1. An intelligent driving assessment training method based on eyeball tracking is characterized by comprising the following steps:

acquiring eyeball tracking sample information and vehicle driving sample information of a driver, and mapping the eyeball tracking sample information, the vehicle driving sample information and a driving grade result of the driver to be used as sample data;

inputting the sample data into a PNN model to generate a driving evaluation model;

the method comprises the steps of collecting eyeball tracking information and vehicle driving information of a user, and carrying out data analysis on the collected eyeball tracking information and the vehicle driving information through a driving evaluation model to obtain a driving evaluation result;

and generating a driving training model according to the driving evaluation result so as to carry out driving training for the user.

2. The intelligent driving assessment training method based on eyeball tracking as claimed in claim 1, wherein the process of obtaining eyeball tracking sample information of a driver and vehicle driving sample information comprises:

the eyeball of the driver is tracked through the VR head so as to obtain the eyeball tracking sample information of the driver, and the vehicle driving sample information of the driving operation of the driver according to the instruction in the VR virtual driving game is obtained through the VR virtual driving game.

3. The intelligent driving assessment training method based on eyeball tracking as claimed in claim 2, wherein the process of obtaining the vehicle driving sample information of the driving operation of the driver according to the instructions in the VR virtual driving game through the VR virtual driving game comprises:

creating a VR virtual driving game through unity3 d;

creating a driving context and an obstacle level in the VR virtual driving game;

issuing instructions regarding vehicle speed and attitude based on the driving situation and the obstacle level;

and acquiring vehicle driving sample information of the driver for driving operation according to the instruction.

4. The intelligent driving assessment training method based on eye tracking according to claim 1,

the eye tracking sample information includes eye movement data and pupil movement data.

5. The intelligent driving assessment training method based on eye tracking according to claim 1,

the vehicle driving sample information comprises a vehicle running track, a running vehicle speed, obstacle information and a driving result.

6. The intelligent driving assessment training method based on eyeball tracking as claimed in claim 5, wherein the process of inputting the sample data into PNN model to generate driving assessment model comprises:

taking the three-dimensional vector of the eyeball tracking sample information as first data; taking a four-dimensional vector formed by the vehicle running track and the running vehicle speed as second data; taking a five-dimensional vector formed by the vehicle running track, the running vehicle speed and the obstacle information as third data;

taking the first data, the second data and the third data as input quantities of the PNN model, and taking the driving grade result as an expected output vector of the PNN model to carry out repeated training so as to obtain a predicted driving model;

testing and verifying the predicted driving model through vehicle driving data with known driving grade results; if the prediction result output by the prediction driving model according to the vehicle driving data is inconsistent with the known driving grade result, the repeated training of the prediction driving model is continued until the prediction result output by the prediction driving model according to the vehicle driving data is consistent with the known driving grade result; and if the prediction result output by the prediction driving model according to the vehicle driving data is consistent with the known driving grade result, taking the prediction driving model as a driving evaluation model.

7. The intelligent driving assessment training method based on eyeball tracking as claimed in claim 6, wherein the process of collecting eyeball tracking information and vehicle driving information of a user and performing data analysis on the collected eyeball tracking information and vehicle driving information through the driving assessment model to obtain the driving assessment result comprises:

storing the driving assessment model in the VR virtual driving game to generate a VR virtual driving test module;

tracking the eyeball of the user through a VR head to acquire eyeball tracking information of the user, and acquiring vehicle driving information of the user through a VR virtual driving game in the VR virtual driving test module;

acquiring a driving evaluation result of the user according to the eyeball tracking information and the vehicle driving information through a driving evaluation model in the VR virtual driving test module; wherein the driving assessment result at least comprises a driving grade result of the user.

8. The intelligent driving assessment training method based on eyeball tracking as claimed in claim 5, wherein the process of generating a driving training model for driving training for the user according to the driving assessment result comprises:

amplifying or reducing and adjusting the vehicle speed and the obstacles of the user according to the driving evaluation result to generate a driving training model;

and acquiring a training result of the user based on the driving training model, and performing parameter adjustment on the driving training model according to the training result so as to perform driving training for the user.

