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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/09/2024 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner. The initialed and dated copy of Applicant’s IDS form, 1449, is attached to the instant Office Action
Status of Claims
This is a Non-Final Action on the merits in response to the application filed on 09/09/2024.
Claims 1 – 18 are currently pending and have been examined in this application.
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 – 18, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 15 recite:
collect cafe monitoring data wherein the cafe monitoring data includes at least one of status data and predicted data regarding the inside of the cafe;
collect customer data related to a customer from used by the customer;
determine a cafe curation scheme;
and generate a cafe list based on the determined cafe curation scheme and provide the cafe list to the customer.
The limitations of claim 1, under its broadest reasonable interpretation recites certain methods of organizing human activity. The claim particularly recites managing interactions where there are interactions between a person and a computer. For example, claim 1 recites observing cafe monitoring data, wherein the cafe monitoring data includes at least one of status data and predicted data regarding the inside of the café; observing customer data related to a customer used by the customer; evaluating a cafe curation scheme; and evaluating a cafe list based on the evaluated cafe curation scheme and provide the cafe list to the customer; and all of these limitations are merely collecting and managing interactions between a human and a computer. Claim 15 is substantially similar and recites the same subject matter as claim 1. Accordingly, claims 1 and 15 recite certain methods of organizing human activity.
The limitations of claim 1, under its broadest reasonable interpretation recites a mental process where the claim is collecting information, as we have collecting café monitoring data and collecting customer data; analyzing the data, as we have evaluating a café curation scheme; and displaying certain results of the collection and analysis, as we have generate a cafe list based on the determined cafe curation scheme and provide the cafe list to the customer. Accordingly, claims 1 and 15 recite mental processes.
The dependent claims encompass the same abstract ideas as well. For instance, claim 2 is directed towards observing the status data includes data related to at least one of table or seat arrangement status, table or seat occupancy status, noise status, status of music being played in a store, status of a playback history of in a store, visiting customer gender status, visiting customer age group status, visiting customer behavior status, visiting customer stay time status, order status by menu, and interior status inside the cafe; and the predicted data includes data related to at least one of predicted changes in table or seat occupancy rates, predicted seat ambiance, predicted wait times, predicted noise changes, predicted music to be played in the store, predicted visiting customer gender, predicted visiting customer age group, predicted visiting customer behavior patterns, predicted visiting purpose, predicted menu rankings, predicted recommended menu, predicted sales, predicted inventory, and predicted cafe atmosphere; claim 3 is directed towards observing the status data and the prediction data are anonymized; claim 4 is directed towards observing the customer data includes data related to at least one of customer gender, customer age, customer location, service usage history, customer preferences, preferred menu items, cafe visit history, and predicted visit purpose; claim 5 is directed towards observing a first scheme includes a scheme that uses both the status data and the prediction data for cafe curation, and evaluate the first scheme as the cafe curation scheme; claim 6 is directed towards observing a second scheme includes a scheme that uses the status data but does not use the prediction data for cafe curation, and evaluate the second scheme as the cafe curation scheme; claim 7 is directed towards observing a third scheme includes a scheme that does not use both the status data and the prediction data for cafe curation, and evaluate the third scheme as the cafe curation scheme; claim 8 is directed towards observing a fourth scheme includes a scheme that uses past cafe monitoring data and evaluate the third scheme and the fourth scheme as the cafe curation scheme; claim 9 is directed towards observing a fifth scheme includes a scheme that uses the customer data for curation, and evaluate at least one of the first scheme through the fourth scheme and the fifth scheme as the cafe curation scheme; claim 10 is directed towards evaluate the inside of the cafes included in the generated cafe listto the customer; and claim 18 is directed towards providing the prediction data related to the cafes included in the cafe list to the customer. Thus, the dependent claims further limit the abstract idea.
These judicial exceptions are not integrated into a practical application. The additional elements of a cafe curation device comprising: a cafe monitoring data collection module configured to collect cafe monitoring data provided from an artificial intelligence-based cafe monitoring device installed in a café, a customer data collection module configured to, a customer terminal, a status check signal transmission module configured to transmit a status check signal to the artificial intelligence-based cafe monitoring device, a cafe curation scheme determination module configured to, the status check signal, a cafe list generation module configured to, terminal, when the response signal includes a first response signal, the cafe curation scheme determination module is configured to, when the response signal includes a second response signal different from the first response signal, the cafe curation scheme determination module is configured to, when the response signal includes a third response signal different from the first response signal and the second response signal, the cafe curation scheme determination module is configured to, collected by the cafe monitoring data collection module for cafe curation, and when the response signal includes the third response signal, the cafe curation scheme determination module is configured to, and the cafe curation scheme determination module is configured to, a digital twin generation module configured to, terminal in the form of the digital twin virtual space, the cafe list generation module is configured to operate in conjunction with the digital twin generation module to, digital twin generation module is configured to update the digital twin virtual space, to the customer terminal, allowing the customer terminal to play the sound data during the rendering of the digital twin virtual space, terminal, to the customer terminal in the form of the digital twin virtual space, and allowing the customer terminal to display the prediction data on the screen in the form of at least one of text, icons, images, and videos are considered generic computer components as per Applicant’s Specifications shown below:
“[Mode for Invention] In some embodiments, the customer terminal 30 and the store owner terminal 32 may be computing devices such as smartphones, tablet computers, wearable devices, laptop computers, desktop computers, and the like. Users may utilize the cafe curation service through an application running on the customer terminal 30 and the store owner terminal 32.”
