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 .
Notice to Applicant
The following is a Non-Final Office action, responsive to Applicant’s communication of 8/4/25, in which Applicant filed the application. Claims 1-18 are pending in this application and have been rejected below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/4/25 is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to an apparatus which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
An information processing … comprising:
…
acquiring, …, acquisition information,
wherein the acquisition information is at least a part of network information of distributing a prediction target container (Applicant’s FIG. 10, [0080] as published - The network information D15 includes… users “desires to acquire” beverages (e.g. water, juice, milk); or consumer information of age, gender, preferences, or distribution/inventory information on beverages),
the prediction target container is at least partially filled with a content,
the prediction target container at least in part represents a product,
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container with a same specification as the prediction target container; and
generating, based on the network information of distributing the prediction target container as input to a … model, a demand-supply information according to the network information of distributing the prediction target container (Applicant’s [0079], FIG. 10 as published states “ The training data acquisition unit 202A acquires training data D17 constituted by network information D15 of the containers 10 that are a training target and demand-supply information D16 of the containers 10. The training data D17 is data that is used as training data, verification data and test data in supervised learning. In addition, the demand-supply information D16 is data that is used as a ground truth label in supervised learning.”
wherein the … model has previously been trained ...according to a correlation between network information of a training target container and training demand-supply information, the training demand-supply information comprises training information of demand and supply of respective types of contents of the product that are filled in another container with the same specification as the training target container (Applicant’s FIG. 10, [0080] as published - The network information D15 constituting the input data of the training data D17 includes, as information acquired based on the container identification information D7 and the web access information D8 included in the information storage carrier 100 attached to each of the containers 10 that are a training target, the acquisition information D12 indicating the type of the content 11 that the consumer U7 of the product 12 constituted by the container 10 desires to acquire among the contents 11 with which a different container having the specification identical to that of the container 10 can be filled. [0088] as published “That is, the machine learning unit 202B generates a trained learning model D18 by inputting the plurality of sets of the training data D17 to the learning model D18 and training the learning model D18 with a correlation between the network information D15 and the demand-supply information D16 included in the training data D17. “).
As drafted, this is, under its broadest reasonable interpretation, directed to the Abstract idea groupings of “certain methods of organizing human activity” (commercial or legal interactions…marketing or sales activities or behaviors) and mathematical relationships, as here we are predicting beverages and customer information, where targets are predicted to be filled with customer desires for different products, based on demand and supply, by correlating different types of contents of products for another container with the “same specification”.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
An information processing device comprising:
a memory; and
a processor executing a computer-executable instructions that cause the information processing device to perform:
acquiring, based on web access information, acquisition information,
…
an information storage attaches to the prediction target container ([0039] as published states “The information storage carrier 100 is constituted by a code image such as a one-dimensional code or a two-dimensional code. In this case, a label on which the code image is printed may be attached to or wound around the container 10, or may be embedded inside the formed body of the container 10. In addition, the information storage carrier 100 is constituted by an electronic tag (IC tag) called RFID, or the like”),
the information storage comprises the web access information (Applicant’s [0038] as published states “As illustrated in FIG. 2, the container 10 is provided with an information storage carrier 100 capable of storing container identification information D7 and web access information D8... The web access information D8 includes, for example, a web address (URL) for accessing a web page provided by the web service, and link information for starting an application provided by the web service.),
generating, based on the network information of distributing the prediction target container as input to a trained machine learning model, a demand-supply information according to the network information of distributing the prediction target container,
wherein the trained machine learning model has previously been trained by machine learning according to a correlation between network information of a training target container and training demand-supply information, the training demand-supply information comprises training information of demand and supply of respective types of contents of the product that are filled in another container with the same specification as the training target container.
The additional elements, individually or in combination, of memory, processor executing instructions to perform the method, using “trained machine learning model”, where information storage (e.g. code, QR code) allow web access for receiving customer information, amount to no more than mere instructions to apply the exception [market demand forecasting] using a generic computer component (See MPEP 2106.05(f) – memory, processor, code to website, machine learning) and individually or in combination is consideration “field of use” (MPEP 2106.05h – trained machine learning). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system, units, and application, are MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and MPEP 2106.05h (field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
In addition, “information storage comprises the web access information” is considered conventional computer functions (See MPEP 2106.05(d)(II) - . Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claim 6 at step one is an apparatus, which is a statutory category. Claim 6 is rejected for similar reasons as claim 1 above. Claim 6 states there is “inference”, but Applicant’s [0090] as published states “prediction result (inference result)”’ [0126] as published “generating (inferring) from the network information D15 of the container 10 being a prediction target, the demand-supply information D16, so the “inference” is considered making a prediction/forecast similar to claim 1. Claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2 and step 2b, and in combination/individually at step 2a, prong two and step 2b - is considered MPEP 2106.05f – apply it [abstract idea] on a computer and field of use (MPEP 2106.05h) for the same reasons as above.
Independent Claim 7, 11 is directed to an apparatus at step 1, which is a statutory category. Claim 7, 11 is rejected for the same reasons as claim 1 and 6.
Independents Claims 8-10, 18 are directed to a method at step 1, which is a statutory category. Claims 8-10, 18 are rejected for the same reasons as claim 1, 6, and 7.
Claims 2-4, 12-14 narrow the abstract idea by having “either” distribution information OR inventory information describing or representing number of containers in different distribution stages. This is viewed as part of mathematical relationship, and as part of estimating demand and inventory needs in a business and supply chain. Notably, the claims are set up so that it is a prediction; so the claims 2-4, 12-14 are treated as “predictions” of inventory information. To extent a real-time inventory position is needed (in claim 3, 13), this is considered at step 2a, prong two and step 2B, apply it [abstract idea] on a computer (MPEP 2106.05f) and field of use (MPEP 2106.05h).
Claim 5 narrows the abstract idea by having EITHER demand data or supply data.
Claim 15 has additional elements, similar to those in claim 1, where “terminal device” of consumer gets web access through web service. This is considered, at step 2a, prong two and step 2B, apply it [abstract idea] on a computer (MPEP 2106.05f) and field of use (MPEP 2106.05h). At step 2B, it is also considered a conventional computer function (See MPEP 2106.05(d)(II) - . Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321).
Claims 16-17 narrow abstract idea, similar to claim 1, by acquiring information from a customer. Claim 17 further specifies consumer information includes “consumer position information”, which is interpreted as including location information. At step 2a, prong two and step 2B, similar to claim 1, use of web service and storage is considered, at step 2a, prong two and step 2B, apply it [abstract idea] on a computer (MPEP 2106.05f) and field of use (MPEP 2106.05h). considered, at step 2a, prong two and step 2B, apply it [abstract idea] on a computer (MPEP 2106.05f) and field of use (MPEP 2106.05h). At step 2B, it is also considered a conventional computer function (See MPEP 2106.05(d)(II) - . Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321).
