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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/10/2026 has been entered.
Response to Arguments
Regarding the arguments against the rejection of the claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claims are directed to a fundamentally technical process involving physical measurement of a physical object which enables assignment of patients to medical specialties. Examiner asserts that analyzing physical motion of a physical object such as eye movement by taking measurements and assigning patients to medical specialties is abstract as noted in the rejection below under section Step 2A Prong One.
Further, the use of the trained neural network with nodes with weights from labeled eye movement data is recited as mere tools to perform the abstract idea including anonymization data and processing it as there is no indication of specific technology implementation to demonstrate a technical improvement as noted below in the Step 2A Prong 2 analysis. Analysis of the raw video to generate geometric parameters such as head tilt measurements for example recites an abstract idea. The use of the neural network recites mere computer implementation.
Additionally, since the claims are analyzed to be a part of “certain methods of organizing human activity”, abstract steps do not necessarily need to be performed in the human mind. As noted in the rejection below, the capturing of the eye movements recites insignificant pre solution activity, converting the video data into specific geometric parameters is abstract, removing interpupillary distance is abstract, and using the NN recites mere computer implementation. Rejection has been updated below.
Applicant further argues that the instant application uses “specific techniques” to solve a technical problem similar to that of the case of Thales Visionix Inc. Examiner further asserts that this example recites a specific configuration of sensors that solved a technical problem. There is no indication that the instant application recites a particular solution to a particular technical problem. The additional elements of the trained NN recites mere computer implementation, and the use of generic computing devices to carry out the abstract idea more efficiently, such as improving accuracy of diagnosis and eye analysis, improving privacy recites an improvement to the abstract idea, not necessarily a technology improvement, see MPEP 2106.05(a)II, particularly “Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”. Further, reducing transmission requirements by merely transmitting the processed data in a generic manner does not recite a technical improvement, see MPEP 2106.05(f), “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”
Applicant further argues that the claims do not recite mere generic transmission of data between computer and recites an improvement to technology related to preserving patient privacy. Examiner asserts that separating identifiable and anonymized data at an acquisition device recites the use of generic computing components to retain the anonymized data (part of the abstract idea) at the local device recites limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Under BRI, the transformation into specific geometric parameters such as head orientation data recites abstract limitations of analysis that does not necessarily need a computer to perform this task. Again, the steps of extracting the parameters and removing the identifiers are abstract steps and do not recite a technical solution. Use of the NN and recitation of the nodes as claimed and training using the labeled training data recites generic implementation as there is no specific or technological improved computer to perform these steps.
The Examples 47-49 in contrast recite eligible claims for certain claims of each example, however there is no indication that there are specific technological parameters being used in the NN in the instant application beyond the generic training steps as claimed.
As previously noted, the transmission of less data is not necessarily a technology improvement, as there is no particular algorithm or specific technical steps to achieve such an alleged improvement. Merely storing the identifiable video data at the acquisition device merely recites storing information in memory.
Regarding the analysis of the claims under Step 2B, Examiner further asserts that prior art is not necessary to establish significantly more subject matter. See MPEP 2106.05(d)(I), specifically “Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination.” Examiner directs the Applicant to the rejection below, where cases were cited to analyze the additional elements relate to the insignificant extra solution activity and there was recitation of the Applicant's Specification to show the generic nature of the computing components to implement the abstract idea. See MPEP 2106.05(d)(II). As previously noted and further detailed in the below rejection, the steps recite abstract limitations and additional elements that do not recite significantly more than the judicial exception and recites steps that are well understood, routine, and conventional.
