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 .
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The disclosure is objected to because of the following informalities: Please resubmit revised specification showing new text underlined and deleted text with strikeouts. Appropriate correction is required.
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 1-15 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without adding significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to either a practical application of the abstract idea or significantly more than the abstract idea itself. Groupings of abstract ideas include: Mathematical Concepts, Mental Processes and Certain Methods of Organizing Human Activity.
Certain Methods of Organizing Human Activity include:
Fundamental economic principles or practices,
Commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and
Managing personal behavior or relationships or interaction between people (including social activities, teaching and following rules or instructions).
Mathematical Concepts
Mathematical relationships
Mathematical formulas
Mathematical calculations
Mental Processes
Concepts performed in the human mind (including an observation, evaluation, judgement, opinion)
Step 1
In the instant case, claim 8 is directed to a process. Analysis of claim 8 applies to analysis of claims 1-7 and 9-15.
Step 2A Revised (First Prong)
Determine whether claim 8 is directed to a judicial exception. Elements of an abstract idea are underlined. See Analysis.
Step 2A Revised (Second Prong)
Determine whether claim 8 has additional elements (in italics) integrated into a practical application:
a) requires an additional element or a combination of elements in the claim to apply, rely on, or use the judicial exception in a manger that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception; and
b) uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
See Analysis.
Step 2B (Revised)
In Step 2B, evaluate whether claim 8 recites additional elements that amount to an inventive concept that adds significantly more than the recited judicial exception. See Analysis.
Analysis
In Claim 8:
(Original) An information processing method comprising:
a step of acquiring, as product storage information, information including a product type, an effective time limit, and a quantity of a product before consumption, for each of users;
a step of determining a totaled quantity of the product and a totaled number of users each storing the product, for each of effective time limits, from the product storage information for users living within a predetermined range from a store selling the product, based on the product storage information for each of the users acquired at the step of acquiring and address information of the users; and
a step of outputting results determined in the step of determining.
Claim 8 executes methods that are directed to abstract ideas comprising processes that can be executed by a human while following a procedure that organizes human activity related to commercial interactions using conventional computing elements as disclosed in the instant specification.
No evidence of an improvement to the functioning of a computer, or to any other technology or technical field.
No evidence exists in the instant specification or claims of a particular machine.
No evidence exists of a transformation or reduction of a particular article to a different state or thing.
The claim does not go beyond generally linking the use of the judicial exception to a particular technological environment, e.g. processor, device.
Claim 8 does not recite additional elements that amount to inventive concepts that are “significantly more” than the recited judicial exception. Courts have routinely found conventional computer processing functions (e.g. sending/receiving data, formatting data, storing data, retrieving data, manipulating data, calculating, searching data, displaying data, organizing data) insignificant to transform an abstract idea into a patent-eligible invention. See Alice, 134 S. Ct. at 2360. As such, the claims amount to nothing significantly more than an instruction to implement the abstract idea across a generic computer network which is not enough to transform an abstract idea into a patent-eligible invention.
The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the steps when each is taken alone. That is, the steps involved in the recited process undertake their roles in performance of their activities according to their generic functionalities which are well-understood, routine and conventional. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Conclusion
Accordingly, the examiner concludes there are no meaningful limitations in claims 1-15 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7-11 and 15 are rejected under 35 USC 103 as being unpatentable over Kamadolli et al., US 2017/0109686 “Kamadolli,” in view of Winkle et al., US 2018/0211208 “Winkle.”
In Kamadolli see at least (underlined text is for emphasis):
Regarding claim 8: (Original) An information processing method comprising:
[Kamadolli: 0017] FIG. 1 depicts a system environment 100 for consumption pattern analysis according to one embodiment. As shown, system environment 100 includes cloud-based server 105. Cloud-based server 105 is configured to provide services to consumers, retailers, and manufacturers.
a step of acquiring, as product storage information, information including a product type, and a quantity of a product before consumption, for each of users;
[Kamadolli: 0017] … For example, cloud-based server 105 can transmit notifications to consumer device 140 when a product being monitored by an internet enabled device (e.g., internet enabled device 110-A, 110-B, or 110-C) is almost depleted. Please note: Monitored product is almost depleted qualifies as product storage information.