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 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 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 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, 5-11, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto (JP 2022128099) and Borjian (US 2023/0297948).
Concerning claim 1, Yamamoto discloses:
An information processing device (Yamamoto – see page 2, Embodiments, 2nd paragraph - sales distribution information collection system 1 according to the present invention collects various types of information using electronic computers and personal digital assistants using electric information communication lines (for example, public telephone lines and Internet lines). , a computer or a personal digital assistant to calculate various kinds of information. In the sales distribution information collection system 1, the configuration including various means to be described later is configured by hardware or
software.) comprising:
Yamamoto discloses having computers configured by hardware or software (See page 2, Embodiments, 2nd-3rd paragraphs).
Borjian discloses:
a memory (Borjian see par 26 - Processor 222 may include one or more general or specific purpose processors to perform computation and control functions of system 210. see par 27 - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data. ); and
a processor executing a computer-executable instructions that cause the information processing device to perform (Borjian – see par 27 - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data. For example, memory 214 may store software modules that provide functionality when executed by processor 222):
acquiring, based on web access information, acquisition information (Yamamoto – see page 3, 2nd paragraph - The sales and distribution survey information collection means is for collecting sales and distribution survey information related to the flow of product distribution and desired sales of the product; see page 3, 3rd paragraph - Specifically, the sales and distribution survey information gathering means comprises, for example, a website for inputting and transmitting sales and distribution survey information based on the transmission related information attached to the product. A screen is provided for entering sales and distribution survey information; see page 4, 2nd paragraph - access to the means of collecting sales and distribution survey information; This is the URL of the home page for entering and transmitting the sales and distribution survey information, which is the communication information;),
wherein the acquisition information is at least a part of network information of distributing a prediction target container (Applicant’s FIG. 10, [0080] as published - The network information D15 constituting the input data of the training data D17 includes, as information acquired based on the container identification information D7 and the web access information D8 included in the information storage carrier 100 attached to each of the containers 10 that are a training target, the acquisition information D12 indicating the type of the content 11 that the consumer U7 of the product 12 constituted by the container 10 desires to acquire among the contents 11 with which a different container having the specification identical to that of the container 10 can be filled; so this information is consumer desires among things in FIG. 10 – beverages ; users “desires to acquire” beverages (e.g. water, juice, milk); or consumer information of age, gender, preferences, or distribution/inventory information on beverages
Yamamoto discloses based on broadest reasonable interpretation in light of the specification – see page 4, 2nd paragraph - PET bottled beverages are produced at a factory and shipped from the factory (S10). Then, at the stage of distribution, a label seal on which transmission-related information is described is affixed to the PET bottled beverage shipped from the factory by the information adding means (S12). The transmission-related information described on the label sticker includes individual identification information, which is different information for each PET bottled beverage, information that shows the location where this label sticker is attached, etc., and access to the means of collecting sales and distribution survey information; This is the URL of the home page for entering and transmitting the sales and distribution survey information, which is
the communication information; see page 4, 3rd to last paragraph - assumed information calculated in the case of PET bottled beverages is the desired quantity demanded and the desired price, but as described above, assumed information of other contents may be used. The estimated information calculated will be fed back to manufacturers, distribution stages, retailers, etc., and will be utilized for future product sales),
an information storage attaches to the prediction target container (Yamamoto – see page 4, 2nd paragraph - PET bottled beverages are produced at a factory and shipped from the factory (S10). Then, at the stage of distribution, a label seal on which transmission-related information is described is affixed to the PET bottled beverage shipped from the factory by the information adding means (S12); The transmission-related information described on the label sticker includes individual identification information, which is different information for each PET bottled beverage, information that shows the location where this label sticker is attached, etc., and access to the means of collecting sales and distribution survey information. This is the URL of the home page. see page 4, 5th paragraph - the sales and distribution survey information collecting means causes the purchaser's portable information terminal to display a screen for inputting sales and distribution survey information (specifically, it is a dedicated home page screen).),
the information storage comprises the web access information (Yamamoto see page 4, 5th paragraph - the sales and distribution survey information collecting means causes the purchaser's portable information terminal to display a screen for inputting sales and distribution survey information (specifically, it is a dedicated home page screen). Then, according to the display on the screen, the purchaser inputs the sales and distribution survey information, and transmits the input sales and distribution survey information to the sales and distribution survey information collecting means (S18).),
the prediction target container is at least partially filled with a content (Yamamoto - see page 4, 3rd to last paragraph - assumed information calculated in the case of PET bottled beverages is the desired quantity demanded and the desired price, but as described above, assumed information of other contents may be used. The estimated information calculated will be fed back to manufacturers, distribution stages, retailers, etc., and will be utilized for future product sales; see page 4, 2nd to last paragraph - According to such a sales distribution information collection system 1, it is possible to acquire information on future distribution and sales of products that reflects the wishes of purchasers)),
the prediction target container at least in part represents a product (Yamamoto – see page 3, 5th paragraph - the sales and distribution survey information may be a concept that includes information at the time of purchase by the purchaser of the product. This information at the time of purchase includes the purchaser's motivation for purchasing this product, information on other products compared at the time of purchase of this product; see page 4, 2nd paragraph – PET bottled beverages; see page 4, 6th paragraph - collected sales and distribution survey information is accumulated (S34).),
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container (Applicant’s [0062] as published states “The information acquisition unit 201A acquires acquisition information D12 indicating a type of the content 11 that the consumer U7 of the product 12 desires to acquire (hereinafter referred to as a “acquisition-desired content type”) among the contents 11 with which a different container having the specification identical to that of the container 10 (hereinafter referred to as an “identical-specification container”) can be filled, based on the container identification information D7 and the web access information D8 included in the information storage carrier 100 attached to the container 10 constituting the product 12 Yamamoto – see page 3, 4th paragraph - if the purchaser who purchased the product is a numerical value indicating whether the purchaser will purchase the product further at that
amount (it may be the desired repurchase price), it is meaningful as the desired price ; see page 4, 3rd to last paragraph - assumed information calculated in the case of PET bottled beverages is the desired quantity demanded and the desired price, but as described above, assumed information of other contents may be used. The estimated information calculated will be fed back to manufacturers, distribution stages, retailers, etc., and will be utilized for future product sales; see page 4, 2nd to last paragraph - According to such a sales distribution information collection system 1, it is possible to acquire information on future distribution and sales of products that reflects the wishes of purchasers) with a same specification as the prediction target container (Yamamoto – see page 3, 5th paragraph - information at the time of purchase includes the purchaser's motivation for purchasing this product, information on other products compared at the time of purchase of this product, information on other products referred to when purchasing this product, or viewing time of information on other products. etc. can be considered. More specifically, when purchasing information, for example, a purchaser who purchased a product from a vending machine tried to select other products in front of the vending machine before purchasing the product; see page 3, last paragraph – page 4, 1st paragraph – specific example of product is a drink in a PET bottle); and
Yamamoto discloses having drinks in a PET bottle as beverages (See page 3-4). Yamamoto also discloses that the survey includes “information on “other products” compared at the time of purchase of this product, information on other products referred to when purchasing this product, or viewing time of information on other products… or tried to select other products.” (See page 3, 5th paragraph). Since the bottles would be the ”same”, and information is gathered on “other” products, it appears Yamamoto discloses the limitations.