and thus are not practically integrated nor significantly more.
The combination of these additional elements are no more than mere instructions to apply the
exception using generic computer components (e.g., processors); and all involve evaluation and observation of data, and considered insignificant extra-solution activity (e.g., data gathering). Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 14, and 16 – 18 when analyzed both individually and in combination are also held to be ineligible for the same reasons above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Accordingly, claims 1 – 18, are not patent eligible.
Claim Rejections – 35 U.S.C. §103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 9, 14 – 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Poppi, Giacomo (WO2018142107A1) hereinafter “Poppi” in view of Ok, Jae Yun (WO2023068737A1) hereinafter “Ok” in view of Eyster, Justin et al. (U.S. Publication No. 2018/0293489) hereinafter “Eyster”.
Claims 1 and 15:
a cafe monitoring data collection module configured to collect cafe monitoring data provided, wherein the cafe monitoring data includes at least one of status data and predicted data regarding the inside of the cafe; Poppi teaches on Pg. 8, ¶ 4, the sensor unit may be configured to transmit timestamps, indicating the times at which the vibration data and/or the temperature data were collected, to the central activity monitor. For example, the vibration data and/or the temperature data may be transmitted to the central activity monitor in packets of data together with the relevant time stamp. This can allow the central activity monitor to accurately record vibration levels against time, regardless of any delay in transmitting the vibration data to the central activity monitor. Poppi teaches on Pg. 6, ¶ 3, a mathematical model is used to determine the type of activity. Thus the processor may be configured to apply the vibration data to a mathematical model to determine the type of activity. The mathematical model may be a machine learning model, which may be pre- trained on reference data. For example, the mathematical model may be a neural network model. This may allow underlying patterns in the vibration data to be identified more accurately. Thus this embodiment may allow more accurate determination of the different types of activity to be achieved, although at the cost of some additional processing.
a customer data collection module configured to collect customer data related to a customer from a customer terminal used by the customer; Poppi teaches on Pg. 6, ¶ 4, preferably the sensor unit comprises an internal clock. In this case the sensor unit may be configured to transmit timestamps, indicating the times at which the vibration data and/or the temperature data were collected, to the central activity monitor. For example, the vibration data and/or the temperature data may be transmitted to the central activity monitor in packets of data together with the relevant time stamp. This can allow the central activity monitor to accurately record vibration levels against time, regardless of any delay in transmitting the vibration data to the central activity monitor.
a cafe curation scheme determination module configured to determine a cafe curation scheme based on a response signal to the status check signal; Poppi teaches on Pg. 3, ¶ 3, by sensing vibration of the table at different points in time to produce vibration data, and processing the vibration data to determine different types of activity at the table, it may be possible for the system to identify various stages of consumption of food or beverages at the table. This in turn may be used to provided notifications to staff to help improve the customer experience and/or metrics which may be used by management to increase operational efficiency. Poppi teaches on Pg. 21, ¶ 3, using the sensor data and waiter proximity data, the backend server is configured such that it may output a prediction of the current status of the table.
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor and Poppi and Ok are similar where Poppi and Ok teach providing restaurant service to people, and Ok further teaches the following:
from an artificial intelligence-based café monitoring device installed in a café; Ok teaches on Pg. 5, ¶ 6, the artificial intelligence chatbot unit 210 may process a process for executing an artificial intelligence chatbot service by interworking with the display unit 120 and the voice input/output unit 130. The artificial intelligence chatbot unit 210 may provide the artificial intelligence chatbot service for at least one of product ordering, product payment, automatic delivery, information request, search, conversation, online ticket reservation/issuance, and special tasks. For example, the service robot 1000 is disposed in a restaurant or cafe, etc. to enable product ordering and payment and delivery processing for the order through the artificial intelligence chatbot unit 210, and also, the service robot 1000 can be used at airports, Placed in train stations, terminals, etc., through the artificial intelligence chatbot unit 210, online ticket reservation and issuance processing, search, conversation, and special missions are possible. You can search, chat, etc. The special task may include at least one of route guidance, transfer, emergency patient report, and wanted person report, and will be described in more detail through a configuration description of the special task execution unit 270 to be described later.