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 1-19 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 15-16 are related to a system, and claims 1-14 are also related to methods (i.e., a process) and claims 17-19 are a computer program product using non-transitory computer-readable media storing instructions.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
A method for analyzing eye movement of a dizzy patient to enable assignment of the dizzy patient to a medical specialty, the method being carried out by an assignment system comprising an acquisition device configured to capture eye movements, where the method comprises:
- capturing eye movements of the dizzy patient in the form of video data using a video sensor of the acquisition device;
- converting the video data into anonymized eye movement parameters by mathematically transforming the video data into at least one parameter selected from the group consisting of: direction vectors of individual eyes, gaze direction of the eyes, head tilt measurements, head orientation data, Euler angles, rotation matrices, and pixel translations, wherein the converting comprises removing patient-identifiable biometric information, and wherein the anonymized eye movement parameters have a significantly smaller memory size than the recorded video data;
- transmitting only the anonymized eye movement parameters from the acquisition device to a processing device over a network while retaining patient-identifiable video data locally at the acquisition device;
- processing the anonymized eye movement parameters in a trained neural network at the processing device, wherein the trained neural network comprises neural network nodes with associated weights established based on a plurality of labeled eye movement data from patients with vestibular conditions,
- determining at least one medical specialty based on a result of processing in the trained neural network, and
- outputting an assignment of the dizzy patient to the at least one specific medical specialty.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations are related to analyzing eye movement of a dizzy patient to enable assignment of the dizzy patient to a medical specialty:
“converting” the video data into anonymized eye movement parameters by mathematically transforming the video data into at least one parameter selected for the group consisting of: direction vectors of individual eyes, gaze direction of the eyes, head tilt measurements, head orientation data, Euler angles, rotation matrices, and pixel translations, where the converting comprises removing patient-identifiable biometric information, where this is an abstract limitation of analysis using a mathematical process of converting the video data into anonymized eye movement data into a parameter such as head tilt measurements or gaze direction. Further, as part of the abstract idea, an individual can remove identifiable biometric info such as interpupillary distance when generating the eye movement parameters,
“processing” the anonymized eye movement parameters, which is an abstract limitation of an analysis of the previously converted data,
“determining” at least one medical specialty based on the result of the processing, which is an abstract limitation of a judgment based on the previous abstract analysis,
“outputting” the assignment of the dizzy patient to the at least one specific medical specialty, which is an abstract limitation related human interactions of placing the patient in the appropriate specialty group based on the previous abstract steps.
The claim limitations as a whole recite steps for analyzing eye movement of a dizzy patient to enable assignment of the dizzy patient to a medical specialty, and therefore recite managing interactions between people including social activities. The analysis of the video to generate the anonymized eye movement parameters for a dizzy patient and then outputting an assignment for the patient of a specific medical specialty recites management of the care of the patient and therefore recites certain methods of organizing human activity.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., neural network) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Additionally, claim 15 recites the similar abstract idea limitations for analyzing eye movement of a dizzy patient to enable assignment of the dizzy patient to a medical specialty, where there is further recitation of further description of the patient-identifiable information as including interpupillary distance and further defines an aspect of the abstract idea. The claim limitations as a whole recite steps for analyzing eye movement of a dizzy patient to enable assignment of the dizzy patient to a medical specialty, and therefore recite managing interactions between people including social activities. The analysis of the video to generate the anonymized eye movement parameters for a dizzy patient and then outputting an assignment for the patient of a specific medical specialty recites management of the care of the patient and therefore recites certain methods of organizing human activity.
