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 .
1. Examiner’s amendment filed 08/03/2026 is entered. Claims 5 and 9 are canceled and new claims 16-19 are added. Claims 1-4, 6-8, 10-19 are pending for examination. Claims 2-4, 6-8, 16-19 depend from claim 1. Claims 11-13 depend from claim 10. Claims 1, 10, 14 and 15 are independent claims.
Claim Rejections - 35 USC § 101
2. 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 1-4, 6-8, 10-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-4, 6-8, 16-19 are to a system, clam 14 to a process, and claim 15 to manufacture, which are statutory (Step 1: Yes).
Step 2A Analysis:
1. (Currently Amended) An information processing system comprising:
(i) a storage unit that stores a user's purchase state of a product, purchase history information indicating products which have been purchased by the user before including information on a purchase date of the user for each product, and a model generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data; and
(ii) a management unit that manages the purchase state, and when information on the product for the user is read at a store, stores the purchase state of the product in the storage unit with the purchase state set to an unpurchased state,
(iii) wherein the management unit determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state, the management unit further determining a result when the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition,
(iv) wherein the management unit determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-15 recite abstract idea.
The highlighted limitations of claim 1, comprising “ (i) stores a user's purchase state of a product, purchase history information indicating products which have been purchased by the user before including information on a purchase date of the user for each product,; and (ii) a management that manages the purchase state, and when information on the product for the user is read at a store, stores the purchase state of the product in the storage unit with the purchase state set to an unpurchased state, (iii) wherein the management determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state, the management further determining a result when the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition, (iv) wherein the management determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state.. “, as recited, relates to a commercial activity of managing purchase states of an item in a store including updating the unpurchased state on fulfilment of a condition, and fall within the Certain Methods of Organizing Human Activity”. See MPEP 2106.04(a)(2), subsection II. Thus, claim 1 and its dependent claims 2-4, 6-8, 16-19 recite “Certain Methods of Organizing Human Activity”
The highlighted limitations comprising, “ (ii) management that manages the purchase state, when information on the product for the user is read at a store, stores the purchase state of the product with the purchase state set to an unpurchased state, ((iii) wherein the management determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state, the management further determining a result when the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition, and iv) wherein the management determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state. “, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. In these limitations, other than reciting “by a computer/ processor” nothing in the claim elements precludes the steps from practically being performed in the by a person in his mind and manually using a pen and paper. For example, but for the “by the computer/processor” language, the claim encompasses a person looking at data collected relating to a purchase state of a product whether an unpurchased and forming a simple judgement and manual step of updating the purchase state based on a condition is fulfilled and also determines a result from a model based on the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition. The mere nominal recitation of by a controller/processor does not take the claim limitations out of the mental process grouping. Thus, the claim 1 and its dependent claims 2-4, 6-8, 16-19 recite a mental process.
Since the other two independent claims 14 and 15 recite similar limitations as claim 1, they are analyzed on the same basis reciting, “ Certain Methods of Organizing Human Activity”, and “Mental Processes”.
Claims 10-13:
Regarding claim 10, its limitations, “ An information processing system comprising: a storage unit that stores a user's purchase state of a product: a management unit that manages the purchase state, and when information on the product for the user is read at a store, stores the purchase state of the product in the storage unit with the purchase state set to an unpurchased state, wherein the management unit determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state;”, are similar to the limitations discussed for claim 1 reciting mental process and certain methods of organizing human activity. Further, the additional limitations comprising, “ a receiving unit that receives a purchase instruction of a product; a determination unit that determines a purchase price of the product at a lowest selling price of the product in a predetermined period with reference to predetermined timing related to purchase of the product by referring to a first storage unit that stores information on a selling price of the product; and an output unit that outputs information on the purchase price determined by the determination unit.”; under their broadest reasonable interpretation, relate to purchase and selling activity falling within certain methods of organizing human activity. Thus, claim 10 with its dependent claims 11-13 recite “ Certain Methods of Organizing Human Activity”, and “Mental Processes”.