To any extent, Borjian also discloses:
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container “with a same specification as the prediction target container” (Borjian – see par 20 - Input features 102 can include features for the product/product line (e.g., sales data, demand data, and the like)). In some embodiments, input features 102 can include product features (e.g., lead time for stocking, pack size, minimum order quantity, other suitable supply chain product attributes, and the like), demand features (e.g., data that represents demand of the product, such as mean demand and standard deviation); see par 59 – trained model can scale to several different product types (e.g. products with different types of demand patterns); see par 74 - Based on sales and stock of the products, actual supply chain metric data (e.g., sales data, lost sales data, stock on hand data, and the like) can be observed while the policies are implemented. ).
Yamamoto and Borjian disclose:
generating, based on the network information of distributing the prediction target container as input to a trained machine learning model, a demand-supply information according to the network information of distributing the prediction target container (Applicant’s [0079], FIG. 10 as published states “ The training data acquisition unit 202A acquires training data D17 constituted by network information D15 of the containers 10 that are a training target and demand-supply information D16 of the containers 10. The training data D17 is data that is used as training data, verification data and test data in supervised learning. In addition, the demand-supply information D16 is data that is used as a ground truth label in supervised learning)
Borjian discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 34 - machine learning component 302 of FIG. 3 can be implemented by machine learning model 104 of FIG. 1. For example, a trained machine learning model may be configured to generate supply chain metric predictions based on a supply chain policy for product(s). In an example, training data 304 can include instances of product data (e.g., product features, demand data, and the like), supply chain policies for the product, historic supply chain metric data for the product (e.g., sales data, lost sales data, stock on hand data, and the like) and simulated supply chain metric data for the product; see par 46 - the data modeling software can perform a number of calculations to generate forecasts for the product (e.g., the simulated supply chain metric data), such as sales forecasts, inventory forecasts, and the like. For example, product forecasts for different supply chain policies (e.g., replenishment policies) can be generated, and a supply chain policy that achieves desired forecasts can be recommended for the product.),
wherein the trained machine learning model has previously been trained by machine learning according to a correlation between network information of a training target container and training demand-supply information, the training demand-supply information comprises training information of demand and supply of respective types of contents of the product that are filled in another container with the same specification as the training target container (Applicant’s FIG. 10, [0080] as published - The network information D15 constituting the input data of the training data D17 includes, as information acquired based on the container identification information D7 and the web access information D8 included in the information storage carrier 100 attached to each of the containers 10 that are a training target, the acquisition information D12 indicating the type of the content 11 that the consumer U7 of the product 12 constituted by the container 10 desires to acquire among the contents 11 with which a different container having the specification identical to that of the container 10 can be filled. [0088] as published “That is, the machine learning unit 202B generates a trained learning model D18 by inputting the plurality of sets of the training data D17 to the learning model D18 and training the learning model D18 with a correlation between the network information D15 and the demand-supply information D16 included in the training data D17. “
Borjian discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 16 - In addition, the trained model takes as input demand features and product features, which are readily available pieces of data, along with the candidate replenishment policies for the product. Accordingly, supply chain metric predictions can be quickly and efficiently computed by the trained model, enabling agile determination of replenishment policies (or adjustment of an existing replenishment policy). A selected replenishment policy can then be implemented. see par 64 - Each candidate supply chain policy can have corresponding supply chain metric predictions generated by the machine learning model. In some embodiments, demand features for the first product can include one or more of mean and standard deviation for product demand, and product features for the first product can include one or more of lead time for replenishment, pack size, and minimum order quantity. see par 54-55 - For example, a set of predicted supply chain metrics 112 can include a lost sales metric (e.g., lost sales over a period of time) and a stock on hand metric (e.g., stock on hand over a period of time). a frontier curve can be generated that maps lost sales metric values to stock on hand metric values. When graphically depicted, an example frontier curve can plot possible lost sales metric values on the x-axis and, for each possible lost sales metric value, its y-value can be the smallest stock on hand metric value over all candidate policies that achieves that lost sales metric value. In some implementations, policies can be ranked according to the stock on hand metric values that map to a given lost sales metric value (e.g., ranking a policy with a lowest stock on hand metric value first and remaining policies/values in descending order)).
Both Yamamoto and Borjian are analogous art as they are directed to estimating future sales/demand of products (Yamamoto Abstract; Borjian Abstract, par 20). Yamamoto discloses having computers configured by hardware or software (See page 2, Embodiments, 2nd-3rd paragraphs). Yamamoto discloses having drinks in a PET bottle as beverages (See page 3-4). Yamamoto also discloses that the survey includes “information on “other products” compared at the time of purchase of this product, information on other products referred to when purchasing this product, or viewing time of information on other products… or tried to select other products.” (See page 3, 5th paragraph). Borjian improves upon Yamamoto by disclosing memory with software executed by processors (See par 26-27) and predicting sales and stock of a plurality of products that include features of pack size (See par 20, 59, 74). One of ordinary skill in the art would be motivated to further include having memory with software executed by processors and predicting sales and stock of a plurality of products to efficiently improve upon the computer and consideration of other beverage products as disclosed in Yamamoto.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimated future distribution and sales in Yamamoto to further have memory with software executed by processors as well as scaling demand for multiple products and considering product features including pack size as disclosed in Borjian, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 5, Yamamoto and Borjian disclose:
The information processing device according to claim 1, wherein
the demand-supply information includes at least one of,
as information related to demand of the product, demand prediction information related to a demand prediction of the product (Yamamoto see page 4, 3rd to last paragraph - assumed information calculated in the case of PET bottled beverages is the desired quantity demanded and the desired price, but as described above, assumed information of other contents may be used. The estimated information calculated will be fed back to manufacturers, distribution stages, retailers, etc., and will be utilized for future product sales), and
as information related to supply of the product, supply plan information related to a supply plan of the product (Borjian see par 46 - the data modeling software can perform a number of calculations to generate forecasts for the product (e.g., the simulated supply chain metric data), such as sales forecasts, inventory forecasts, and the like. For example, product forecasts for different supply chain policies (e.g., replenishment policies) can be generated, and a supply chain policy that achieves desired forecasts can be recommended for the product; see par 74 - In some implementations, the functionality of FIG. 4 can be used to select and implement replenishment policies for numerous different kinds of products. Implementations of the replenishment policies includes triggering replenishment orders of the relevant products at defined times and of defined quantities (according to the policies)).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above.