Claim 17 recites the same abstract idea as claim 1.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 4 recites abstract limitations of “playing” a test video during the capturing of the eye movements, which is an interaction with the patient related presenting information, thus further describing the abstract idea. Claim 6 recites abstract limitations of further analyzing the video data to different eye movement tests, thus further describing the abstract idea. Claim 7 recites abstract limitations of “performing” quality control of the video data after the video is acquired, which is an analysis of the video and thus further describes the abstract idea. Claim 8 recites abstract limitations further describing the determining of the specialty which includes further analysis of a safety factor which includes the accuracy of the assignment, thus further describing the abstract idea. Claims 9 and 10 recites abstract limitations of the analysis for manually revieing the video data and for manual verification of the artificial video data that is gathered, further describing the abstract idea. Claim 11 recites abstract limitations of “recording” auxiliary parameters, further describing the abstract idea. Claims 18 and 19 recite further abstract limitations of removing patient identifiable biometric information including interpupillary distance to achieve anonymity, further describing the abstract idea related to the anonymization of the video data.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
Regarding claim 1:
A method for analyzing eye movement of a dizzy patient to enable assignment of the dizzy patient to a medical specialty, the method being carried out by an assignment system (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) comprising an acquisition device configured to capture eye movements, where the method comprises:
- capturing eye movements of the dizzy patient in the form of video data using a video sensor of the acquisition device (merely data gathering steps as noted below, see MPEP 2106.05(g) and Symantec);
- converting the video data into anonymized eye movement parameters by mathematically transforming the video data into at least one parameter selected from the group consisting of: direction vectors of individual eyes, gaze direction of the eyes, head tilt measurements, head orientation data, Euler angles, rotation matrices, and pixel translations, wherein the converting comprises removing patient-identifiable biometric information, and wherein the anonymized eye movement parameters have a significantly smaller memory size than the recorded video data (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
- transmitting only the anonymized eye movement parameters from the acquisition device to a processing device over a network while retaining patient-identifiable video data locally at the acquisition device (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
- processing the anonymized eye movement parameters in a trained neural network at the processing device, wherein the trained neural network comprises neural network nodes with associated weights established based on a plurality of labeled eye movement data from patients with vestibular conditions (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)),
- determining at least one medical specialty based on a result of processing in the trained neural network, and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
- outputting an assignment of the dizzy patient to the at least one specific medical specialty.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of:
the method carried out by an assignment system,
the anonymized eye movement parameters have a significantly smaller memory size than the recorded video data,
transmitting only the anonymized eye movement parameters from the acquisition device to a processing device over a network while retaining patient-identifiable video data locally at the acquisition device, and
the use of a neural network at the processing device, where the trained neural network is at the processing device, wherein the trained neural network comprises neural network nodes with associated weights established based on a plurality of labeled eye movement data from patients with vestibular conditions,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 16] of the Applicant’s Specification recites the use of an assignment system as part of a generic assignment device. [Page 6] recites the anonymized parameters having significantly smaller memory size than the recorded video data, however there is no indication of the use of this anonymized eye movement parameters to improve a technology related to the transmission of the data to other devices. [Page 7] recites the generic transmission of the parameters from the acquisition device to the processing device while retaining the patient identifiable video data at the acquisition device, however there is no indication of a technology improvement as recites generic computer functionality related to the transmission of data between devices; there is no indication that the transmission of only the anonymized parameters improves the processing of the of the acquired video data or improves the technology related to the neural network. [Page 7] recites the use of the network for transmitting the parameters to the processing device. [Page 6, 7] recites the use of a generic neural network for the video data processing on the processing device. [Page 14] recites the generic training of the neural network. [Page 13] recites the structure of the nodes with weights based on the eye movement data from patients with vestibular conditions, however there is no indication as claimed of a technology improvement related to the NN technologies beyond the general node structure as described. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Regarding the additional limitation of capturing eye movements of the dizzy patient in the form of video data using a video sensor of the acquisition device, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [Pages 4, 10] of Applicant’s Specification recites the use of the acquisition device for capturing the eye movements in the form of video data with the use of the video sensor as further described in [Pages 3, 10]. The use of the device to capture video data is used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities.