Since each of the claims 14, 6-8, 10-19 recite limitations falling under two separate groupings of abstract ideas, the Supreme Court (discussing Bilski v. Kappos, 561 U.S. 593 (2010)) has treated such claims in the same manner as claims reciting a single judicial exception. Accordingly, limitations considered under Certain Methods of Organizing Human Activity” and “Mental Processes” are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES)
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-4, 6-8, 10-19: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of using generic computer recited at a high level of generality implementing the steps: (i) a storage unit that stores a user's purchase state of a product, purchase history information indicating products which have been purchased by the user before including information on a purchase date of the user for each product, and a model generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data; (ii) a management unit that manages the purchase state, and when information on the product for the user is read at a store, stores the purchase state of the product in the storage unit with the purchase state set to an unpurchased state, (iii) wherein the management unit determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state, the management unit further determining a result when the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition, (iv) wherein the management unit determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state.
The limitations in steps “(i) a storage unit that stores a user's purchase state of a product, purchase history information indicating products which have been purchased by the user before including information on a purchase date of the user for each product, and a model generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data;”; recite storing data related to a user’s purchase state of a product which have been purchased with purchase details at a high level of generality (i.e. as a general means of storing purchase state of a product and purchase history information] and amounts to mere pre or post solution storing which is a form of insignificant extra‐solution activity. The limitations describing that the model is generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data, as recited, merely are non-functional descriptive subject matter. The claim does not recite an active step of generating a machine learning model using the purchase history information of each user and a product recommended for each user as learning data. Even if the limitations are amended to be an active step, the use of machine learning model is a nominal manner without providing details on improving the functioning of the machine learning algorithm generating the model. Thus, the limitations in step (i) do not integrate the abstract idea into a practical application, because they do not add any meaningful limits on practicing the abstract idea.
In the limitations in steps (ii), (iii) and (tv), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Regarding claim 10, it recites the additional elements similar to claim 1, and additional the elements comprising receiving purchase instruction of a product, and an output unit to output information on the purchase price, which are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and output. See MPEP 2106.05. These limitations are recited as being performed by a computer which is recited at a high level of generality and is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The other additional limitations comprising, “ a determination unit that determines a purchase price of the product at a lowest selling price of the product in a predetermined period with reference to predetermined timing related to purchase of the product by referring to a first storage unit that stores information on a selling price of the product”, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed individually and in combination, the additional elements in claims 1 and 10 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Since the other two independent claims 14 and 15 recite similar limitations as claim 1, they are analyzed on the same basis being directed to the judicial exception.
Dependent claims 2-4, 6-8, 11-13, merely expand the scope of claims 1 and 10 of storing data , managing data and . Limitations in claims 2-3, 4, 6-7, 11-13, 17, 18 recite non-functional descriptive data, and limitations in claims 4, 8, 16, 17, 18, and 19 recite storing data, output data, transmitting messages, which are insignificant extra-solution activity, , and making determinations, and generating messages which fall within “Mental Processes. Accordingly, even in combination, these additional elements in the dependent claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, even in combination, the additional elements in dependent claims 2-4, 6-8, 11-13, 16-19 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims 2-4, 6-8, 11-13, and 16-19 are directed to an abstract idea.
Even when viewed in combination, the additional elements in claims 1-4, 6-8, 10-19 do not integrate the recited judicial exception into a practical application, because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claims are directed to the judicial exception. (Step 2A: YES). Step 2A=Yes. Claims 1-4, 6-8, 10-19 are directed to abstract ideas.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-4, 6-8, 10-19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claim recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim.
As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-4, 6-8, 10-19 amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The use of a model generated based on machine learning using purchase history information of each user and a product recommended is reciting in a nominal manner without providing details reflecting improving the functioning of the machine learning model itself and amounts to simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
As per MPEP 2106 , a conclusion that an additional element or elements is/are extra-solution activity, or are well-understood, conventional and routine activity in step 2A should be re-evaluated in step 2B. Additional elements comprising of storing , receiving and outputting data, were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/ transmitting/ outputting/ presenting/storing data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting/ presenting/storing data .steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered in combination, these additional elements in claims 1-20 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO). Claims 1-4, 6-8, 10-19, as recited, are patent ineligible.