9. An intelligent driving assessment training system based on eyeball tracking, which realizes the intelligent driving assessment training method based on eyeball tracking according to any one of claims 1 to 8, and is characterized by comprising the following steps:

the system comprises a sample acquisition module, a data processing module and a data processing module, wherein the sample acquisition module is used for acquiring eyeball tracking sample information and vehicle driving sample information of a driver and mapping the eyeball tracking sample information, the vehicle driving sample information and a driving grade result of the driver to be used as sample data;

the model training module is used for inputting the sample data into a PNN model to generate a driving evaluation model;

the driving evaluation module is used for acquiring eyeball tracking information and vehicle driving information of a user and carrying out data analysis on the acquired eyeball tracking information and the vehicle driving information through the driving evaluation model so as to obtain a driving evaluation result;

and the driving training module is used for generating a driving training model according to the driving evaluation result so as to carry out driving training on the user.

10. The intelligent eyeball tracking-based driving assessment training system of claim 9, wherein the sample acquisition module and the driving assessment module comprise VR head-worn and VR virtual driving games, wherein,

the VR head is used for tracking the eyeball of the driver to obtain eyeball tracking sample information of the driver and also used for tracking the eyeball of the user to obtain eyeball tracking information of the user;

the VR virtual driving game is used for obtaining vehicle driving sample information of driving operation of the driver according to instructions in the VR virtual driving game and obtaining vehicle driving information of the user.

Background

Due to advances in technology and the diversification of market demands, virtual reality systems are becoming more and more common and are used in many fields such as computer games, health and safety, industry and educational training. To name a few examples, hybrid virtual reality systems are being integrated into mobile communication devices, gaming machines, personal computers, movie theaters, theme parks, university laboratories, student classrooms, hospital exercise gyms, and other corners of life.

Virtual reality or mixed reality Head Mounted Displays (HMDs) are commonly used to experience a virtual environment, and various HMDs include a scene camera that captures images of a user's environment (e.g., a scene) and a display that displays the images to the user.

The virtual reality technology can create a virtual computer simulation system to bring experience close to the real world to users, so the virtual reality technology can also carry out driving training, and driving training equipment realized based on the virtual reality system is also available in the market at present, but the driving training equipment cannot carry out deep data analysis according to eyeball tracking information and driving situations, so that the driving training equipment is inaccurate in evaluation and training.

Therefore, there is a need for an intelligent driving assessment training method and system based on eye tracking, which can assess driving of a user, generate training parameters suitable for the user, and train the user.

Disclosure of Invention

In view of the above problems, an object of the present invention is to provide an intelligent driving assessment training method and system based on eye tracking, so as to solve the problem that the existing driving training device cannot perform deep data analysis according to eye tracking information and driving situations, which results in inaccurate assessment and training of the driving training device.

The invention provides an intelligent driving evaluation training method based on eyeball tracking, which comprises the following steps:

acquiring eyeball tracking sample information and vehicle driving sample information of a driver, and mapping the eyeball tracking sample information, the vehicle driving sample information and a driving grade result of the driver to be used as sample data;

inputting the sample data into a PNN model to generate a driving evaluation model;

the method comprises the steps of collecting eyeball tracking information and vehicle driving information of a user, and carrying out data analysis on the collected eyeball tracking information and the vehicle driving information through a driving evaluation model to obtain a driving evaluation result;

and generating a driving training model according to the driving evaluation result so as to carry out driving training for the user.

Preferably, the process of obtaining eyeball tracking sample information of the driver and vehicle driving sample information includes:

the eyeball of the driver is tracked through the VR head so as to obtain the eyeball tracking sample information of the driver, and the vehicle driving sample information of the driving operation of the driver according to the instruction in the VR virtual driving game is obtained through the VR virtual driving game.

Preferably, the process of obtaining vehicle driving sample information of driving operation performed by the driver according to an instruction in the VR virtual driving game through the VR virtual driving game includes:

creating a VR virtual driving game through unity3 d;

creating a driving context and an obstacle level in the VR virtual driving game;

issuing instructions regarding vehicle speed and attitude based on the driving situation and the obstacle level;

and acquiring vehicle driving sample information of the driver for driving operation according to the instruction.

Preferably, the eye tracking sample information includes eye movement data and pupil movement data.

Preferably, the vehicle driving sample information includes a vehicle running track, a running vehicle speed, obstacle information, and a driving result.