a status check signal transmission module configured to transmit a status check signal to the artificial intelligence-based cafe monitoring device; Ok teaches in Mode-For-Invention, Pg. 3, ¶ 9, and Pg. 4, ¶ 1, an artificial intelligence-based service robot 1000 according to an embodiment of the present invention may include at least one of a hardware unit 100 and a software unit 200, and includes a management server 10 and a financial It is connected to the server 20 through wireless communication and can transmit and receive necessary information and data. Here, the management server 10 monitors the overall status (driving status, power status, malfunction, location, damage, etc.) of the service robot 1000 and receives and shares information and data generated from the service robot 1000. Tasks and information processed by the service robot 1000 may be grasped and managed. The financial server 20 may process payment for each payment method in conjunction with the service robot 1000 when a user proceeds with product payment, online ticket purchase, etc. through the service robot 1000.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi with an artificial-intelligence-based service robot of Ok to assist businesses with implementing artificial intelligence that includes chatbot service for ordering products, information request, or making payments (Ok, Spec. Pg. 3, ¶ 5).
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Ok are similar to Eyster where Poppi, Ok and Eyster teach providing restaurant service to people, and Eyster further teaches the following:
and a cafe list generation module configured to generate a cafe list based on the determined cafe curation scheme and provide the cafe list to the customer terminal; Eyster teaches in ¶ 0003, a generalized method of automatically selecting or recommending items based on user preference topography. In particular a restaurant can be recommended using vector distances to the dishes of a specific restaurant. The system analyzes recipe vectors of menu items chosen by members of the group, and selects a restaurant that best satisfies the menu items selected.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok with a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster to assist businesses with implementing customer preferences input into computer system to provide food and restaurant recommendations (Eyster, Spec. ¶¶ 0067 – 0068).
Claim 2:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein: the status data includes data related to at least one of table or seat arrangement status, table or seat occupancy status, noise status, status of music being played in a store, status of a playback history of in a store, visiting customer gender status, visiting customer age group status, visiting customer behavior status, visiting customer stay time status, order status by menu, and interior status inside the cafe; Poppi teaches in 1) Table Vacant, Pg. 36, ¶ 2, ongoing low table activity is detected at the table, yet comparatively higher than when the table is vacant. Customers may be looking at menus or having a drink - which may increase the reported table activity. The backend server processes the collected data and validates it against a set of conditions. For example, if the table was previously vacant and the table is active for over 1 minute, customers can be said to have been seated.
and the predicted data includes data related to at least one of predicted changes in table or seat occupancy rates, predicted seat ambiance, predicted wait times, predicted noise changes, predicted music to be played in the store, predicted visiting customer gender, predicted visiting customer age group, predicted visiting customer behavior patterns, predicted visiting purpose, predicted menu rankings, predicted recommended menu, predicted sales, predicted inventory, and predicted cafe atmosphere; Poppi teaches in 3) Being Attended, Pg. 36, ¶ 4, The waiter will take the customer's orders and will have to stand next to the table to do so. In this example meal scenario, the waiter has forgotten to attend the customers. Here, the backend server is programmed to check whether a table has been forgotten and sends a reminder notification alert to the waiter's device. Determination of whether or not a table has been forgotten is made based on the time a customer is seated, i.e. the table activity status is moved from inactive to active, and a waiter being determined from proximity data as attending the table.
Claim 3:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Eyster are similar to Ok where Poppi, Eyster and Ok teach providing restaurant service to people, and Ok further teaches the following:
wherein the status data and the prediction data are anonymized within the artificial intelligence-based cafe monitoring device; Ok teaches in Pg. 3, ¶ 6, the software unit receives the captured image data using a predefined human behavior deep learning algorithm, outputs an analysis result for the user's lost state, health abnormality, and behavioral pattern for concealment, and outputs the analysis result According to the special task performance control unit for controlling the artificial intelligence chatbot unit and the self-driving control unit to perform the special task may be further included. Status data and prediction data are taught above in claim 2.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with an artificial-intelligence-based service robot of Ok to assist businesses with implementing artificial intelligence that includes chatbot service for ordering products, information request, or making payments (Ok, Spec. Pg. 3, ¶ 5).