3. Prior art discussion:
Reference independent claims 1, 14, and 15 the best prior art Cheng et al. [US 20190325456 A1], hereinafter Cheng teaches a storage unit that stores a user's purchase state of a product [See Cheng para 0026], and herein the management unit determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state [paras 0026 and 0039 teach that if a condition such as the product is purchased the information is updated from the unpurchased state to purchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state [Se para 0026, “ A retailer of product 120 can provide information regarding purchase status and can update product database 115 when a purchase status is changed from unpurchased to purchased. A product servicer can provide information regarding servicing of product 120 for recording in product database 115.. ….[ 0039] In some embodiments, information pertaining to products purchased in association with a user account is stored locally on client device 102 or other client devices associated with the user account. In some embodiments, information pertaining to products purchased in association with the user account can be encrypted and stored on a server that is accessible by client device 102.’], and stores purchase history information indicating products which have been purchased by the user before, and the management unit determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state [See claim 4], but fails to disclose or render obvious, alone or combined, at least the limitations comprising, “ wherein the storage unit further stores a model generated based on machine learning using purchase history information of each user and a product recommended for each user as learning data, and the management unit determines a result when the purchase history information including the information on the purchase date of the user is input to the model as a product that satisfies the predetermined matching condition.”. Claims 2-4, 6-8, 16-19 depend from claim 1.
Reference independent claim 10, the best prior art Cheng et al. [US 20190325456 A1], hereinafter Cheng teaches a storage unit that stores a user's purchase state of a product, a management unit that manages the purchase state, and when information on the product for the user is read at a store, stores the purchase state of the product in the storage unit with the purchase state set to an unpurchased state, wherein the management unit determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state [See Cheng paras 0026, 0039, and claim 4 but fails to disclose or render obvious, alone or combined, at least the limitations comprising, “ determines a purchase price of the product at a lowest selling price of the product in a predetermined period with reference to predetermined timing related to purchase of the product by referring to a first storage unit that stores information on a selling price of the product; and an output unit that outputs information on the purchase price determined by the determination unit.”. Claims 11-13 depend from claim 10.
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(i) Kaddevarmuth et al. [US 20230169540 A1 ; see para 0083] describes using machine learning model or artificial intelligence to generate cross-product propensity to buy signals based on purchase history data and virtual shopping basket analysis which can be used for
identifying relevant content and recommendations.
(ii) Ross et al. cited in the Non-Final Rejection mailed 05/18/2026 [ US Patent 11,100, 524 B1; see claim 12] describes predictive machine learning models targets likelihood of purchasing one of the respective products from the set of products, and each of the component predictive machine learning models determines a respective one of the set of product purchase ranks, wherein the customer purchase history information for each of the plurality of customer records includes an initial product purchase from the set of products, and a date of the initial product purchase, and wherein the predictive machine learning model is continuously trained using updated customer profile data and updated customer purchase history information; and updating, by the processor, the plurality of customer records in the customer database to indicate whether the respective customer record is included in the target group or is included in the non-target group.
Foreign references:
(iii) WO 2022102141 A1 cited in the Non-Final Rejection mailed 05/18/2026 describes providing a storage product list in each storage device 100 and storing information such as the product ID and the purchase status, and the purchase status indicates either unpurchased or purchased. In this example, it is shown that four kinds of lunch boxes having the product IDs LB01, LB02, LB03 and LB04 are housed in the storage device 100a having the storage device ID ST001. The product 104 of LB01 and LB02 indicates that it has not been purchased and can be purchased at this time, and the product 104 of LB03 and LB04 indicates that it has been purchased and cannot be purchased at this time. The storage notification includes the data of the product barcode 118 of the stored product 104 and the used clerk ticket.
(iv) JP 7237393 B1 cited in the Non-Final Rejection mailed 05/18/2026 teaches that a management server after determining that a payment has been made for a purchased item it updates the purchase state information for the specific item.
NPL reference:
(v) C. Doi, M. Katagiri, T. Araki, D. Ikeda and H. Shigeno, "Family Structure Attribute Estimation Method for Product Recommendation System," 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taipei, Taiwan, 2017, pp. 167-173 retrieved from IP. COM on 09102026 [see Abstract] describes a method comprising using Random Forest, a machine learning method, employed to generate the model based on purchase histories for a product and suggesting products that reflect the family structure attributes of the consumer.