Preferably, the process of inputting the sample data into a PNN model to generate a driving assessment model comprises:

taking the three-dimensional vector of the eyeball tracking sample information as first data; taking a four-dimensional vector formed by the vehicle running track and the running vehicle speed as second data; taking a five-dimensional vector formed by the vehicle running track, the running vehicle speed and the obstacle information as third data;

taking the first data, the second data and the third data as input quantities of the PNN model, and taking the driving grade result as an expected output vector of the PNN model to carry out repeated training so as to obtain a predicted driving model;

testing and verifying the predicted driving model through vehicle driving data with known driving grade results; if the prediction result output by the prediction driving model according to the vehicle driving data is inconsistent with the known driving grade result, the repeated training of the prediction driving model is continued until the prediction result output by the prediction driving model according to the vehicle driving data is consistent with the known driving grade result; and if the prediction result output by the prediction driving model according to the vehicle driving data is consistent with the known driving grade result, taking the prediction driving model as a driving evaluation model.

Preferably, the process of collecting the eyeball tracking information and the vehicle driving information of the user, and performing data analysis on the collected eyeball tracking information and the vehicle driving information through the driving evaluation model to obtain the driving evaluation result includes:

storing the driving assessment model in the VR virtual driving game to generate a VR virtual driving test module;

tracking the eyeball of the user through a VR head to acquire eyeball tracking information of the user, and acquiring vehicle driving information of the user through a VR virtual driving game in the VR virtual driving test module;

acquiring a driving evaluation result of the user according to the eyeball tracking information and the vehicle driving information through a driving evaluation model in the VR virtual driving test module; wherein the driving assessment result at least comprises a driving grade result of the user.

Preferably, the process of generating a driving training model according to the driving evaluation result to perform driving training for the user includes:

amplifying or reducing and adjusting the vehicle speed and the obstacles of the user according to the driving evaluation result to generate a driving training model;

and acquiring a training result of the user based on the driving training model, and performing parameter adjustment on the driving training model according to the training result so as to perform driving training for the user.

The invention also provides an intelligent driving evaluation training system based on eyeball tracking, which is used for realizing the intelligent driving evaluation training method based on eyeball tracking and comprises the following steps:

the system comprises a sample acquisition module, a data processing module and a data processing module, wherein the sample acquisition module is used for acquiring eyeball tracking sample information and vehicle driving sample information of a driver and mapping the eyeball tracking sample information, the vehicle driving sample information and a driving grade result of the driver to be used as sample data;

the model training module is used for inputting the sample data into a PNN model to generate a driving evaluation model;

the driving evaluation module is used for acquiring eyeball tracking information and vehicle driving information of a user and carrying out data analysis on the acquired eyeball tracking information and the vehicle driving information through the driving evaluation model so as to obtain a driving evaluation result;

and the driving training module is used for generating a driving training model according to the driving evaluation result so as to carry out driving training on the user.

Preferably, the sample acquisition module and the driving assessment module comprise a VR headset and a VR virtual driving game, wherein,

the VR head is used for tracking the eyeball of the driver to obtain eyeball tracking sample information of the driver and also used for tracking the eyeball of the user to obtain eyeball tracking information of the user;

the VR virtual driving game is used for obtaining vehicle driving sample information of driving operation of the driver according to instructions in the VR virtual driving game and obtaining vehicle driving information of the user.

It can be known from the above technical solutions that the intelligent driving evaluation training method and system based on eyeball tracking provided by the present invention first obtain eyeball tracking sample information and vehicle driving sample information of a driver, then map the eyeball tracking sample information, the vehicle driving sample information and a driving grade result of the driver as sample data, input the sample data into a PNN model to generate a driving evaluation model, then collect eyeball tracking information and vehicle driving information of a user, and perform data analysis on the collected eyeball tracking information and vehicle driving information through the driving evaluation model to obtain a driving evaluation result, and then generate a driving training model according to the driving evaluation result to perform driving training for the user, that is, the obtained eyeball movement data represents observation habits and pre-judgment abilities of the user, and a reasonable driving evaluation model is designed through a model based on a PNN neural network, and then, the driving evaluation model is used for processing and analyzing data including eyeball motion data to evaluate the driving of the user, and meanwhile, training parameters suitable for the user are generated so as to train the user and rely on eyeball tracking data, the driving can be trained by combining VR equipment, the driving habit of the user is improved emphatically, and the driving observation capability of the user is improved.