Concerning independent claim 6, Yamamoto and Borjian disclose:
An inference device (Applicant’s [0090] as published states “prediction result (inference result)”’ [0126] as published “generating (inferring) from the network information D15 of the container 10 being a prediction target, the demand-supply information D16. Yamamoto discloses based on broadest reasonable interpretation in light of the specification – see page 2, Embodiments, 2nd paragraph - sales distribution information collection system 1 according to the present invention collects various types of information using electronic computers and personal digital assistants using electric information communication lines (for example, public telephone lines and Internet lines). , a computer or a personal digital assistant to calculate various kinds of information. In the sales distribution information collection system 1, the configuration including various means to be described later is configured by hardware or
software; See page 4, 3rd to last paragraph - The estimated information calculated will be fed back to manufacturers, distribution stages, retailers, etc., and will be utilized for future product sales.
see also Borjian – see par 21- machine learning model 104 can be configured to generate predicted supply chain metrics 112 using input features 102 that represent a given candidate supply chain policy and one or more product(s). In some embodiments, supply chain metrics 112 can be a predicted sales metric and/or a predicted stock on hand metric. see par 24 - FIG. 2 is a block diagram of a computer server/system 210 in accordance with embodiments.) comprising:
a memory (Borjian [same as cl. 1] - see par 26 - Processor 222 may include one or more general or specific purpose processors to perform computation and control functions of system 210. see par 27 - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data); and
a processor executing a computer-executable instructions that cause the inference device to perform (Borjian see par 26-27 [as in claim 1] - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data):
acquiring, based on web access information, acquisition information (Yamamoto – same as cl. 1 – see page 3, 2nd paragraph; see page 3, 3rd paragraph; see page 4, 2nd paragraph),
wherein the acquisition information comprises network information of distributing a prediction target container (Yamamoto same as cl. 1 – see page 4, 2nd paragraph; see page 4, 3rd to last paragraph),
an information storage attaches to the prediction target container (Yamamoto [same as cl. 1] – see page 4, 2nd paragraph; see page 4, 5th paragraph),
the information storage comprises the web access information (Yamamoto -same as cl. 1 - see page 4, 5th paragraph),
the prediction target container is at least partially filled with a content (Yamamoto - see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph),
the prediction target container at least in part represents a product (Yamamoto – see page 3, 5th paragraph; see page 4, 2nd paragraph – PET bottled beverages; see page 4, 6th paragraph),
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container (Yamamoto – same as cl.1 - see page 3, 4th paragraph; see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph) with a same specification as the prediction target container (Yamamoto –same as cl.1 - see page 3, 5th paragraph; see page 3, last paragraph – page 4, 1st paragraph; Borjian – see par 20; see par 59; see par 74); and
generating, based on the network information of distributing the prediction target container, a demand-supply information as inference according to the network information of distributing the prediction target container ([0090] as published states “The input layer 110 includes neurons the number of which is corresponding to the number of values of the network information D15 as input data, and each value of the network information D15 is input to each neuron. The output layer 112 includes neurons the number of which is corresponding to the number of values of the demand-supply information D16 as output data, and a prediction result (inference result) of the demand-supply information D16 for the network information D15 is output as output data.
Borjian - see par 34 - machine learning component 302 of FIG. 3 can be implemented by machine learning model 104 of FIG. 1. For example, a trained machine learning model may be configured to generate supply chain metric predictions based on a supply chain policy for product(s). In an example, training data 304 can include instances of product data (e.g., product features, demand data, and the like), supply chain policies for the product, historic supply chain metric data for the product (e.g., sales data, lost sales data, stock on hand data, and the like) and simulated supply chain metric data for the product; see par 36 - The design of machine learning component 302 can include any suitable machine learning model components (e.g., a neural network, support vector machine, specialized regression model, random forest classifier, gradient boosting classifier, and the like). For example, a neural network can be implemented along with a given cost function (e.g., for training/gradient calculation). The neural network can include any number of hidden layers; see par 46- the data modeling software can perform a number of calculations to generate forecasts for the product (e.g., the simulated supply chain metric data), such as sales forecasts, inventory forecasts, and the like. For example, product forecasts for different supply chain policies (e.g., replenishment policies) can be generated, and a supply chain policy that achieves desired forecasts can be recommended for the product),
wherein the demand-supply information comprises information of demand and supply of respective types of contents of the product that are filled in another container with the same specification as the prediction target container (Borjian [same as cl. 1] – see par 16; see par 64; see par 54-55).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above.
Concerning independent claim 7, Yamamoto and Borjian disclose:
A… device (Yamamoto – see page 2, Embodiments, 2nd paragraph - sales distribution information collection system 1 according to the present invention collects various types of information using electronic computers and personal digital assistants using electric information communication lines (for example, public telephone lines and Internet lines). , a computer or a personal digital assistant to calculate various kinds of information. In the sales distribution information collection system 1, the configuration including various means to be described later is configured by hardware or software).
