplete" because it was not completed as required and/or required data is missing or of insufficient quality.).
With respect to claim 10, Ellis teaches a data harmonizer device for harmonizing data received from a plurality of data collection devices, the data harmonizer device comprising:
a memory storing vendor independent libraries corresponding to respective data collection devices in the plurality of data collection devices (Ellis, pa 0172, Configuration is based on provision of the modular data ingestion subsystem and modular data processing subsystem described above (step 900). Additionally, a database is maintained storing a catalogue of data collection activities (901). This includes task definitions (discussed further below) defining data collection activities which may be performed at user devices), wherein the respective data collection devices comprise respective sensor devices configured to sense data from an environment (Ellis, pa 0032, The devices include a user device such as a smartphone 104 or other computing/communications device, running a mobile application 106 for performing data collection and interacting with the central study management system 100. In this example, the user additionally uses a smartwatch 108 with one or more sensor(s) for acquiring health data. Researchers (e.g. 110, 114) also interact with the central study management system using respective user devices 112, 116 such desktop/laptop/tablet personal computers.); and
a data converter device (Ellis, Fig. 3, data processor 316) configured to:
parse, using the vendor independent libraries stored in the memory, metadata from each device in the plurality of data collection devices (pa 0058, If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243).),
identify a plurality of instrument tuning files from respective data collection devices in the plurality of data collection devices using the metadata (Ellis, pa 0059-0060, Once the raw assignment data has been extracted from the response received it is checked for quality in step 244. … a sensor data file is received for a walking assignment. The walking assignment requires the patient to walk for 6 minutes), wherein the plurality of instrument tuning files comprise respective calibration parameters that provide metrics for accuracy of measurements for the respective data collection devices in the plurality of data collection devices (Ellis, pa 0239, The assignment-level configuration provides instructions on the flow of a given 'virtual visit'. It consists of assignment flows, which provide the configuration for a state-engine that runs the assignments for the day. Each assignment contains: … pa 0244-0245, Compliance Metrics which set additional rules on how the assignments can be completed. Some examples could be a time limit that begins once the first assignment is started, or readings that would indicate that a user took or didn't take medication before an assignment when it is required. [0245] A task list which links to the task definitions that should be available for completion within the launch due window. For example, the tasks list could include a walk test and a symptom questionnaire.),
determine the calibration parameters using the plurality of instrument tuning files (Ellis, pa 0065, In step 246 assignment compliance is calculated. This may e.g. involve determining whether all necessary measurements and other inputs for an assignment have been carried out. For example, if an assignment includes multiple data collection activities ( e.g. a walking assignment, a breathing assignment, and a questionnaire), and one of the activities has not been completed, this may be flagged as non-compliant.),
aggregate the data into a single file using the calibration parameters (Ellis, pa 0069-0070, In the final stage, data from all assignments and patients/devices is compiled into a study dataset by a study dataset processing module 126. The process is illustrated in FIG. 2E. … This dataset contains all of the measurements recorded for all of the completed subject assignments.),
extract experimental data from the aggregated data using the calibration parameters and the vendor independent libraries (Ellis, pa 0072, which may include removal of duplicates or removal of records relating to missed or incomplete assignments/tasks depending upon the requirements of the researcher. Examiner note: data left after removal of records is “experimental data” & pa 0190, After selection from the catalogue, the configuration may be generated directly from the task definition or the task definition may be customized by the researcher if needed.).
Ellis doesn't expressly discuss convert the experimental data from respective vendor formats into a unified markup format using the vendor independent libraries, thereby generating converted data.
Alveraz teaches convert the experimental data from respective vendor formats into a unified markup format using the vendor independent libraries, thereby generating converted data (Alveraz, pa 0015, One particular model for handling increasingly complex data flows is the Aeronautical Information Exchange Model (“AIXM”). AIXM is a model that describes the entities and relationships for aeronautical features (e.g., airports, designated points, runways, airspaces, volumes, navaids, terminal procedures, etc.). AIXM describes Extensible Markup Language (“XML”) messages and features used to exchange information about the aeronautical data. & pa 0031, the processor(s) 106 can also be configured to convert the first data set 116 from the first data structure to the common data structure 118 to generate a first converted data set.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Ellis with the teachings of Alvarez because it enables consumption of data from different data providers (Alvarez, pa 0016-0017).