Drawings

Other objects and results of the present invention will become more apparent and more readily appreciated as the same becomes better understood by reference to the following specification taken in conjunction with the accompanying drawings. In the drawings:

FIG. 1 is a flow chart of a method for intelligent driving assessment training based on eye tracking according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an intelligent driving assessment training system based on eye tracking according to an embodiment of the present invention.

Detailed Description

The virtual reality technology can create a virtual computer simulation system to bring experience close to the real world to users, so the virtual reality technology can also carry out driving training, and driving training equipment realized based on the virtual reality system is also available in the market at present, but the driving training equipment cannot carry out deep data analysis according to eyeball tracking information and driving situations, so that the driving training equipment is inaccurate in evaluation and training.

In view of the above problems, the present invention provides an intelligent driving assessment training method and system based on eye tracking, and the following describes in detail specific embodiments of the present invention with reference to the accompanying drawings.

In order to illustrate the intelligent driving assessment training method and system based on eyeball tracking provided by the present invention, fig. 1 shows an example of the intelligent driving assessment training method based on eyeball tracking according to the embodiment of the present invention; fig. 2 shows an example of an intelligent driving assessment training system based on eye tracking according to an embodiment of the present invention.

The following description of the exemplary embodiment(s) is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. Techniques and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be considered a part of the specification where appropriate.

As shown in fig. 1, the intelligent driving assessment training method based on eyeball tracking according to the embodiment of the present invention includes:

s1: acquiring eyeball tracking sample information and vehicle driving sample information of a driver, and mapping the eyeball tracking sample information, the vehicle driving sample information and a driving grade result of the driver to be used as sample data;

s2: inputting the sample data into a PNN model to generate a driving evaluation model;

s3: the method comprises the steps of collecting eyeball tracking information and vehicle driving information of a user, and carrying out data analysis on the collected eyeball tracking information and the vehicle driving information through a driving evaluation model to obtain a driving evaluation result;

s4: and generating a driving training model according to the driving evaluation result so as to carry out driving training for the user.

As shown in fig. 1, the process of acquiring the eyeball-tracking sample information of the driver and the vehicle driving sample information in step S1 includes:

s11: the eyeball tracking sample information of the driver is obtained by tracking the eyeball of the driver through the VR head, and the vehicle driving sample information of the driver for driving operation according to the instruction in the VR virtual driving game is obtained through the VR virtual driving game.

Wherein, the process of obtaining the vehicle driving sample information that the driver drives the operation according to the instruction in the virtual driving game of VR through the virtual driving game of VR includes:

s111: creating a VR virtual driving game through unity3 d;

s112: creating a driving situation and an obstacle level in a VR virtual driving game;

s113: issuing instructions regarding vehicle speed and attitude based on the driving situation and the obstacle level;

s114: obtaining vehicle driving sample information of a driver for driving operation according to the instruction;

the eyeball tracking sample information comprises eyeball motion data, pupil movement data and other tracking data about eyeballs;

the vehicle driving sample information includes data on the driver driving the VR virtual driving game, such as a vehicle running track, a running vehicle speed, obstacle information, and a driving result.

Specifically, firstly, a unity3d is used for manufacturing a VR virtual driving game, a game level is designed, real road conditions and driving situations are simulated, a vehicle speed and driving requirements are provided for a user, various road conditions and driving operations are simulated, namely a simulation VR virtual driving game with a driving situation and an obstacle level is created, then a driver is enabled to operate according to an instruction sent by the VR virtual game data acquisition level, the VR virtual game acquires vehicle driving sample information of the driver, and meanwhile, the driver wears a VR head matched with the VR virtual game, so that the VR head acquires eyeball tracking sample information of the driver, and data support is provided for next training of a PNN model.