[Kamadolli: 0018] … As one example, internet enabled device 110-A can be a smart refrigerator which is capable of monitoring the contents of perishables goods (such as milk) stored within the refrigerator. The smart refrigerator can periodically transmit replenishment data such as the amount of milk that has been consumed or the amount of milk that is still remaining. The smart refrigerator can be capable of monitoring multiple products which are stored within the refrigerator. Please note: Perishable goods, e.g. Milk (dairy) qualifies as a product type.
[Kamadolli: 0023] … Internet enabled device 110 can transmit replenishment data as the amount of product that remains or the amount of product that has been consumed. For example, a smart refrigerator can include sensors (e.g., cameras or scales) to monitor the amount of milk (or other product stored in the refrigerator) that is remaining. Please note: Amount of milk or other product stored in the smart refrigerator qualifies as quantity of product before consumption.
a step of determining a totaled quantity of the product and a totaled number of users each storing the product, for each of effective time limits, from the product storage information for users living within a predetermined range from a store selling the product, based on the product storage information for each of the users acquired at the step of acquiring and address information of the users; and
Rejection is based in part upon teachings applied to claim 8 by Kamadolli and further upon the combination of Kamadolli-Winkle.
In Kamadolli see at least:
[Kamadolli: 0020] Cloud-based server 105 further includes decision engine 160. Decision engine 160 is configured to analyze consumer profiles to determine product consumption patterns on a per geo-location basis. In one embodiment, decision engine 160 can analyze the replenishment data received from internet enabled devices 110 and consumer profiles within consumer profiles 170 to determine a product consumption pattern. For example, a smart refrigerator can transmit replenishment data related to products stored within the smart refrigerator to decision engine 160. Decision engine 160 can determine the geo-location of the smart refrigerator and identify consumer profiles in consumer profiles 170 that are within a proximity of the geo-location of the smart refrigerator. Decision engine 160 can then determine the consumption pattern of a product being monitored within the refrigerator (such as milk) for the geo-location where the refrigerator is located. In some examples, the consumption pattern can be real-time since it is based on data being constantly received from the internet enabled devices.
[Kamadolli: 0021] Once the consumption pattern for the product has been determined, decision engine 160 can determine the amount of a given product that needs to be replenished in one or more geo-locations. For example, an analysis of all refrigerators within a geo-location can predict that 1000 gallons of milk is going to be purchased in the next week by consumers within that geolocation.
[Kamadolli: 0031] As shown, decision engine 160 can begin at step (1) by receiving replenishment data form an internet enabled device. The replenishment data can serve as real-time consumption data that is being received from the consumers. This allows retailers and manufacturers evaluate the needs of the customer in real-time. In one example, the replenishment data can be related to a product that is being generated by the manufacturers and sold by the retailers. After receiving the replenishment data, decision engine 160 can query customer profiles for consumption data associated with the product being monitored by the internet enabled device. Consumer profiles database 170 can store multiple consumer profiles. Each consumer profile can store metadata on a consumer's consumption of one or more products. For example, the metadata can include the customer's product purchase history or the customer's rate of consumption for that particular product. The consumer profile can be retrieved from consumer profiles database 170. In one example, the consumer profile can belong to the owner of internet enabled device 110. In other examples, multiple consumer profiles can be retrieved from consumer profiles database 170. Once the consumer profile(s) have been received, decision engine 160 can determine product consumption pattern for products per geo-location at step (3). Decision engine 160 can create multiple geo-locations. The geo-locations can be overlapping or non-overlapping. Decision engine 160 can calculate the consumption pattern for the product in each geo-location. The consumption pattern can be an estimate on the rate of consumption for the product in that particular geo-location. The product consumption patterns can be generated from the consumer profile(s) and optionally the real-time replenishment data. For example, consumer profile data can be aggregated based on geo-location to identify the demand for products at particular geo-locations. In some examples, real-time replenishment data can be weighted over customer profile data. Based on the consumption patterns, decision engine 160 can predict the demand for each product or product category at step (4). In one embodiment, demand for each product category can be confined to the geo-location. Based on the demand, decision engine 160 can predict the products that are to be replenished in each geo-location at step (5). Analysis can be performed on the demand per geo-location to provide valuable insights to manufacturers at step (6) and to retailers at step (7).
Please note: Regarding address, geo-location-aware systems utilize an actual address, e.g. street, town/city and zip code information, and/or GPS coordinates and/or triangulation. Please see below Winkle regarding delivery address information.