(vi) A. Khanna and R. Tomar, "IoT based interactive shopping ecosystem," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India, 2016, pp. 40-45, retrieved from IP. Com on 05122026, hereinafter Tomar cited in the Non-Final Rejection mailed 05/18/2026, describes , see page 41, describes that Cloud acts as the storage and processing unit, and all requests coming from the customer are addressed at the cloud end, wherein it also keeps track of all the stores located in a shopping complex along with the details of every product being available at respective stores. The cloud maintains database where information pertaining to every product such as product ID, product name, date of manufacture, name of manufacture, cost of the product, any special discount being offered on that product, shelf life, payment info and its RFID tag serial number and during the process of purchasing a product, scanned serial numbers are sent to the cloud along with their payment details and when the purchase has been made RFID readers are informed about it and asked to update the product status of respective products.
5. Allowability Note: If the independent claims are amended to overcome 35 USC 101 rejection, the claims can be placed in condition for allowance. All amendments would be subject to reconsideration and search.
Response to Arguments
6.1. 35 USC 101 rejection:
Applicant's arguments filed 08/03/2026, see pages 8-14 have been fully considered but they are not persuasive.
Step 2A, Prong One:
Examiner has reviewed and considered all the arguments on pages 9-10 but respectfully disagrees with the Applicant that the limitations of claim 1 does not recite “Certain Methods of Organizing Human Activity” and “mental Processes”. Step 2A, Prong One part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations of claim 1” (i) stores a user's purchase state of a product, purchase history information indicating products which have been purchased by the user before including information on a purchase date of the user for each product”, (ii) a management that manages the purchase state, and when information on the product for the user is read at a store, stores the purchase state of the product in the storage unit with the purchase state set to an unpurchased state, (iii) wherein the management determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state, the management further determining a result when the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition, (iv) wherein the management determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state.. “, as recited, relates to a commercial activity of managing purchase states of an item in a store including updating the unpurchased state on fulfilment of a condition so that to improve the convenience for users when purchase products [See Specification paras 0004 and 0005] , and fall within the Certain Methods of Organizing Human Activity”. See MPEP 2106.04(a)(2), subsection II. Thus, claim 1 and its dependent claims 2-4, 6-8, 16-19 do “set forth” and “describe” recite “Certain Methods of Organizing Human Activity” including sales activities for providing an improved experience to customers when they purchase products.
Also, the limitations, “ (ii) management that manages the purchase state, when information on the product for the user is read at a store, stores the purchase state of the product with the purchase state set to an unpurchased state, ((iii) wherein the management determines a product that satisfies a predetermined matching condition from among the products included in the purchase history information as a product to be delivered to the user with the purchase state set to the unpurchased state, the management further determining a result when the purchase history information including the information on the purchase date of the user is input to the model as the product that satisfies the predetermined matching condition, and iv) wherein the management determines whether or not a first condition has been satisfied after the product has been set to the unpurchased state, and when it is determined that the first condition has been satisfied, updates the purchase state of the product stored in the storage unit from the unpurchased state to a purchased state”, under their broadest reasonable interpretation, relate to management personnel evaluating the available data on products and managing the purchase state of the products based on the purchase history of the product to indicate an unpurchased state or purchased state based on the pre-determined conditions. Thus, the limitations of claim 1 fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Abstract Idea Groupings [R-07.2022] II. MENTAL PROCESSES: claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); • a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011.
Applicant’s arguments with reference to the use of a machine learning generated model is an additional element and that is e considered and analyzed under Step 2A, Prong Two. The limitations of claim 1 considered above, do “set forth” and “describe” an abstract idea falling under “Mental Process”.
Step 2A, Prong Two: Examiner has reviewed and considered all the arguments on pages 10-12 but respectfully disagrees with the Applicant that the limitations of claim 1 are not directed to an abstract idea.
Applicant’s reference to “Desjardins” has been considered but are not persuasive, because “Desjardins” explicitly states, “that in order that the limitations even including recitation of machine learning integrate the abstract idea into practical application, the claims must recite, “ improving the functioning of the machine learning model , cite reduced storage requirements, lower system complexity, and the prevention of “catastrophic forgetting”, which is not the case here. Instead , the claim limitations merely recite the use of machine learning in a nominal manner without providing any details reflecting “improving the functioning of the machine learning model , cite reduced storage requirements, lower system complexity, etc.” and the claim is directed to merely a business related providing convenience to the users when purchasing products.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Examiner listed all the additional elements, see para 2 above, and when analyzed per Step 2A, Prong Two analysis. The additional elements comprise recite storing data related to a user’s purchase state of a product which have been purchased with purchase details at a high level of generality (i.e. as a general means of storing purchase state of a product and purchase history information] and amounts to mere pre or post solution storing which is a form of insignificant extra‐solution activity. The limitations in steps (ii), (iii) and (tv), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
In view of the foregoing, Applicant’s arguments are not persuasive, and even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Same response to claim 10 applies.