Borjian discloses:
A “machine learning” device (Borjian – see par 18 - FIG. 1 illustrates a system for predicting supply chain policies using machine learning according to an example embodiment. System 100 includes input features 102, machine learning model 104; see par 7 - FIG. 2 illustrates a block diagram of a computing device operatively coupled to a prediction system) comprising:
Yamamoto and Borjian disclose:
a memory (Borjian [same as cl. 1] - see par 26 - Processor 222 may include one or more general or specific purpose processors to perform computation and control functions of system 210. see par 27 - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data); and
a processor executing computer-executable instructions that cause the machine learning device to perform (Borjian see par 26-27 [as in claim 1] - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data):
storing a plurality of sets of training data (Borjian see par 18 - FIG. 1 illustrates a system for predicting supply chain policies using machine learning according to an example embodiment. System 100 includes input features 102, machine learning model 104, database 106, simulator 108, training data 110; see par 30 - A database 217 is coupled to bus 212 to provide centralized storage for modules 216 and 218 and to store, for example, enterprise sales and product data, training data; see par 47 – training data 110 can include both observed data and simulated data; see par 49 - An example data preparation pipeline that leverages data modeling software and simulations (e.g., simulator 108) is further described to achieve such a diverse training set.),
wherein a set of training data of the plurality of sets of training data comprises network information of distributing a prediction target container (same as cl. 1, 6 - Borjian - see par 34; see par 36; see par 46) and demand-supply information (Borjian discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 34 - machine learning component 302 of FIG. 3 can be implemented by machine learning model 104 of FIG. 1. For example, a trained machine learning model may be configured to generate supply chain metric predictions based on a supply chain policy for product(s).),
the network information comprises acquisition information (Yamamoto same as cl. 1 – see page 3, 2nd paragraph; see page 3, 3rd paragraph; see page 4, 2nd paragraph; see page 4, 3rd to last paragraph),
the acquisition information is based on web access information (Yamamoto -same as cl. 1 - see page 4, 5th paragraph),
the web access information included in an information storage (Yamamoto see page 4, 5th paragraph),
the information storage attaches to the container (Yamamoto – see page 4, 2nd paragraph - PET bottled beverages are produced at a factory and shipped from the factory (S10). Then, at the stage of distribution, a label seal on which transmission-related information is described is affixed to the PET bottled beverage shipped from the factory by the information adding means (S12); The transmission-related information described on the label sticker includes individual identification information, which is different information for each PET bottled beverage, information that shows the location where this label sticker is attached, etc., and access to the means of collecting sales and distribution survey information. This is the URL of the home page. see page 4, 5th paragraph - the sales and distribution survey information collecting means causes the purchaser's portable information terminal to display a screen for inputting sales and distribution survey information (specifically, it is a dedicated home page screen),
the container is at least partially filled with a content (Yamamoto - see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph)
the container at least in part represents a product (Yamamoto – see page 3, 5th paragraph; see page 4, 2nd paragraph – PET bottled beverages; see page 4, 6th paragraph),
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container (Yamamoto – same as cl.1 - see page 3, 4th paragraph; see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph) with a same specification as the prediction target container (Yamamoto –same as cl.1 - see page 3, 5th paragraph; see page 3, last paragraph – page 4, 1st paragraph; Borjian – see par 20; see par 59; see par 74),
the demand-supply information comprises training information of demand and supply of respective types of contents of the product (Borjian discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 34 - machine learning component 302 of FIG. 3 can be implemented by machine learning model 104 of FIG. 1. For example, a trained machine learning model may be configured to generate supply chain metric predictions based on a supply chain policy for product(s). In an example, training data 304 can include instances of product data (e.g., product features, demand data, and the like), supply chain policies for the product, historic supply chain metric data for the product (e.g., sales data, lost sales data, stock on hand data, and the like) and simulated supply chain metric data for the product; see par 46 - the data modeling software can perform a number of calculations to generate forecasts for the product (e.g., the simulated supply chain metric data), such as sales forecasts, inventory forecasts, and the like. For example, product forecasts for different supply chain policies (e.g., replenishment policies) can be generated, and a supply chain policy that achieves desired forecasts can be recommended for the product) that are filled in another container with the same specification as the prediction target container (Borjian– see par 16 - In addition, the trained model takes as input demand features and product features, which are readily available pieces of data, along with the candidate replenishment policies for the product. Accordingly, supply chain metric predictions can be quickly and efficiently computed by the trained model, enabling agile determination of replenishment policies (or adjustment of an existing replenishment policy). see par 20 - In some embodiments, input features 102 can include product features (e.g., lead time for stocking, pack size, minimum order quantity, other suitable supply chain product attributes, and the like), demand features (e.g., data that represents demand of the product, such as mean demand and standard deviation; see par 64 - Each candidate supply chain policy can have corresponding supply chain metric predictions generated by the machine learning model. In some embodiments, demand features for the first product can include one or more of mean and standard deviation for product demand, and product features for the first product can include one or more of lead time for replenishment, pack size, and minimum order quantity.);
training, based on the set of training data as input, a machine learning model, wherein the set of training data comprises a correlation between the network information and the demand-supply information (Borjian discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 16 - In addition, the trained model takes as input demand features and product features, which are readily available pieces of data, along with the candidate replenishment policies for the product. Accordingly, supply chain metric predictions can be quickly and efficiently computed by the trained model, enabling agile determination of replenishment policies (or adjustment of an existing replenishment policy). A selected replenishment policy can then be implemented. see par 44 - Machine learning model 104 can be trained to generate predicted supply chain metrics 112 by training data 110. For example, simulator 108 can access historic enterprise data stored at database 106 and generate portions of training data 110. In some embodiments, training data 110 can include instances of product data (e.g., product features, demand data, and the like), supply chain policies for the product, historic supply chain metric data for the product (e.g., sales data, lost sales data, stock on hand data, and the like) and simulated supply chain metric data for the product; see par 64 - Each candidate supply chain policy can have corresponding supply chain metric predictions generated by the machine learning model. In some embodiments, demand features for the first product can include one or more of mean and standard deviation for product demand, and product features for the first product can include one or more of lead time for replenishment, pack size, and minimum order quantity. see par 54-55 [as in cl. 1]- For example, a set of predicted supply chain metrics 112 can include a lost sales metric (e.g., lost sales over a period of time) and a stock on hand metric (e.g., stock on hand over a period of time). a frontier curve can be generated that maps lost sales metric values to stock on hand metric values. When graphically depicted, an example frontier curve can plot possible lost sales metric values on the x-axis and, for each possible lost sales metric value, its y-value can be the smallest stock on hand metric value over all candidate policies that achieves that lost sales metric value. In some implementations, policies can be ranked); and
storing the machine learning model trained with the correlation (Borjian – see par 47 - raining data 110 can include both observed data and simulated data. For example, a portion of the supply chain policies for a product in the instances of training data 110 can be generated by the data modeling software of simulator 108 while another portion of the supply chain policies for the product can be policies implemented historically (e.g., stored at database 106). In another example, a portion of the supply chain metric data for the product in the instances of training data 110 can be generated by the data modeling software of simulator 108 while another portion of the supply chain metric data for the product can be historical/observed data (e.g., stored at database 106). see par 49 - training data 110 includes a diverse dataset representative of a larger population of data to provide machine learning model 104 improved predictive power. For example, a diverse training set can enable a trained machine learning model to predict the quality of different replenishment policies for items from different categories. An example data preparation pipeline that leverages data modeling software and simulations (e.g., simulator 108) is further described to achieve such a diverse training set.).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above.
Concerning independent claim 8, Yamamoto and Borjian disclose:
An information processing method (Yamamoto – see page 5, 2nd paragraph - according to the present invention, it is possible to provide a sales and distribution information collection system and a sales and distribution information collection method capable of acquiring information on future distribution and sales of products that reflects the wishes of purchasers) comprising.
The remaining limitations are the same as claim 1. The claim is obvious in light of Yamamoto and Borjian for the same reasons as in claim 1.
Concerning independent claim 9, Yamamoto and Borjian disclose:
An inference method (Yamamoto – see page 5, 2nd paragraph - according to the present invention, it is possible to provide a sales and distribution information collection system and a sales and distribution information collection method capable of acquiring information on future distribution and sales of products that reflects the wishes of purchasers), comprising:
The remaining limitations are the same as claim 6. The claim is obvious in light of Yamamoto and Borjian for the same reasons as in claim 1.
Concerning independent claim 10, Yamamoto and Borjian disclose:
A … method (Yamamoto – see page 5, 2nd paragraph - according to the present invention, it is possible to provide a sales and distribution information collection system and a sales and distribution information collection method capable of acquiring information on future distribution and sales of products that reflects the wishes of purchasers) comprising:
A “machine learning” method (Borjian – see par 18 - FIG. 1 illustrates a system for predicting supply chain policies using machine learning according to an example embodiment. System 100 includes input features 102, machine learning model 104; see par 7 - FIG. 2 illustrates a block diagram of a computing device operatively coupled to a prediction system).