In Kamadolli:
a) monitors product(s) stored in a customer’s smart refrigerator in a geo-location,
b) receives product replenishment data from one or more customers in a geographic region,
c) aggregates consumer profile data based on geo-location to identify the demand for products at particular geo-locations,
d) predicts that a product will be depleted within a predefined period of time, and
e) providing valuable insight to manufacturers and retailers for product demand per geo-location,
Kamadolli, however, does not expressly mention techniques for using time limits for replenishment. Winkle on the other hand would have taught Kamadolli such techniques.
In Winkle see at least:
[Winkle: 0020] In one embodiment, the sensor data module 110 may be configured to manage and analyze data sensed by the multiple sensors (e.g., sensors 420) disposed in the residence at particular locations. The sensors may be disposed at or near a refrigerator, a kitchen, a kitchen cabinet, a pantry, a waste container, a recycling container, a laundry area, a garage, or other storage areas within the residence. The data sensed by the sensors may indicate freshness of an item, a quality of item, a temperature of item or surroundings, a usage data of item, a weight of item, a gas property indicative of an odor emitted by item, an expiration date of item, a machine-readable data affixed to item, a text affixed to item, a location of item within the residence, a shape of an item, and the like. The sensors disposed in the residence may include a weight sensor, a pressure sensor, a temperature sensor, an off-gassing sensor, a color sensor, a moisture sensor, a location sensor, identifier sensors (e.g., optical label scanner/reader, RFID reader, etc.), image sensing devices, and other sensors. In one embodiment, the sensor data module 120 is included at a computing device (e.g., device 410) or a server (e.g., server 430).
[Winkle: 0021] In an example embodiment, one or more sensors are combined together in a container or box that a user can easily place in his residence at desired locations. In another embodiment, the sensors are provided as a “smart shelf” that can be installed in various storage units, for example, in a refrigerator, kitchen cabinet, pantry, and the like. Some embodiments include a sensor matrix including a first array of sensors and a second array of sensors disposed at various locations in the residence.
[Winkle: 0036] At step 208, the replenishment module 140 generates an alert at a user interface in response to determining that the item needs replenishment in step 206. The alert may include an item name. The alert may also include a reason for replenishment, such as, expired item, depleted item, occurrence of a temporal event, and the like.
[Winkle: 0037] In one embodiment, the replenishment module 140 is able to determine that an item needs to be replenished before the item is completely consumed, depleted, expended, exhausted or expired, and can alert a user regarding replenishment of the item. Hence, a user may not run out of any items in his residence.
[Winkle: 0047] … For example, regarding type of item, the third array of sensors may be configured to capture images and thereby read barcode labels, recognize text, or recognize color of the item, and/or the third array of sensors may detect RFID tags.
[Winkle: 0048] In one embodiment, the storage areas, such as a refrigerator, cabinets, pantry, etc., in the residence includes shelves to hold the items. The shelf may include a bottom surface and side surfaces, a first array of sensors arranged on the bottom surface of the shelf and configured to measure at least one of weight, pressure, temperature, and moisture, and a second array of sensors arranged on one or more side surfaces of the shelf and configured to measure at least one of weight, pressure, temperature, and moisture. An interface may be operatively coupled to the first and second arrays of sensors, and configured to transmit sensor data from the first and second arrays to a local or remote computing device.
[Winkle: 0071] … For example, exemplary storage device 524 can store one or more databases 526 for storing information, such as data sensed by the sensors 420, order data, item data, pickup and delivery addresses, and/or any other information to be used by embodiments of the system 100.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Winkle, which recommend replenishment when the food item has expired or is depleted, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Winkle to the teachings of Kamadolli would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Therefore Kamadolli-Winkle teach and/or suggest:
a step of acquiring, as product storage information, information including a product type, an effective time limit, and a quantity of a product before consumption, for each of users;
a step of outputting results determined in the step of determining.
Rejection is based upon the teachings and rationale applied to claim 8 by Kamadolli-Winkle and further upon Kamadolli-Winkle:
[Kamadolli: 0022] FIG. 2 depicts a system for assisting a consumer in purchasing products which need replenishment according to one embodiment. System 200 includes bid engine 150, internet enabled device 110, consumer profiles 170, consumer device 140, and local retailers 120-A and 120-B. Bid engine 150 is capable of processing replenishment data from internet enabled device 110 to identify one or more products being monitored that may require replenishment. Once the products have been identified, bid engine 150 can analyze local retailers 120-A and 120-B using predefined criteria and recommend a local retailer to purchase the product from. Exemplary criteria include lowest price, closest proximity to the consumer, closest proximity to the internet enabled device, or some combination of multiple criteria. The recommendation can be transmitted from bid engine 150 to consumer device 140 where the consumer can consume the recommendation. Please note:
[Kamadolli: 0041] User interface output devices 714 can include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices, etc. The display subsystem can be a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), or a projection device. In general, use of the term “output device” is intended to include all possible types of devices and mechanisms for outputting information from computer system 700.