Claim 4:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein the customer data includes data related to at least one of customer gender, customer age, customer location, service usage history, customer preferences, preferred menu items, cafe visit history, and predicted visit purpose; Poppi teaches in Interactions, Pg. 44, ¶ 2, The sensor units may also act as a mean of identifying and connecting customers, the tables they are seated at and the business. A customer could, for example, place their phone above a sensor unit which would trigger a notification on their phone. This would cause a notification to pop up and, when swiped, it would open the customer's browser with the options of ordering, paying, calling a waiter, leaving a review associated to the data collected during their meal etc.
Claim 5:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein: a first scheme includes a scheme that uses both the status data and the prediction data for cafe curation; Poppi teaches in Pg. 42, ¶ 6, and Pg. 43, ¶ 1, a floorspace map could be provided which shows which tables are in use, if customers have placed an order, if they have been waiting too long, predictions of how busy the restaurant will get throughout the day, etc. The intention here is to notify staff of anomalies and/or forgotten duties.
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Eyster are similar to Ok where Poppi, Eyster and Ok teach providing restaurant service to people, and Ok further teaches the following:
and when the response signal includes a first response signal, the cafe curation scheme determination module is configured to determine the first scheme as the cafe curation scheme; Ok teaches on Pg. 6, ¶ 5, the user information analyzer 260 may analyze and estimate the user's gender and age group and the user's head position included in the photographed image data based on image analysis technology. In other words, the current artificial intelligence chatbot service determines whether the user is male or female, and estimates whether the user is in his or her teens, 20s, or 30s, that is, the head position of the user can be estimated through a key estimation algorithm.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with an artificial-intelligence-based service robot of Ok to assist businesses with implementing artificial intelligence that includes chatbot service for ordering products, information request, or making payments (Ok, Spec. Pg. 3, ¶ 5).
Claim 6:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following
wherein: a second scheme includes a scheme that uses the status data but does not use the prediction data for cafe curation, and when the response signal includes a second response signal different from the first response signal, the cafe curation scheme determination module is configured to determine the second scheme as the cafe curation scheme; Poppi teaches on Pg. 5, ¶¶ 1 – 3, the communications module may permit the central activity monitor to transmit as well as to receive information. The central activity monitor may be configured to generate a service alert in dependence on the determined type of activity at the table. The service alert may be sent to one or more members of staff, in order to alert them that action is needed. For example, the central activity monitor may be arranged to transmit the service alert to a mobile communications device associated with a member of staff. This may help to ensure that prompt action is taken, leading to improved customer experience and more efficient usage of the tables. Alternatively or in addition, the service alert may be displayed on a user interface. The service alert may indicate an action to be taken by that member of staff. For example, the service alert may indicate that the member of staff should attend a particular table.
Claim 7:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein: a third scheme includes a scheme that does not use both the status data and the prediction data for cafe curation, and when the response signal includes a third response signal different from the first response signal and the second response signal, the cafe curation scheme determination module is configured to determine the third scheme as the cafe curation scheme; Poppi teaches on Pg. 34, ¶ 4, in a yet another embodiment, an inactivity threshold is calculated for each axis whereby the accelerometer collects a reading for each axis at the same time. For this embodiment these values are between -32767 and + 32767. This means that when completely still the accelerometer does not report (0, 0, 0), but has at least one non-zero value , e.g. a measurement of (0, 0, 12000). Here the reading in the z axis represents the tilt of the table.
Claim 8:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein: a fourth scheme includes a scheme that uses past cafe monitoring data collected by the cafe monitoring data collection module for cafe curation, and when the response signal includes the third response signal, the cafe curation scheme determination module is configured to determine the third scheme and the fourth scheme as the cafe curation scheme; Poppi teaches on Poppi teaches on Pg. 29, ¶ 6 and Pg. 30, ¶ 1, the vibration data collected by the accelerometer 402 is reduced before sending it for further processing. For example, the table vibration is averaged every 0.5 seconds of data collected. The difference between an empty table and an individual placing their arm on the table, can be distinguished statistically. The system determines a baseline when the table is inactive. If there is human intervention, this characteristic will be straightforward to identify. For example, if over the span of 20 seconds, this characteristic is determined as "Yes" every 5 seconds it is indicative of persons being present at the table, e.g. one or more people are sat at a table. Pg. 30, ¶ 2, if a table is vacant, it is useless to collect data as it would be a waste of storage space and battery life. If someone is seated at the table, the vibration level exceeds a background vibration level threshold and the sensor switches to a collect vibration data mode. Poppi teaches on Pg. 41, ¶ 1, If none of a sample axis' readings exceeds its threshold, the sample is discarded. This means only the relevant data to be transmitted from the sensor (i.e. only data representing human interaction).