As shown in fig. 1, the process of inputting sample data into the PNN model to generate the driving evaluation model in step S2 includes:

s21: taking the three-dimensional vector of the eyeball tracking sample information as first data; taking a four-dimensional vector formed by the vehicle running track and the running vehicle speed as second data; taking a five-dimensional vector formed by the vehicle running track, the running vehicle speed and the obstacle information as third data;

s22: taking the first data, the second data and the third data as input quantities of the PNN model, and taking a driving grade result as an expected output vector of the PNN model to carry out repeated training so as to obtain a predicted driving model;

s23: testing and verifying the predicted driving model through the vehicle driving data with known driving grade results; if the prediction result output by the prediction driving model according to the vehicle driving data is inconsistent with the known driving grade result, the repeated training of the prediction driving model is continued until the prediction result output by the prediction driving model according to the vehicle driving data is consistent with the known driving grade result; and if the prediction result output by the prediction driving model according to the vehicle driving data is consistent with the known driving grade result, taking the prediction driving model as a driving evaluation model.

The PNN (Probabilistic Neural Networks, PNN) model is a Probabilistic Neural network, proposed by d.f. specht in 1990, the main idea being bayesian decision rules, i.e. the expected risk of misclassification is minimal, separating the decision space within the multidimensional input space. The method is an artificial neural network based on statistical principle, and is a feedforward network model taking a Parzen window function as an activation function.

Specifically, the eyeball tracking sample information (x) is first obtained₁，y₁，z₁) As the first data X₁(ii) a Vehicle for transportingVehicle running track (x)₂，y₂，z₂) A four-dimensional vector formed by the running vehicle speed (u) is used as the second data X₂(ii) a Moving the vehicle track (x)₂，y₂，z₂) A five-dimensional vector composed of the operating vehicle speed (u) and the obstacle information (v) is used as the third data X₃Then the first data X is processed₁Second data X₂The third data X₃And repeatedly training the driving grade result of the driver as an expected output vector of the PNN model to obtain a predicted driving model, testing and verifying the predicted driving model, and taking the predicted driving model as a driving evaluation model if the predicted driving model passes the verification.

As shown in fig. 1, the process of collecting the eyeball tracking information of the user and the vehicle driving information and performing data analysis on the collected eyeball tracking information and the vehicle driving information through the driving evaluation model to obtain the driving evaluation result in step S3 includes:

s31: storing the driving evaluation model in a VR virtual driving game to generate a VR virtual driving test module;

s32: tracking the eyeballs of the user through the VR head to acquire eyeball tracking information of the user, and acquiring vehicle driving information of the user through a VR virtual driving game in the VR virtual driving test module;

s33: acquiring a driving evaluation result of a user according to the eyeball tracking information and the vehicle driving information through a driving evaluation model in the VR virtual driving test module; the driving evaluation result at least comprises a driving grade result of the user;

wherein the eyeball tracking sample information comprises eyeball motion data and pupil movement data of the user;

the vehicle driving sample information comprises a vehicle running track, a running vehicle speed, obstacle information and a driving result of a user in the VR virtual driving game.

Specifically, a user uses a driving evaluation level card of a VR virtual driving game in a VR virtual driving test module to perform driving operation, and a driving simulation module collects eyeball movement data, vehicle speed, vehicle movement track and obstacle information and automatically calculates and evaluates a driving grade result of the user according to the eyeball movement data, the vehicle speed, the vehicle movement track and the obstacle information.

As shown in fig. 1, in step S4, the process of generating a driving training model according to the driving evaluation result to perform driving training for the user includes:

s41: amplifying or reducing and adjusting the vehicle speed and the obstacle of the user according to the driving evaluation result to generate a driving training model;

s42: acquiring a training result of the user based on the driving training model, and performing parameter adjustment on the driving training model according to the training result so as to perform driving training for the user;

specifically, step S4 is a process of performing parameter adjustment according to the driving evaluation result of the user to generate a driving training model suitable for the user.

In this embodiment, the vehicle speed and the obstacle of the user are adjusted in an enlarged or reduced manner according to the driving evaluation result to generate a driving training model, that is, a training level suitable for the user is generated by adjusting parameters in the original VR virtual driving game, that is, the VR virtual driving game at least includes three levels, namely, a data acquisition level for acquiring vehicle driving sample information of a driver performing driving operation according to an instruction in step S1, a driving evaluation level for use in the driving level result evaluation calculation of the user in step S3, and a training level for use in driving training of the user after parameter adjustment according to the training result in step S4, wherein the three levels all belong to the VR virtual driving game, but the three levels are independent and associated with each other, that is, after data is acquired, the PNN model is repeatedly trained as data input to generate the driving evaluation model, and then the driving evaluation level acquires the vehicle driving information of the user, so that the vehicle driving information is input into the driving evaluation model to enable the driving evaluation to measure the driving evaluation result of the user, and then the parameters of the VR virtual game are adjusted according to the driving evaluation result to generate a training level (driving training model) suitable for the driving evaluation, so that the driving training of the user is carried out according to the self condition of the user.