Regarding claims 1 and 9: Rejections are based upon the teachings and rationale applied to claim 8 by Kamadolli-Winkle and further upon the combination of Kamadolli-Winkle regarding system level computing elements:
[Kamadolli: 0037] FIG. 7 depicts an exemplary computer system 700 according to an embodiment. Computer system 700 can be used to implement the various devices/systems described in the foregoing disclosure, such as the servers of server tier 104 and/or client devices 108(1)-108(N) of FIG. 1. As shown, computer system 700 includes one or more processors 702 that communicate with a number of peripheral devices via a bus subsystem 704. These peripheral devices include a storage subsystem 706 (comprising a memory subsystem 708 and a file storage subsystem 710), user interface input devices 712, user interface output devices 714, and a network interface subsystem 716.
Regarding claims 2, 3, 10 and 11: Rejections are based upon the teachings and rationale applied to claims 1 and 8 by Kamadolli-Winkle and further upon the combination of Kamadolli-Winkle regarding privilege information:
Please note:
Each user receives a best (lowest) price recommendation for a retailer in the user’s geo-location, see [Kamadolli: 0027]; and
Loyalty or rewards card to use in-store, see [Winkle: 0026].
Regarding claims 7 and 15: Rejections are based upon the teachings and rationale applied to claims 1 and 8 by Kamadolli-Winkle and further upon the combination of Kamadolli-Winkle regarding the product is an ingredient:
[Winkle: 0024] In an example embodiment, a user can input a recipe or a list of ingredients and amounts needed for a recipe via a user device (e.g., user device 450). In an example embodiment, a user can input or provide a recipe or a list of ingredients and amounts needed for a recipe via an in-home voice-assisted speaker system (such as various models of Amazon® Echo®, Google® Home® or other similar systems). The home monitoring system 100 can determine if the items on the ingredient list are available in the residence, and if the amount required is available in the residence. If the required amount of an item is not available, then an alert may be generated indicating to the user that a specific item needs replenishment.
Claims 4, 5, 12 and 13 are rejected under 35 USC 103 as being unpatentable over Kamadolli, US 2017/0109686, and Winkle, US 2018/0211208, applied to claims 1 and 8 further in view of Post et al., US 2023/0169456 “Post.”
Regarding claims 4 and 12: Rejections are based in part upon the teachings and rationale applied to claims 1 and 8 by Kamadolli-Winkle and further upon the combination of Kamadolli-Winkle-Post
In Kamadolli-Winkle see at least:
[Kamadolli: 0032] FIG. 4 depicts an example of a user interface presented on a consumer device according to one embodiment. As shown, user interface 400 is configured to notify the consumer of a milk product being monitored by an internet enabled device. User interface 400 includes a notification that the milk product needs to be replenished shortly. User interface 400 can present five local retailers as options where the desired milk product can be purchased. In one embodiment, selecting an option can result in user interface 400 being updated to present directions to the selected local retailer. In another embodiment, selecting the option can result in the consumer device reserving the products at the selected local retailer.
Although the Kamadolli-Winkle system and methods allow the consumer to reserve the products at the selected local retailer, Kamadolli-Winkle do not expressly mention techniques for requesting the product to be delivered to the store. Post on the other hand would have taught Kamadolli-Winkle such techniques.
In Post see at least:
[Post: 0030] As disclosed above, the methods are adaptable to perform e-commerce fulfilment within a store utilizing inventory present in the store, such as for in-store pickup or for shipping out of the store. Alternatively, the e-commerce fulfillment may be utilized for customer in-store pickup in which customers order online and choose to pick up in store and in which the items are shipped from a remote warehouse to the store upon the customer placing the order. The customer is then alerted when their order is ready at the store (in other words back of house e-commerce/last mile). Such e-commerce includes breaking down orders that are shipped to a store in a large batch container holding multiple orders and then segregating the individual orders and presenting the orders to the customer in the retail store. Please note: a) Subject matter supported in provisional specification paragraph [0006], and b) Customer order placement qualifies as a request to ship the customer’s order from the remote warehouse to the store for customer pickup.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Post, which request the product to be delivered to the store from a remote warehouse once the customer places an order, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of B to the teachings of A would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claims 5 and 13: Rejections are based upon the teachings and rationale applied to claims 4 and 12 by Kamadolli-Winkle-Post and further upon the combination of Kamadolli-Winkle-Post regarding batch orders recited above under Post paragraph [0030].