Claim 9:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein: a fifth scheme includes a scheme that uses the customer data for curation, and the cafe curation scheme determination module is configured to determine at least one of the first scheme through the fourth scheme and the fifth scheme as the cafe curation scheme; Poppi teaches on Pg. 30, ¶¶ 3 – 4; distinguish between anomalous vibration data and table activity, one example being a customer leaving the table to attend the bathroom. The Kalman Filter, or similar filter, reduces the impact on the data from anomalous table activity. The process consists of collecting the vibration data, averaging the XYZ components of the accelerometer, determining a baseline threshold vibration below which vibration data will be discarded, averaged data stored and finally the Kalman filter is applied to determine the average level of activity over time, reducing further the generation of inaccuracies. The averaged XYZ component values yield a vibration level indicative of table activity. The averaged vibration level provides a characteristic which may be used to distinguish between the types of table activity. If, for example, over the span of 120 seconds the average level of table activity is 3, the table is vacant. If it is 10, someone is sat at the table. If it is 40, someone is eating at the table. More specifically, the sensor collects data and aggregates it at 10 second intervals. Two measures are derived from this: cumulative variance of readings and the number of readings collected above the inactivity threshold. Both these are used to validate the level of human activity with the table.
Claim 14:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
wherein the cafe list generation module is configured to provide prediction data related to the cafes included in the cafe list to the customer terminal, allowing the customer terminal to display the prediction data; Poppi teaches on Pg. 9, ¶¶ 2 – 3, the central activity monitor may be configured to communicate with another system, such as a point of sale system, an inventory management system, a reservations system, a check-in system or a self-service system, for example, using the appropriate application programming interface (API). For example, the central activity monitor may be able to link to a point of sale system's (API) in order to retrieve details of a customer's order. This may allow additional information to be made available to the system for use in analyzing activity. The central activity monitor may be arranged to receive an input from another system, and the processor may be configured to determine the type of activity at the table based additionally on the input from the other system. For example, the number of courses which have been ordered may be used in determining the type of activity.
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Eyster are similar to Ok where Poppi, Eyster and Ok teach providing restaurant service to people, and Ok further teaches the following:
on the screen in the form of at least one of text, icons, images, and videos; Ok teaches on Pg. 4, ¶ 5, the display unit 120 may also serve as a monitor that outputs and displays information such as video, image, web page, etc. necessary according to the result of the artificial intelligence chatbot service; Ok teaches in claim 1, a display unit installed in the body and outputting response text information.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with an artificial-intelligence-based service robot of Ok to assist businesses with implementing artificial intelligence that includes chatbot service for ordering products, information request, or making payments (Ok, Spec. Pg. 3, ¶ 5).
Claim 18:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
allowing the customer terminal to display the prediction data on the screen; Poppi teaches on Pg. 44, ¶ 6, if the state of a table changed from "table vacant" to "activity detected", the machine learning processor 832 retrieves the data from the database 812, determines the probability that a person has just sat down, and stores the result in the database 812. The portal 828 is then able to display the result.
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Ok are similar to Eyster where Poppi, Ok, and Eyster teach providing restaurant service to people, and Eyster further teaches the following:
providing the prediction data related to the cafes included in the cafe list to the customer terminal, in the form of at least one of text, icons, images, and videos; Eyster teaches in ¶ 0045, the invention can employ deep learning systems such as a neural network which, given an image of a dish, drink, or ingredient, can determine the flavors it has. The neural network can be trained on a large dataset which maps images of foods with their flavors. For example, a dataset may include an image of a steak labeled with the flavor savory, or an image of a peach cobbler labeled with the flavor sweet (or fruit). In a preferred implementation, a convolutional neural network is trained on a dataset of food images mapped to multiple flavors, so that given a new image of a dish, the network can identify its primary flavor, and possibly one or more secondary flavors. Eyster teaches in ¶ 0046, a convolution neural network is a specific type of feed-forward neural network based on animal visual perception, and so is particularly useful in processing image data. Convolutional neural networks are similar to ordinary neural networks but are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. The whole network can still express a single differentiable score function. The image data can be harvested from social media outlets, such as Instagram, Twitter, and Facebook, where users generally only take photos of meals they enjoyed. Employing this thought process, images presented by a user would increase the likelihood that the user enjoyed the meal. Furthermore, images of groups of individuals enjoying a meal at a particular restaurant increases the likelihood that the group as a whole enjoyed the meal. Other kinds of neural networks can be employed, such as other feed-forward neural networks adapted to text input. A convolutional neural network can also be combined with a recurrent neural network. A deep-learning recurrent neural network known as a long short-term memory network combined with a convolutional neural network is known to improve automatic image captioning.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok with a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster to assist businesses with implementing customer preferences input into computer system to provide food and restaurant recommendations (Eyster, Spec. ¶¶ 0067 – 0068).