Step 2B: Examiner has reviewed and considered all the arguments on pages 12-14 but respectfully disagrees with the Applicant that the limitations of claim 1 are recite “Significantly More” not directed to an abstract idea. Step 2B analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Applicant’s arguments, since the step “ (i) a storage unit that stores a user's purchase state of a product, purchase history information indicating products which have been purchased by the user before including information on a purchase date of the user for each product, and a model generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data;”; recites storing a model generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data, the step of storing is not an insignificant extra-solution activity and Berkheimer is not applicable. Examiner respectfully disagrees , because the storing step, as drafted, merely states storing purchase history related data and data related to a model. The limitations, “a model generated based on machine learning using the purchase history information of each user and a product recommended for each user as learning data”, as drafted, merely describes the model being stored as how it was generated and does not recite an active step of generating a model. Accordingly, under step 2A, Prong Two the storing step is recited at a high level of generality [ as a general means of storing purchase related data and a model]and amounts to mere post solution displaying, which is a form of insignificant extra‐solution activity. Therefore, it was re- evaluated under STEP 2B . The background recites that the computer processors are all conventional, and the specification does not provide any indication that the computer is anything other than a conventional. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data or storing or displaying data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the storing step is well‐understood, routine, conventional activity is supported under Berkheimer. The storing limitations does not recite, “Significantly More”.
Reference claim 10, Applicant’s arguments with reference to the limitations, “
a receiving unit that receives a purchase instruction of a product; a determination unit that determines a purchase price of the product at a lowest selling price of the product in a predetermined period with reference to predetermined timing related to purchase of the product by referring to a first storage unit that stores information on a selling price of the product; and an output unit that outputs information on the purchase price determined by the determination unit.”, have been considered which were analyzed under Step 2A. The limitations of receiving purchase instruction and output information amounts to mere data receiving and output recited at a high level of generality amounting to insignificant extra-solution activity. The limitations, “ a determination unit that determines a purchase price of the product at a lowest selling price of the product in a predetermined period with reference to predetermined timing related to purchase of the product by referring to a first storage unit that stores information on a selling price of the product;”, amount to merely determining a lowest selling price from the market for purchasing a product within a predetermined time, such dates range can be done manually by a person evaluating prices collected from the market within a specified time range and does not necessitate inextricable tie to computer technology because these steps can be carried out manually and is just performing the disembodied concept on a general purpose computer. These limitations do not integrate the abstract idea into a practical application or recite, “Significantly More”.
Applicant’s comparison of the limitations of claim 10 to Bascom is not persuasive. In Bascom the court held that claims contained ‘significantly more” than an abstract idea of ‘filtering content because they recited a separate point of novelty that is : installation of filtering tool at a specific location on Internet of the content, remote from end users, with customizable filtering features specific to each end user. Bascom does not apply in this case. In contrast the claims in the instant application do not provide an unconventional computer functionality or a technical improvement to network or hardware or software but the computer recited at a high generality merely implementing insignificant extra-solution activity and mental processes providing an improvement in convenience for users when purchasing products.
Applicant ha snot filed separate arguments for claims 14-15 and dependent claims.
In view of the foregoing, the rejection of all pending claims 1-4, 6-8, 10-19 under 35 USC 101 is sustainable and maintained.
6.2. Rejection of claims under 35 USC 102 and 35 USC 10.3
Applicant’s arguments, see pages 15-16 filed 08/03/2026 with respect to rejection of claims under 35 USC 103 and 35 USC 103 have been fully considered and are persuasive in view of the current amendments to the independent claims 1, 10, 14-15. The rejection of claims under 35 USC 103 and 35 USC 103 has been withdrawn.
Conclusion
7. Final Rejection:
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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