Claims 6 and 14 are rejected under 35 USC 103 as being unpatentable over Kamadolli, US 2017/0109686, Winkle, US 2018/0211208, and Post, US 2023/0169456, applied to claims 4 and 12 further in view of Suzuki et al., WO 2005/05496 “Suzuki” IDS filed November 5, 2024.
Rejections are based in part upon the teachings and rationale applied to claims 4 and 12 by Kamadolli-Winkle-Post and further upon the combination of Kamadolli-Winkle-Post-Suzuki. Although Kamadolli-Winkle-Post do not expressly mention techniques for adjusting inventory, Suzuki on the other hand would have taught Kamadolli-Winkle-Post such techniques.
In Suzuki see at least:
[Suzuki: 0010] In the present invention, an order lead time is calculated for at least each customer based on current order information and past order results, and the magnitude of the risk of shortage is determined based on the order lead time and the supply lead time. Is preferred. In this case, if the average value of the order lead time is longer than the supply lead time, it is determined that the risk of shortage is small, and if the average value of the order time is shorter than the supply lead time. It is preferable to determine that the shortage risk is large. According to this, it is possible to correctly evaluate the magnitude of the shortage risk. Further, a ratio of the order quantity to which the order lead time longer than the supply lead time is given is calculated. If the ratio is equal to or more than a predetermined threshold value, it is determined that the risk of out-of-stock is small, and if the ratio is less than the predetermined threshold value. If so, it is more preferable to judge that the risk of shortage is large. According to this, it is possible to more accurately evaluate the magnitude of the shortage risk.
[Suzuki: 0022] It is more preferable to evaluate the inventory risk as follows. First, for each product type, the ratio of orders received from the largest customer to the total order quantity (hereinafter referred to as the “maximum occupied customer ratio”) is calculated. As a result, if the most occupied customer ratio is less than the predetermined threshold value, the product is determined to be "low inventory risk". Conversely, if the most occupied customer ratio is equal to or more than the predetermined threshold value, The product is judged to have "high inventory risk". In other words, as shown in Fig. 3 (a), if the largest customer of the product is Company A, if the ratio of orders received by Company A's power is small and below the power threshold, `` the inventory risk is small '' As shown in FIG. 3 (b), when the ratio of orders from Company A is large and this is equal to or greater than the threshold, it is determined that “the inventory risk is large”. This is because when the ratio of the largest occupied customers is high, when the prospective production or prospective order is placed based on the forecast of the largest customer, if the number of confirmed orders is significantly small, the prospective production or the prospective order will be added to other products. This is due to the fact that it is not sufficiently allocated to a certain number of customers, resulting in considerable excess inventory. The specific threshold value of the maximum occupancy ratio is not particularly limited, and may be determined based on the characteristics of the industry. Incidentally, since the number of customers / the ratio of the most occupied customers naturally changes, the evaluation of the inventory risk can also change dynamically rather than fixedly for each type of product.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Suzuki, which calculate the ratio of orders received from the largest customer to the total order quantity (hereinafter referred to as the “maximum occupied customer ratio”) is calculated, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Suzuki to the teachings of Kamadolli-Winkle-Post would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
JP 2005056236 (El et al.) IDS filed November 5, 2024, “Method for Predicting Demand,” discloses:
The demand prediction method according to the present invention includes a reception step of receiving expiration date information of inventory in a refrigerator, and an inventory table storing expiration date information of inventory in each refrigerator based on the expiration date information received in the reception step. An update step for updating the demand, a demand prediction step for calculating a demand prediction value for each product based on the information in the inventory table updated in the update step, and a demand prediction value calculated in the demand prediction step. And a notification step of notifying to.
By configuring in this way, the inventory information in the refrigerator, which has been limited to the use only in each home until now, is managed collectively in the information center, and this information is aggregated and analyzed. Demand information can be distributed to grocery stores.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 May 14, 2026
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