Claims 10 – 13, and 16 – 17, are rejected under 35 U.S.C. 103 as being unpatentable over Poppi, Giacomo (WO2018142107A1) hereinafter “Poppi” in view of Ok, Jae Yun (WO2023068737A1) hereinafter “Ok” in view of Eyster, Justin et al. (U.S. Publication No. 2018/0293489) hereinafter “Eyster” in view of Donoho David Leigh et al. (JP 2025-504434 A) hereinafter “Donoho”.
Claim 10:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Ok are similar to Eyster where Poppi, Ok, and Eyster teach providing restaurant service to people, and Eyster further teaches the following:
generate the inside of the cafes included in the generated cafe list wherein: the cafe list generation module is configured to operate, to provide the inside of the cafes included in the cafe list to the customer terminal; Eyster teaches in ¶ 0011, individual preferences regarding a number of attributes are obtained and responses assessed, weighted according to relative importance of the attribute to provide a list of alternatives. One application is for the recommendation of restaurants to a group; Eyster teaches in ¶ 0068, process 70 begins with computer system 10 receiving the personal flavor profiles for multiple individuals (72). These personal flavor profiles may be generated pursuant to the process 60 of FIG. 6, and may be previously stored such as in the profiles database 50. Computer system 10 then receives a request from a user to recommend a dining venue for a particular group of the individuals (74). The user does not necessarily have to be one of the individuals; for example, a secretary or assistant to one of the individuals could make the request. The request identifies which individuals comprise the group. A group flavor profile is then created based on the selected individuals comprising the group (76). This step can be performed using one or more priorities for the different profile elements (78). In this implementation, the request/profiles may include non-food components, in which case current local data may be obtained such as weather outlook, traffic conditions, and locations of the individuals making up the particular group in question (80). Computer system further receives multiple restaurant profiles which are to be analyzed for the recommendation (82). These restaurant profiles may also be previously stored such as in the profiles database 50. Computer system 10 can then find one or more matches of the group flavor profile to restaurant profiles (84). Match results are presented to the requestor with appropriate rankings (86). The user may optionally select one of the recommended restaurants that appears suitable (88), in which case computer system 10 can proceed to electronically make a reservation at the selected restaurant through any appropriate means such as an Internet interface, for example, the No wait reservation application (90). The reservation can be made for the number of people indicated by the group and at a time which allows a sufficient period for travel of the members of the group or as otherwise indicated by the user.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok with a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster to assist businesses with implementing customer preferences input into computer system to provide food and restaurant recommendations (Eyster, Spec. ¶¶ 0067 – 0068).
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor; and Ok teaches an artificial intelligence based service robot that can provide services such as ordering food and making a payment; Eyster teaches recommending restaurants; and Poppi, Ok, and Eyster are similar to Donoho where restaurant information is exchanged and Donoho further teaches the following:
further comprising a digital twin generation module configured to as a digital twin virtual space, wherein: in conjunction with the digital twin generation module in the form of the digital twin virtual space; Donoho teaches on Pg. 115, ¶ 5, Twin. A unique digital object is a permanent digital object that does not depend on representation in a legacy form of media, such as hardcopy paper, local digital files, or cloud files. To support legacy forms of information exchange and ensure backward compatibility, an embodiment of the present invention introduces the concept of a twin of a unique digital object. In an embodiment of the present invention, a unique digital object can acknowledge a proxy information object that is not a unique digital object. A proxy information object contains the same information as the unique digital object in a particular medium. A twin references a particular unique digital object by a unique identifier, enables legacy forms of information exchange, and allows the information in the unique digital object to be displayed in an alternative information representation system. Although a unique digital object is unique by definition, it can acknowledge any number of twins, each of which references a unique identifier. A twin can exist as a representation in a legacy form of information media, such as a hardcopy document, a local digital file, a cloud file, a shared ledger entry, or an alternative information reality, such as a virtual reality (VR) object or entity. For example, if one or more computers each store a local file in their local file system, this is a twin of a particular unique digital object.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with representation, exchange, verification and use of information of Donoho to assist businesses with implementing restaurant menus in a digital twin virtual reality environment for customers visiting the restaurant (Donoho Spec. Pg. 149, ¶ 3).