Additionally, regarding claim 15, this claim recites similar additional elements as claim 1, with other additional elements of: overall assignment system as described in [Page 15] of Applicant’s Specification, [Page 7, 15] describing the processing device, [Page 17] describing the determination device, [Page 15, 17] describing the output device. [Pages 16-17] recites the local conversion of the video data. The Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). The acquisition device is used for pre- solution activities as further described in [Pages 4, 10]. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Additionally, claim 17 recites similar additional elements as claim 1, where the computer product is being carried out on generic computing devices as described in [Page 15] which are interpreted to have non-transitory storage media, and these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to assign a dizzy patient to a medical specialty, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below: Claim 2 recites additional elements further describing the captured video data as being anonymized before it is processed as being translated into eye movement parameters, however the parameters are collected for the abstract idea, and thus the anonymizing is further describing insignificant pre-solution activity. Claim 3 recites additional elements further describing the parameter data as being transmitted from an acquisition device to a processing device, however the acquisition device is used for the insignificant pre-solution activity. Recitation of the processing device and part of the abstract idea being transmitted back to the acquisition device and the use of the output device amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 4 recites additional elements of a display device to carry out part of the abstract idea, however the use of this device amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 5 recites additional elements of gathering patient responses to questions along with the gathered video data, however this is data being gathered for the abstract idea, thus further describing insignificant pre-solution activity. Claim 6 recites additional elements of different neural networks that are used to process the tests, however the use of these neural networks amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 10 recites additional elements further describing the captured video data as artificial video data that is used for part of the abstract idea, thus further describing insignificant pre-solution activity. Claim 12 recites additional elements of the neural network with nodes with associated weights and training the neural network is described in Applicant’s Specification [Page 13]. The steps of providing a variety of eye movements of dizzy patients in the form of video data and manually labeling the eye movements are recited for obtaining the training data for training the NN, however there is no specific configuration of the neural network and the use of the generated training data to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer. Claim 13 recites additional elements of generating artificial video data for the training which is manipulated for it, however there is no specific configuration of the data for training the NN to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer. Claim 14 recites additional elements further describing the steps for the training data, however this recites mere instructions to implement the abstract idea on a computer. Claim 16 recites additional elements of a local assignment device and a central assignment device, however the additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer. Claims 18 and 19 recite further additional elements related to the “low sampling rate” which is further described in [Page 7], however this limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components.
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding the additional elements of claim 1:
Regarding the additional limitations of:
the method carried out by an assignment system,
the anonymized eye movement parameters have a significantly smaller memory size than the recorded video data,
transmitting only the anonymized eye movement parameters from the acquisition device to a processing device over a network while retaining patient-identifiable video data locally at the acquisition device, and
the use of a neural network at the processing device, where the trained neural network is at the processing device, wherein the trained neural network comprises neural network nodes with associated weights established based on a plurality of labeled eye movement data from patients with vestibular conditions,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “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)”). [Page 16] of the Applicant’s Specification recites the use of an assignment system as part of a generic assignment device. [Page 6] recites the anonymized parameters having significantly smaller memory size than the recorded video data, however there is no indication of the use of this anonymized eye movement parameters to improve a technology related to the transmission of the data to other devices. [Page 7] recites the generic transmission of the parameters from the acquisition device to the processing device while retaining the patient identifiable video data at the acquisition device, however there is no indication of a technology improvement as recites generic computer functionality related to the transmission of data between devices; there is no indication that the transmission of only the anonymized parameters improves the processing of the of the acquired video data or improves the technology related to the neural network. [Page 7] recites the use of the network for transmitting the parameters to the processing device. [Page 6, 7] recites the use of a generic neural network for the video data processing on the processing device. [Page 14] recites the generic training of the neural network. [Page 13] recites the structure of the nodes with weights based on the eye movement data from patients with vestibular conditions, however there is no indication as claimed of a technology improvement related to the NN technologies beyond the general node structure as described. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. The transmission of the anonymized parameters data between devices recites well understood, routine, and conventional activities.
Regarding the additional limitation of capturing eye movements of the dizzy patient in the form of video data using a video sensor of the acquisition device, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “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)”). [Pages 4, 10] of Applicant’s Specification recites the use of the acquisition device for capturing the eye movements in the form of video data with the use of the video sensor as further described in [Pages 3, 10]. The use of the device to capture video data is used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities and does not recite significantly more than the judicial exception. The video data is transmitted to the processing device for processing as described in [Page 15], however the forwarding of information from one device to another recites well understood, routine, and conventional activity.
Additionally, regarding claim 15, this claim recites similar additional elements as claim 1, with other additional elements of: overall assignment system as described in [Page 15] of Applicant’s Specification, [Page 7, 15] describing the processing device, [Page 17] describing the determination device, [Page 15, 17] describing the output device. [Pages 16-17] recites the local conversion of the video data. The Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components and does not recite significantly more than the judicial exception (see MPEP § 2106.05(f)). The acquisition device is used for pre- solution activities as further described in [Pages 4, 10]. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “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)”).
Additionally, claim 17 recites similar additional elements as claim 1, where the computer product is being carried out on generic computing devices as described in [Page 15] which are interpreted to have non-transitory storage media, and these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components and does not recite significantly more than the judicial exception.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-19 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Conclusion
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686