The remaining limitations are the same as claim 7. The claim is obvious in light of Yamamoto and Borjian for the same reasons as in claim 1.
Concerning independent claim 11, Yamamoto and Borjian disclose:
An information processing device (Yamamoto – see page 2, Embodiments, 2nd paragraph - sales distribution information collection system 1 according to the present invention collects various types of information using electronic computers and personal digital assistants using electric information communication lines (for example, public telephone lines and Internet lines). , a computer or a personal digital assistant to calculate various kinds of information. In the sales distribution information collection system 1, the configuration including various means to be described later is configured by hardware or
software) comprising:
a memory (Borjian [same as cl. 1] - see par 26 - Processor 222 may include one or more general or specific purpose processors to perform computation and control functions of system 210. see par 27 - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data); and
a processor executing a computer-executable instructions that cause the information processing device to perform (Borjian see par 26-27 [as in claim 1] - System 210 may include memory 214 for storing information and instructions for execution by processor 222. Memory 214 may contain various components for retrieving, presenting, modifying, and storing data):
acquiring, based on web access information, acquisition information (Yamamoto – same as cl. 1 – see page 3, 2nd paragraph; see page 3, 3rd paragraph; see page 4, 2nd paragraph),
wherein the acquisition information comprises network information of distributing a prediction target container (Yamamoto same as cl. 1 – see page 4, 2nd paragraph; see page 4, 3rd to last paragraph),
an information storage attaches to the prediction target container (Yamamoto [same as cl. 1] – see page 4, 2nd paragraph; see page 4, 5th paragraph),
the information storage comprises the web access information (Yamamoto -same as cl. 1 - see page 4, 5th paragraph),
the prediction target container is at least partially filled with a content (Yamamoto - see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph),
the prediction target container at least in part represents a product (Yamamoto – see page 3, 5th paragraph; see page 4, 2nd paragraph – PET bottled beverages; see page 4, 6th paragraph),
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container (Yamamoto – same as cl.1 - see page 3, 4th paragraph; see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph) with a same specification as the prediction target container (Yamamoto –same as cl.1 - see page 3, 5th paragraph; see page 3, last paragraph – page 4, 1st paragraph – specific example of product is a drink in a PET bottle; Borjian – see par 20 - Input features 102 can include features for the product/product line (e.g., sales data, demand data, and the like)). In some embodiments, input features 102 can include product features (e.g., lead time for stocking, pack size, minimum order quantity, other suitable supply chain product attributes, and the like), demand features (e.g., data that represents demand of the product, such as mean demand and standard deviation); see par 59 – trained model can scale to several different product types (e.g. products with different types of demand patterns); see par 74 - Based on sales and stock of the products, actual supply chain metric data (e.g., sales data, lost sales data, stock on hand data, and the like) can be observed while the policies are implemented); and
storing, in a storage device, the acquisition information (Yamamoto – see page 3, 3rd paragraph - sales and distribution survey information input and sent via an electric communication line is accumulated and stored in, for example, a data server provided as sales and distribution survey information collecting means; see page 4, 6th paragraph - the operation of the assumed information calculation system (S30) composed of the sales and distribution survey information collection means and assumed information calculation means will be described in detail. The sales and distribution survey information collection means receives and collects sales and distribution information as described above (S32). The collected sales and distribution survey information is accumulated (S34);
see also Borjian – see par 45 - database 106 can include a multi-dimensional enterprise data model that stores product data at different dimensionalities and/or hierarchical levels, and the sophisticated data modeling software at simulator 108 can access this data to generate the simulated supply chain metric data.).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above.
Concerning claim 15, Yamamoto and Borjian disclose:
The information processing device according to claim 11, wherein
the acquiring further comprises acquiring, based on the web access information read from the information storage by using a terminal device used by the consumer, the acquisition information through a web service (Yamamoto – see page 3, 2nd paragraph - sales and distribution survey information collection means is for collecting sales and distribution survey information related to the flow of product distribution and desired sales of the product, based on the transmission-related information, by inputting the information via telecommunication lines. It consists of a computer, an electric communication line, a server on the electric communication line, a mobile information terminal (for example, a smart phone or a tablet terminal), and the like; see page 3, 3rd paragraph - Specifically, the sales and distribution survey information gathering means comprises, for example, a website for inputting and transmitting sales and distribution survey information based on the transmission related information attached to the product. A screen is provided for entering sales and distribution survey information; see page 4, 2nd paragraph - access to the means of collecting sales and distribution survey information; This is the URL of the home page for entering and transmitting the sales and distribution survey information, which is the communication information).
Concerning independent claim 18, Yamamoto and Borjian disclose:
An information processing method (Yamamoto – see page 5, 2nd paragraph - according to the present invention, it is possible to provide a sales and distribution information collection system and a sales and distribution information collection method capable of acquiring information on future distribution and sales of products that reflects the wishes of purchasers) comprising:
acquiring, based on web access information, acquisition information (Yamamoto – same as cl. 1 – see page 3, 2nd paragraph; see page 3, 3rd paragraph; see page 4, 2nd paragraph),
wherein the acquisition information comprises network information of distributing a prediction target container (Yamamoto same as cl. 1 – see page 4, 2nd paragraph; see page 4, 3rd to last paragraph),
an information storage attaches to the prediction target container (Yamamoto [same as cl. 1] – see page 4, 2nd paragraph; see page 4, 5th paragraph),
the information storage comprises the web access information (Yamamoto -same as cl. 1 - see page 4, 5th paragraph),
the prediction target container is at least partially filled with a content (Yamamoto - see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph),
the prediction target container at least in part represents a product (Yamamoto – see page 3, 5th paragraph; see page 4, 2nd paragraph – PET bottled beverages; see page 4, 6th paragraph),
the acquisition information indicates a type of content that a consumer of the product desires to acquire among contents that are fillable in a container (Yamamoto – same as cl.1 - see page 3, 4th paragraph; see page 4, 3rd to last paragraph; see page 4, 2nd to last paragraph ) with a same specification as the prediction target container (Yamamoto –same as cl.1 - see page 3, 5th paragraph; see page 3, last paragraph – page 4, 1st paragraph; Borjian – see par 20; see par 59; see par 74); and
storing, in a storage device, the acquisition information (Yamamoto – see page 3, 3rd paragraph - sales and distribution survey information input and sent via an electric communication line is accumulated and stored in, for example, a data server provided as sales and distribution survey information collecting means; see page 4, 6th paragraph - the operation of the assumed information calculation system (S30) composed of the sales and distribution survey information collection means and assumed information calculation means will be described in detail. The sales and distribution survey information collection means receives and collects sales and distribution information as described above (S32). The collected sales and distribution survey information is accumulated (S34);
see also Borjian – see par 45 - database 106 can include a multi-dimensional enterprise data model that stores product data at different dimensionalities and/or hierarchical levels, and the sophisticated data modeling software at simulator 108 can access this data to generate the simulated supply chain metric data.).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above.