Claim 11:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor; and Ok teaches an artificial intelligence based service robot that can provide services such as ordering food and making a payment; Eyster teaches recommending restaurants; and Poppi, Ok, and Eyster are similar to Donoho where restaurant information is exchanged and Donoho further teaches the following:
wherein the digital twin generation module is configured to update the digital twin virtual space by reflecting changes in the status data; Donoho teaches on Pg. 112, ¶ 4, an outlet is a particular visualization of a unique digital object or collection of digital objects. An outlet may include a specifically rendered graphic that displays a unique identifier of the outlet (alternatively or in addition to a unique identifier of the information object it presents). As a unique digital object, an API that allows software interaction may be displayed. In one embodiment of the present invention, an outlet is represented by a unique digital object created by a computer program and separate from the presented digital object, and has a unique identifier. This allows for unique identification of a presentation of a particular digital object (targeted to a particular user, for a particular purpose, at a particular time, in a particular location) on a particular user interface (e.g., a hardcopy document, a virtual reality world, or a smartphone application); Donoho teaches on Pg. 165, ¶ 3, Figure 84C shows the user interface design for the Next Generation Document, summarizing the document's validity and reliability status in one symbol (bottom left) and listing the various high-level (or overall) concepts of validity, verifiability and reliability implemented by the Next Generation Document. Each high-level concept is either met (indicated by an appropriate icon) or not met. The summary status of the document's validity, verifiability and reliability can be displayed at any time on the document itself, and can also be updated in real time as the status changes. Donoho teaches above for claim 10, a twin can exist as a representation in a form of virtual reality.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with representation, exchange, verification and use of information of Donoho to assist businesses with implementing restaurant menus in a digital twin virtual reality environment for customers visiting the restaurant (Donoho Spec. Pg. 149, ¶ 3).
Claim 12:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
allowing the customer terminal; Poppi teaches on Pg. 23, ¶ 3, Region 206 shows a relatively constant vibration energy detected, with some variation about a horizontal trend. As a waiter approaches the table, 224, the waiter's proximity to table 224 is detected by the waiter's mobile device. The waiter then goes away from the table indicated by a loss of or low level of Bluetooth signal from the table 224 sensor. This most likely means the waiter has taken the order from the customers. The backend server is configured to identify proximity detection of a waiter going to a table having significant vibration energy and the loss of proximity detection as an interaction between the waiter and customers at the table comprising the taking of an order. In some embodiments Point of Sale data obtained during or just after period 206 may be inspected by the backend server to confirm whether or not an order has been placed. While the customers are waiting for the food to arrive, vibration energy is still detected and can originate from a number of sources such as a customer leaning on the table, moving the cutlery etc.; where vibration energy is sound measured by the accelerometer.
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Ok are similar to Eyster where Poppi, Ok, and Eyster teach providing restaurant service to people, and Eyster further teaches the following:
corresponding to the inside of the cafes included in the generated cafe list to the customer terminal; in claim 10 above, Eyster teaches that from ¶ 0068, recommending a list of restaurants to a group of customers where the customers can select a restaurant from the computer system.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok with a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster to assist businesses with implementing customer preferences input into computer system to provide food and restaurant recommendations (Eyster, Spec. ¶¶ 0067 – 0068).
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor; and Ok teaches an artificial intelligence based service robot that can provide services such as ordering food and making a payment; Eyster teaches recommending restaurants; and Poppi, Ok, and Eyster are similar to Donoho where restaurant information is exchanged and Donoho further teaches the following:
wherein the digital twin generation module is configured to provide sound data, to play the sound data during the rendering of the digital twin virtual space; Donoho teaches on Pg. 115, ¶ 5, access control and access tracking via jackets, outlets and twins Monetization of unique digital objects. This discipline allows easy monetization of access to digital content. All access to digital documents is monitored and the identifier of the device on which the access occurred is known. Upon request, applications can be developed to track and monetize access to digital objects such as books, articles, music, videos and images; Donoho further teaches the digital twin virtual space on Pg. 115, ¶ 5.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with representation, exchange, verification and use of information of Donoho to assist businesses with implementing restaurant menus in a digital twin virtual reality environment for customers visiting the restaurant (Donoho Spec. Pg. 149, ¶ 3).
Claim 13:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor; and Ok teaches an artificial intelligence based service robot that can provide services such as ordering food and making a payment; Eyster teaches recommending restaurants; and Poppi, Ok, and Eyster are similar to Donoho where restaurant information is exchanged and Donoho further teaches the following:
wherein the digital twin generation module is configured to update the sound data associated with the digital twin virtual space by reflecting changes in the status data; Donoho teaches on Pg. 160, ¶ 5, the NGD is implemented as a unique digital object with a payload that contains key document information in both human-readable and machine-readable formats. In a possible embodiment, the NGD also contains a user interface that allows document readers to interact with the document and perform actions such as sharing the document, access control, checking access logs, signing, signature requests and verifications, from the document itself, without having to "drag" the document into an external document system. In a possible embodiment, the NGD also contains a "timeline" of all document history, e.g., version updates, signature requests and signing events, attachment events, access events, etc.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with representation, exchange, verification and use of information of Donoho to assist businesses with implementing restaurant menus in a digital twin virtual reality environment for customers visiting the restaurant (Donoho Spec. Pg. 149, ¶ 3).
