Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Election filed on 1/13/2026. Claims 1-18 are pending and have been examined. Claims 19-27 are withdrawn.
Election
Applicant’s election of Group I (claims 1-18) in the reply filed on 1/13/2026 is acknowledged. Because applicant did not distinctly and specifically point out any supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Therefore, claims 19-27 are withdrawn and have not been examined. Examiner recommends that Applicant cancel the withdrawn, unelected claims.
Information Disclosure Statement
The Information Disclosure Statement (IDS) field 8/16/2024 was received and has been considered.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-18 are directed to a process. Therefore, claims 1-18 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claim 1 recites at least the following limitations that are believed to recite an abstract idea:
receiving a plurality of usage inputs including an application, an application size, and an air parameter;
determining, using an application model, a plurality of input air conditions and a plurality of output air conditions for the application based on the plurality of usage inputs;
calculating, using a selection method, system parameters for a plurality of different air conditioning systems;
selecting, using an optimization model, at least one air conditioning system from the plurality of different air conditioning systems, the optimization model selecting the at least one air conditioning system using the system parameters for the plurality of different air conditioning systems based on one or more optimization targets; and
outputting results reflecting the selected at least one air conditioning system.
The above limitations recite the concept of a personalized product recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. marketing or sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-18 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
The method being a computer-implemented method executed by one or more processors
artificial-intelligence-based models
a module
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. For example, claims 3-5 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 2, 6-18 these claims are similar to the independent claims except that they recite the further additional elements of machine-learning-based models, further artificial-intelligence-based models, training/retraining models, a database, the internet. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
The method being a computer-implemented method executed by one or more processors
artificial-intelligence-based models
a module
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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.
Claim Rejection – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8 are rejected under 35 U.S.C. 102 as being anticipated by Kashimoto et al (WO 2024071257 A1), hereinafter Kashimoto.
Regarding Claim 1, Kashimoto discloses a method of selecting an air conditioning system, the method being a computer-implemented method executed by one or more processors (Kashimoto: [0047], [0050]), the method comprising:
receiving a plurality of usage inputs including an application [room], an application size [size], and an air parameter [shape/window location] (Kashimoto: “The input unit 24 receives input of spatial information relating to the room in which the user wishes to install the air conditioning equipment, and the location (installation location) in the room where the user wishes to install the air conditioning equipment.” [0053] – “the terminal control unit 21 may accept the shape of the room… The terminal control unit 21 may also accept input in square meters. Furthermore, the terminal control unit 21 accepts the user's selection of intended use from options such as LDK, DK, L, bedroom, study, or guest room in the intended use selection field. The terminal control unit 21 receives the selection of the room shape, input of the room size, and input of the room's purpose” [0055]– “display the position of the window that received input on the input reception screen shown in Figure 4B. Users can input the installation location of air conditioning equipment” [0057]);
determining, using an application model, a plurality of input air conditions and a plurality of output air conditions for the application based on the plurality of usage inputs (Kashimoto: “Based on spatial information, the control unit 11 calculates the required air conditioning capacity of the air conditioning equipment for the room in which the user wishes to install the air conditioning equipment,” [0066] – “The control unit 11 determines the corrected heat load in S8 or S9 to be the required air conditioning capacity for the recommended air conditioning equipment (S10)” [0065] – “Based on the shape of the room and the room's purpose included in the spatial information, the control unit 11 refers to the required function table 123 (see Figure 11) to identify the functions required … identifies the circulation airflow function as the necessary function, for example, if there are blind spots within the room from the air conditioning equipment, or if there is an open space or loft. … if the room's use is …a kitchen or bedroom (a specified use), a temperature sensing function may be identified as a necessary feature.” [0068]),
the application model being an artificial-intelligence-based model (Kashimoto: “The object detection model M4 is a learning model that detects objects contained in captured images, such as …neural network … outputs the positions of room corners, kitchen fixtures, furniture and other objects, and windows within the captured image, based on image features.” [0096] – “estimates the shape of the room based on the position of the room corners output by the object detection model” [0097] – “recommended installation locations may be identified using a learning model” [0087]);
calculating, using a selection module, system parameters for a plurality of different air conditioning systems (Kashimoto: “simulates the temperature distribution, humidity distribution, or airflow when each candidate air conditioning device is installed in a room where the user wishes to install the air conditioning device, based on spatial information, installation location, and the performance of the candidate air conditioning devices” [0088] – See also [0109-0111]);
selecting, using an optimization model, at least one air conditioning system from the plurality of different air conditioning systems, the optimization model selecting the at least one air conditioning system using the system parameters for the plurality of different air conditioning systems based on one or more optimization targets (Kashimoto: “a learning model that outputs the probability that each candidate air conditioning equipment will become a recommended air conditioning equipment when acquired spatial information and installation location are input.” [0072] – “outputs the probability that each candidate air conditioning unit will become a recommended air conditioning unit according to the recommended air conditioning unit model M1, then identifies the candidate air conditioning unit with the highest probability as the recommended air conditioning unit and transmits (outputs) it to the user terminal 2” [0077] “having received spatial information and installation location, identifies recommended air conditioning equipment to be purchased or leased by the user based on the received spatial information and installation location” [0046]),
the optimization model being an artificial-intelligence-based model (Kashimoto: “The recommended air conditioning equipment model M1 is generated, for example, using machine learning with a neural network.” [0075]); and
outputting results reflecting the selected at least one air conditioning system (Kashimoto: “The control unit 11 transmits information regarding the identified recommended air conditioning equipment to the user terminal 2. When user terminal 2 receives (acquires) information regarding recommended air conditioning equipment, it displays the acquired information on the recommended air conditioning equipment output screen. Specifically, as shown in Figure 6, the recommended air conditioning equipment output screen displays the external view, model name, sales price, and the reason for selecting the recommended air conditioning equipment (reason for recommendation).” [0061] – see Figure 6).