Claims 2-4 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto (JP 2022128099) and Borjian (US 2023/0297948), as applied to claims 1, 5-11, 15, and 18 above, and further in view of Carrasco-Gallego, et al. " Closed-loop supply chains of reusable articles: a typology grounded on case studies," 2012, International Journal of Production Research, Vol. 50, No. 19, pages 5582-5596.
Concerning claims 2 and 12, Yamamoto discloses that bottled beverage estimated demand is fed back to manufacturers (See page 4).
Borjian disclose:
The information processing device according to claim 1, wherein
the network information further includes
container management information including at least either one of distribution information or inventory information (Borjian – see par 79 - Manufacturing system 680 manufactures items to be sent to inventory system 620 and provides transportation logistics to deliver the items to inventory system 620 using a truck 681 or some other transportation mechanisms. Manufacturing system 680 in one embodiment implements an ERP specialized computer system or a specialized manufacturing system that uses input from prediction system 670 to ascertain an amount of items to manufacture, inventory of resources that are used for the manufacturing, and the amount and timing of the delivery of items to inventory system 620.)
Yamamoto and Borjian discloses:
wherein the distribution information describes distribution of the container with the same specification as the prediction target container and said another container with the same specification as the training target container (Yamamoto – see page 4, 2nd paragraph – PET bottled beverages produced at a factory;
Borjian – See par 76, FIG. 6 – integrated manufacturing inventory system 600 with supply chain predictions; with system 100 of FIG. 1 generating replenishment orders; see par 34 - machine learning component 302 of FIG. 3 can be implemented by machine learning model 104 of FIG. 1. For example, a trained machine learning model may be configured to generate supply chain metric predictions based on a supply chain policy for product(s). In an example, training data 304 can include instances of product data (e.g., product features, demand data, and the like); see par 79 - Manufacturing system 680 manufactures items to be sent to inventory system 620 and provides transportation logistics to deliver the items to inventory system 620 using a truck 681 or some other transportation mechanisms. Manufacturing system 680 in one embodiment implements an ERP specialized computer system or a specialized manufacturing system that uses input from prediction system 670 to ascertain an amount of items to manufacture, inventory of resources that are used for the manufacturing, and the amount and timing of the delivery of items to inventory system 620), as being distributed through respective distribution stages of a plurality of distribution stages (Yamamoto – see page 4, 2nd to last paragraph - estimated information calculated will be fed back to manufacturers, distribution stages, retailers, etc., and will be utilized for future product sales.)
Carrasco discloses:
the inventory information describes a number of containers in respective distribution stages of the plurality of distribution stages (Carrasco – see page 5584, Section 3 - use the term reusable articles (RA) to refer to durable products intended to be used multiple times by different users in different locations of a supply-chain network. see page 5587, Section 3.2.3 - RA travel across the boundaries of the organisations that make up the supply-chain network, so in some stages of article life-cycle, the focal company (that owns the reusable articles) has very limited or no control over a part of the articles; see page 5593, 6th paragraph - Multi-depot networks are also concerned by a third challenge involving the periodical rebalancing of RA inventory between depots. As RA do not have to return to the original issuing depot, transhipments between depots
are needed from time to time in order to ensure that the inventory in each depot is sufficient to cope with its demand).
Yamamoto, Borjian, and Carrasco disclose for claim 12:
storing, in the storage device, the acquisition information and the container management information (Yamamoto – see page 4, 6th paragraph - the operation of the assumed information calculation system (S30) composed of the sales and distribution survey information collection means and assumed information calculation means will be described in detail. The sales and distribution survey information collection means receives and collects sales and distribution information as described above (S32). The collected sales and distribution survey information is accumulated (S34); see also Carrasco - see page 5593, 6th -7th paragraph - Multi-depot networks are also concerned by a third challenge involving the periodical rebalancing of RA inventory between depots. As RA do not have to return to the original issuing depot, transhipments between depots
are needed from time to time in order to ensure that the inventory in each depot is sufficient to cope with its demand; models for repositioning empty containers in
freight transport).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above. In addition, Yamamoto, Borjian, and Carrasco are analogous art as they are directed to fulfilling future sales/demand of products (Yamamoto Abstract; Borjian Abstract, par 20; Carrasco abstract, page 5592-5593, Section 5 (fulfill demand). Yamamoto discloses that bottled beverage estimated demand is fed back to manufacturers and having bottled beverages produced at a factory (See page 4). Borjian discloses having inventory of resources used for manufacturing (See par 79). Carrasco improves upon Yamamoto and Borjian by disclosing having reusable articles in a multi-depot network, and having transshipments to ensure inventory meets demand. One of ordinary skill in the art would be motivated to further include having reusable articles in a multi-depot network, and having transshipments to ensure inventory meets demand to efficiently improve upon the consideration of other bottled beverage products as disclosed in Yamamoto and the inventory of resources used for manufacturing in Borjian.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimated future distribution and sales in Yamamoto to further have scaling demand for multiple products and considering product features including pack size as disclosed in Borjian, and to further have transshipments to ensure inventory of reusable articles to meet demands as disclosed in Carrasco, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claims 3 and 13, Yamamoto and Borjian and Carrasco disclose:
The information processing device according to claim 2, wherein
the distribution information includes distribution position information of a position of the prediction target container in a distribution network (Carrasco - see page 5593, 6th paragraph - Multi-depot networks are also concerned by a third challenge involving the periodical rebalancing of RA inventory between depots. As RA do not have to return to the original issuing depot, transhipments between depots
are needed from time to time in order to ensure that the inventory in each depot is sufficient to cope with its demand), and
the inventory information includes inventory position information of a position of the container being in stock (Carrasco see page 5585, 3rd paragraph – abbreviations - returnable transportation items (RTI); returnable packaging materials (RPM);
. reusable products (RP); see page 5593, 7th paragraph - This challenge has already been solved in the academic literature: models for repositioning empty containers in freight transport are widely used in RTI contexts. However, the application of these models for balancing the inventory between depots remains typically limited to one particular class of RA (RTI), whereas their use could also be extended to other classes, such as RP organised according to a multi-depot network. As the three classes or RA share similar logistics characteristics, the results obtained for one particular class can be extended to other classes).
It would have been obvious to combine Yamamoto and Borjian and Carrasco for the same reasons as claim 1 above.