Levinson teaches normalize the converted data using the unified … format thereby generating normalized data (Levinson, Col. 42 Li. 30-33, Sensor data 4131-4135 may be communicated to a heat map generator 4140 configured to receive the sensor data 4131-4135, generate heat map data representative of the sensor data).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Ellis with the teachings of Levinson because it provides sensed data that can be efficiently analyzed (Levinson, Col. 40 Li. 40-51).
With respect to claims 11 and 12, the limitations are essentially the same as claims 2 and 3, and are rejected for the same reasons.
With respect to claims 17-19, the limitations are essentially the same as claims 1-3, and are rejected for the same reasons.
Claims 5-8, 13-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ellis in view of Alvarez and Levinson, and further in view of Elsaid et al. (US 2024/0411816).
With respect to claim 5, Ellis in view of Alvarez and Levinson teaches the system of claim 3, as discussed above. Ellis in view of Alvarez and Levinson doesn't expressly discuss wherein the metadata includes a pointer to a location in a corresponding memory of a computer that controls corresponding data collection device in the plurality of data collection devices.
Elsaid teaches wherein the metadata includes a pointer to a location in a corresponding memory of a computer that controls corresponding data collection device in the plurality of data collection devices (Elsaid, pa 0057, At block 702, the method includes retrieving, with a data collector service from a pre-defined location in an electronic memory, a template associated with a scientific instrument … the data collector service 608 periodically checks a predefined location of an electronic memory for the presence of a template).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Ellis in view of Alvarez and Levinson with the teaching of Elsaid because it provides indication of the way that allows communication between the collector and device (Elsaid, pa 0053).
With respect to claim 6, Ellis in view of Alvarez, Levinson and Elsaid teaches the system of claim 5, wherein the instrument tuning file is stored at the location (Elsaid, pa 0057, At block 702, the method includes retrieving, with a data collector service from a pre-defined location in an electronic memory, a template associated with a scientific instrument.).
With respect to claim 7, Ellis in view of Alvarez, Levinson and Elsaid teaches the system of claim 5, wherein the experimental parameters are stored at the location (Elsaid, pa 0058, the data collector service 608 parses the template by interpreting the schema in a declarative manner to extract a series of rules for the scientific instrument associated with the template. The rules define what data should be collected under what conditions, and how such data should be packaged and reported.).
With respect to claim 8, Ellis in view of Alvarez, Levinson and Elsaid teaches the system of claim 5, wherein the data converter is configured to locate the instrument tuning file in the corresponding memory based on the metadata (Elsaid, pa 0053, To communicate with and receive data from a scientific instrument, the data collector service 608 operates according to the template for that scientific instrument. In some aspects, the template is stored in an electronic memory of the computing device 602. …The template acts as a contract between the data collector service 608 and any instrument and/or instrument driver that is part of the data collection process. The data collector service 608 retrieves a template, loads the schema, and interprets the schema in a declarative manner. In some aspects the templates are stored in pre-defined locations in the electronic memory. For example, the electronic memory of the computing device 602 may have reserved locations for the storage of templates.).
With respect to claims 13-16 and 20, the limitations are essentially the same as claims 5-8, and are rejected for the same reasons.
Response to Arguments
35 U.S.C. 101
Applicant argues that the amended independent claims 1, 10, and 17 contain patentable subject matter. The Examiner respectfully disagrees. The amendments add system components to the claim, however, these are recited as a high-level of generality (i.e., as generic memory) such that it amounts to no more than mere instructions to apply the exception using generic computer components.
35 U.S.C. 103
Applicant argues that Ellis fails to teach that the “catalog of data collection activities” are “vendor independent libraries” that are used to “parse metadata from each data collection device in the plurality of data collection devices” and to “convert the experimental data from respective vendor formats into a unified markup format” because the catalog of Ellis defines data collection activities. The Examiner respectfully disagrees. When parsing the data, the assignment data is collected in accordance with the assignment given (pa 0058). The data received is stored as raw data and contains patient information that must be parsed in order to be interpreted, such as assignment name, device used and timestamps. An example is given in pa 0060 where the walking assignment requires the patient to walk for 6 minutes. A quality check on the sensor data is performed based on this requirement, providing “vendor independent libraries” that are used to “parse metadata from each data collection device in the plurality of data collection devices.”
Applicant seems to argue a newly amended limitation. Applicant’s amendment has rendered the previous rejection moot. Upon further consideration of the amendment, a new grounds of rejection is made in view of Alvarez et al. (US 20240119847).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRITTANY N ALLEN whose telephone number is (571)270-3566. The examiner can normally be reached M-F 9 am - 5:00 pm EST.
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/BRITTANY N ALLEN/Primary Examiner, Art Unit 2169
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