Claim 16:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi further teaches the following:
included in the cafe list to the customer terminal; Poppi teaches in Interface with other systems, Pg. 46, ¶ 3, the system may also be configured to handle third party application programming interfaces (AP ls). For example, if an order is placed on a restaurant's point of sale (PoS) system, the backend server 814 may link to the PoS's API 830 and retrieve the details of the order.
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor, and Poppi and Ok are similar to Eyster where Poppi, Ok, and Eyster teach providing restaurant service to people, and Eyster further teaches the following:
of the inside of the cafes included in the generated cafe list and providing the inside of the cafes; Eyster in ¶ 0068, Process 70 begins with computer system 10 receiving the personal flavor profiles for multiple individuals (72). These personal flavor profiles may be generated pursuant to the process 60 of Fig. 6, and may be previously stored such as in the profiles database 50. Computer system 10 then receives a request from a user to recommend a dining venue for a particular group of the individuals (74). The user does not necessarily have to be one of the individuals; for example, a secretary or assistant to one of the individuals could make the request. The request identifies which individuals comprise the group. A group flavor profile is then created based on the selected individuals comprising the group (76). This step can be performed using one or more priorities for the different profile elements (78). In this implementation, the request/profiles may include non-food components, in which case current local data may be obtained such as weather outlook, traffic conditions, and locations of the individuals making up the particular group in question (80). Computer system further receives multiple restaurant profiles which are to be analyzed for the recommendation (82). These restaurant profiles may also be previously stored such as in the profiles database 50. Computer system 10 can then find one or more matches of the group flavor profile to restaurant profiles (84). Match results are presented to the requestor with appropriate rankings (86). The user may optionally select one of the recommended restaurants that appears suitable (88), in which case computer system 10 can proceed to electronically make a reservation at the selected restaurant through any appropriate means such as an Internet interface, for example, the No wait reservation application (90). The reservation can be made for the number of people indicated by the group and at a time which allows a sufficient period for travel of the members of the group or as otherwise indicated by the user.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok with a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster to assist businesses with implementing customer preferences input into computer system to provide food and restaurant recommendations (Eyster, Spec. ¶¶ 0067 – 0068).
Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor; and Ok teaches an artificial intelligence based service robot that can provide services such as ordering food and making a payment; Eyster teaches recommending restaurants; and Poppi, Ok, and Eyster are similar to Donoho where restaurant information is exchanged and Donoho further teaches the following:
creating a digital twin virtual space; Donoho teaches on Pg. 155, ¶ 5, a manufacturer may choose to create a unique digital object by default to mirror each individual object they manufacture. The mirror object can act as a digital twin. In claim 10 above, Donoho teaches digital twin in virtual reality from Pg. 115, ¶ 4;
in the form of the digital twin virtual space; in claim 10 above Donoho teaches Pg. 115, ¶ 5, a digital twin virtual reality environment.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with representation, exchange, verification and use of information of Donoho to assist businesses with implementing restaurant menus in a digital twin virtual reality environment for customers visiting the restaurant (Donoho Spec. Pg. 149, ¶ 3).
Claim 17:
Poppi, Ok, and Eyster teach claims 1 and 15. Poppi teaches restaurant and customer point of sale (POS), sensing table activity, vibration data, and central activity monitor; and Ok teaches an artificial intelligence based service robot that can provide services such as ordering food and making a payment; Eyster teaches recommending restaurants; and Poppi, Ok, and Eyster are similar to Donoho where restaurant information is exchanged and Donoho further teaches the following:
providing sound data related to the digital twin virtual space to the customer terminal; in claim 12 above, Donoho teaches on Pg. 115, ¶ 5, using digital objects such as music in the digital twin virtual reality environment.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a system for monitoring activity in a hospitality establishment comprising a plurality of tables of Poppi and an artificial-intelligence-based service robot of Ok and a system that facilitates the selection of a restaurant suitable for a group having disparate taste preferences of Eyster with representation, exchange, verification and use of information of Donoho to assist businesses with implementing restaurant menus in a digital twin virtual reality environment for customers visiting the restaurant (Donoho Spec. Pg. 149, ¶ 3).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference AthuluruTlrumala, GiriSrinivasaRao et al. (U.S. Publication No. 2016/0335686) discloses a real-time experience management method, system, and mobile device include checking in a person with an associated mobile device at a site.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor Beth Boswell can be reached at (571) 272-6737.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
04/18/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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