Concerning claims 4 and 14, Yamamoto and Borjian and Carrasco disclose:
The information processing device according to claim 2, wherein
the plurality of distribution stages comprises at least
a cleaning stage of cleaning the container (Carrasco – see page 5585, 1st paragraph – reconditioning involves cleaning; see page 5588, Section 3.5 – RA (returnable articles)… with facility with filling plant and sterilization unit),
a filling stage of filling the container with the content (Carrasco – see page 5585, 4th paragraph – RPM (returnable packaging materials)… are refillable glass bottles for beverages, kegs; see page 5588, Section 3.5 – RA (returnable articles)… with facility with filling plant and sterilization unit), and
a consumption stage of consuming, by the consumer, the content with which the container has been filled (Yamamoto see page 4, 3rd paragraph - At the final stage of the distribution stage, the PET bottled beverages are put into a vending machine that sells directly to the consumer, making them available for purchase by the purchaser. Then, the purchaser purchases the PET-bottled beverage from the vending machine (S16).
see also Borjian see par 19 - supply chain policies can be defined for a particular product (e.g., retail product), a product line, a particular product/product line and one or more locations (e.g., retail locations); see par 76 - can generate replenishment orders based on the selected replenishment policy from an inventory management system, the electronic order causing a reallocated amount of the retail item to be sent, using a transportation mechanism, from one or more warehouses to one or more retail stores as well as fulfilling the generated electronic order at one or more of the plurality of warehouses.
see also Carrasco – see page 5587, section 3.3 - Note also that there is a part of the supply chain when RA are at the ‘customer-use’ stage, which is unobservable for the focal organisation in charge of RA reconditioning).
It would have been obvious to combine Yamamoto and Borjian and Carrasco for the same reasons as claim 1 above.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto (JP 2022128099) and Borjian (US 2023/0297948), as applied to claims 1, 5-11, 15, and 18 above, and further in view of Okada (JP 2022011078).
Concerning claim 16, Yamamoto and Borjian disclose:
The information processing device according to claim 15, wherein the acquiring further comprises acquiring consumer information related to the consumer from the terminal device when the web service is accessed (Yamamoto – see page 3, 5th paragraph - information at the time of purchase includes the purchaser's motivation for purchasing this product, information on other products compared at the time of purchase of this product, information on other products referred to when purchasing this product, or viewing time of information on other products. etc. can be considered. More specifically, when purchasing information, for example, a purchaser who purchased a product from a vending machine tried to select other products in front of the vending machine before purchasing the product), and
the storing the acquisition information further comprises storing the acquisition information and the consumer information in the storage device (Applicant’s [0069] The position included in the consumer profile data D10 corresponds to consumer position information related to a position of the consumer U7. The consumer position information is recorded as coordinates such as the latitude and the longitude indicating a position at a specific time point (for example, when he/she has accessed to a web service) of the consumer U7. The consumer position information may indicate a position such as an address or a range of activity, and in this case, the consumer position information may be recorded by using a region or an area such as an administrative district or a mesh section.) Yamamoto – see page 3, 2nd to last paragraph - from the distribution route, it is also possible to calculate the expected amount of demand for the product or the appropriate price of the product for each region in sales in any region; – see page 4, 6th paragraph - the operation of the assumed information calculation system (S30) composed of the sales and distribution survey information collection means and assumed information calculation means will be described in detail. The sales and distribution survey information collection means receives and collects sales and distribution information as described above (S32). The collected sales and distribution survey information is accumulated (S34)).
In light of Okada being applied to claim 17, Okada also applied here:
The information processing device according to claim 15, wherein the acquiring further comprises acquiring consumer information related to the consumer from the terminal device when the web service is accessed (Okada – see page 2, last paragraph – page 3, 1st paragraph – first model 51 is machine learning model outputs beer information regarding beer beverage proposed to customer by inputting attributes of customer and answers to a plurality of questions; store contents of questions when taking a questionnaire; see page 3, 5th paragraph - The GPS receiving unit 27 is a receiver that receives GPS signals and acquires the position information of the terminal 2; see page 5, 2nd paragraph -3rd paragraphs – server transitions to input screen for terminal; accepts inputs including… age, gender, customer attributes, customer’s location, occupation; asks customer preferences and gets answers; see page 7, 1st paragraph - server 1 acquires location information (GPS signals, etc.) from the terminal 2).
It would have been obvious to combine Yamamoto and Borjian for the same reasons as claim 1 above. In addition, Yamamoto, Borjian, and Okada are analogous art as they are directed to fulfilling future interest/demand of customers (Yamamoto Abstract; Borjian Abstract, par 20; Okada Abstract, page 6 – estimate beer to be proposed based on customer attributes/interests/region). Yamamoto discloses calculating expected demand for product in a region and gets information from a survey from a customer (See page 3-4). Borjian disclose predicting related to retail stores (See par 76-77). Okada improves upon Yamamoto and Borjian by disclosing a user terminal that provides location/GPS information in addition to answers to questions regarding beverages (See page 3, 5, 7). One of ordinary skill in the art would be motivated to further include providing location/GPS information in addition to answers to questions regarding beverages to efficiently improve upon the receipt of customer information in a survey where different regions have sales as disclosed in Yamamoto and the multiple retail locations in Borjian.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimated future distribution and sales in Yamamoto to further have scaling demand for multiple products and considering product features including pack size as disclosed in Borjian, and to further have location/GPS information relayed from a customer device as disclosed in Okada, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 17, Yamamoto, Borjian, and Okada disclose:
The information processing device according to claim 16, wherein
the consumer information includes consumer position information a position of the consumer (Borjian – see par 19 - supply chain policies can be defined for a particular product (e.g., retail product), a product line, a particular product/product line and one or more locations (e.g., retail locations), or any other suitable combination of product(s) in retail settings. see par 80 - Retail locations/stores 601-604 for direct consumer sales exhibit volatile inventory patterns, for example due to random nature and external factors affecting sales;
Okada – see page 2, last paragraph – page 3, 1st paragraph – first model 51 is machine learning model outputs beer information regarding beer beverage proposed to customer by inputting attributes of customer and answers to a plurality of questions; store contents of questions when taking a questionnaire; see page 3, 5th paragraph - The GPS receiving unit 27 is a receiver that receives GPS signals and acquires the position information of the terminal 2; see page 5, 2nd paragraph -3rd paragraphs – server transitions to input screen for terminal; accepts inputs including… age, gender, customer attributes, customer’s location, occupation; asks customer preferences and gets answers; see page 7, 1st paragraph - server 1 acquires location information (GPS signals, etc.) from the terminal 2).
It would have been obvious to combine Yamamoto and Borjian and Okada for the same reasons as claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Devarakonda (US 2020/0210947) – directed to controlling inventory in a supply chain (See Abstract); supply chain 100 includes distribution center 108; . The distribution center 108 in turn may fulfill a sales order in delivering products to the customer 110. see par 214, FIG. 15 – communication components 2740 detect identifiers – e.g. one-dimensional bar codes (e.g. UPC); multi-dimensional bar codes such as QR codes
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
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, Anita Coupe can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
Prosecution Insights