As described above, the intelligent driving evaluation training method based on eyeball tracking according to the present invention first obtains eyeball tracking sample information and vehicle driving sample information of a driver, then maps the eyeball tracking sample information and the vehicle driving sample information with a driving grade result of the driver as sample data, inputs the sample data into a PNN model to generate a driving evaluation model, then collects eyeball tracking information and vehicle driving information of a user, and performs data analysis on the collected eyeball tracking information and vehicle driving information through the driving evaluation model to obtain a driving evaluation result, and then generates a driving training model according to the driving evaluation result to perform driving training for the user, that is, the obtained eyeball motion data represents observation habits and pre-judgment abilities of the user, and a reasonable driving evaluation model is designed through a model based on a PNN neural network, and then, the driving evaluation model is used for processing and analyzing data including eyeball motion data to evaluate the driving of the user, and meanwhile, training parameters suitable for the user are generated so as to train the user and rely on eyeball tracking data, the driving can be trained by combining VR equipment, the driving habit of the user is improved emphatically, and the driving observation capability of the user is improved.

As shown in fig. 2, the present invention further provides an intelligent driving assessment training system 100 based on eye tracking, for implementing the aforementioned intelligent driving assessment training method based on eye tracking, including:

the sample acquisition module 101 is configured to acquire eyeball tracking sample information and vehicle driving sample information of a driver, and map the eyeball tracking sample information and the vehicle driving sample information with a driving level result of the driver to serve as sample data;

the model training module 102 is used for inputting sample data into the PNN model to generate a driving evaluation model;

the driving evaluation module 103 is configured to collect eyeball tracking information of a user and vehicle driving information, and perform data analysis on the collected eyeball tracking information and vehicle driving information through a driving evaluation model to obtain a driving evaluation result;

and the driving training module 104 is used for generating a driving training model according to the driving evaluation result so as to carry out driving training for the user.

Wherein, the sample acquisition module and the driving evaluation module comprise a VR head-wearing game and a VR virtual driving game, wherein,

the VR head is used for tracking the eyeball of the driver to obtain eyeball tracking sample information of the driver and also used for tracking the eyeball of the user to obtain eyeball tracking information of the user;

the VR virtual driving game is used for obtaining vehicle driving sample information of driving operation of the driver according to instructions in the VR virtual driving game and obtaining vehicle driving information of the user.

As can be seen from the above embodiments, the present invention provides an intelligent driving evaluation training system 100 based on eyeball tracking, which first obtains eyeball tracking sample information and vehicle driving sample information of a driver through a sample obtaining module 101, then maps the eyeball tracking sample information and the vehicle driving sample information with a driving grade result of the driver to serve as sample data, then inputs the sample data into a PNN model through a model training module 102 to generate a driving evaluation model, then collects the eyeball tracking information and the vehicle driving information of a user through a driving evaluation module 103, and performs data analysis on the collected eyeball tracking information and the vehicle driving information through the driving evaluation model to obtain a driving evaluation result, and then generates a driving training model according to the driving evaluation result through a driving training module 104 to perform driving training for the user, the method comprises the steps of acquiring eyeball motion data, expressing observation habits and prejudging abilities of users, designing a reasonable driving evaluation model through a model based on a PNN neural network, processing and analyzing data including the eyeball motion data through the driving evaluation model to evaluate driving of the users, generating training parameters suitable for the users at the same time, training the users by means of eyeball tracking data, training the driving by combining VR equipment, emphasizing on improvement of driving habits of the users, and improving driving observation abilities of the users.

The intelligent driving assessment training method and system based on eyeball tracking proposed by the invention are described above by way of example with reference to the accompanying drawings. However, it should be understood by those skilled in the art that various modifications can be made to the intelligent driving assessment training method and system based on eye tracking provided by the present invention without departing from the scope of the present invention. Therefore, the scope of the present invention should be determined by the contents of the appended claims.