Regarding claim 2, Kashimoto discloses the method of claim 1, wherein the application model, the optimization model, or both is a machine-learning-based model (Kashimoto: “The recommended air conditioning equipment model M1 is generated, for example, using machine learning with a neural network.” [0075]).
Regarding claim 3, Kashimoto discloses the method of claim 1, wherein the system parameters includes air conditioning system size and resource requirements (Kashimoto: “extracts candidate air conditioning equipment that has an air conditioning capacity equal to or greater than the air conditioning capacity calculated in S12 … the control unit 11 may calculate the proportion of the area reached by the airflow within the room based on spatial information, installation position, airflow reach distance, and left-right airflow diffusion angle, and extract candidate air conditioning equipment for which this proportion is above a certain value …The control unit 11 extracts a candidate air conditioning unit (third candidate air conditioning unit) from the second candidate air conditioning unit that has an airflow reach distance greater than or equal to the distance calculated in S14, and whose left right angle is included within the left-right airflow diffusion angle (S15).” [0067] – “The estimated load field stores the estimated heat load per square meter. The control unit, for example, if the intended use is an LDK (living room, dining room, kitchen) and the floor is the top floor, will determine the approximate load to be 140 W/m2” [0064] – “The control unit 11 may also identify locations where an electrical outlet for an air conditioner exists as possible installation locations” [0097]).
Regarding claim 4, Kashimoto discloses the method of claim 3, wherein the resource requirements includes energy requirements (Kashimoto: “The estimated load field stores the estimated heat load per square meter. The control unit, for example, if the intended use is an LDK (living room, dining room, kitchen) and the floor is the top floor, will determine the approximate load to be 140 W/m2” [0064] – “The control unit 11 may also identify locations where an electrical outlet for an air conditioner exists as possible installation locations” [0097] – “The control unit 11 extracts a candidate air conditioning unit (third candidate air conditioning unit) from the second candidate air conditioning unit that has an airflow reach distance greater than or equal to the distance calculated in S14, and whose left right angle is included within the left-right airflow diffusion angle (S15).” [0067]).
Regarding claim 5, Kashimoto discloses the method of claim 1, wherein the air parameter is a location of the application (Kashimoto: “Users can select the direction corresponding to north from among the 16 cardinal directions. … accepts input regarding the type of building containing the room, the location of the room within the building (floor number), and the region in which the building is located” [0059-0060]– “display the position of the window that received input on the input reception screen shown in Figure 4B. Users can input the installation location of air conditioning equipment” [0057]).
Regarding claim 6, Kashimoto discloses the method of claim 1,
wherein selecting the at least one air conditioning system includes selecting two or more air conditioning systems from the plurality of different air conditioning systems using the optimization model, the optimization model selecting the two or more air conditioning systems from the plurality of different air conditioning systems using the system parameters for the plurality of different air conditioning systems based on the one or more optimization targets (Kashimoto: “the recommended air conditioning equipment output screen may display multiple air conditioning equipment as recommended equipment, ordered from highest to lowest recommendation level” [0061] – “outputs the probability that each candidate air conditioning unit will become a recommended air conditioning unit according to the recommended air conditioning unit model M1, then identifies the candidate air conditioning unit with the highest probability as the recommended air conditioning unit and transmits (outputs) it to the user terminal 2” [0077] “having received spatial information and installation location, identifies recommended air conditioning equipment to be purchased or leased by the user based on the received spatial information and installation location” [0046]),
wherein the method further comprises generating, using a generative comparison model, a narrative comparing the selected two or more air conditioning systems, the generative comparison model being an artificial-intelligence-based model, and wherein outputting the results includes outputting the two or more air conditioning systems with the narrative (Kashimoto: “the recommended air conditioning equipment output screen may display multiple air conditioning equipment as recommended equipment, ordered from highest to lowest recommendation level…as shown in Figure 6, the recommended air conditioning equipment output screen displays the external view, model name, sales price, and the reason for selecting the recommended air conditioning equipment (reason for recommendation).” [0061] – It is recognized that an output of the respective price and reasoning of each model constitutes a comparison of the two models.).
Regarding claim 7, Kashimoto discloses the method of claim 6, wherein the generative comparison model is a machine-learning-based model (Kashimoto: “The control unit 11 may also calculate the contribution of each piece of information included in the spatial information input to the recommended air conditioning equipment model M1, identify the information that contributed most to the output accuracy, and transmit (output) this information to the user terminal 2 as the reason for identifying the recommended air conditioning equipment. While there are no limitations on the method for calculating contribution, methods such as SHAP (SHapley Additive exPlanation), LIME (Local Interpretable Model-Agnostic Explanations), or attention mechanisms can be used.” [0077]).
Regarding claim 8, Kashimoto discloses the method of claim 6, wherein the narrative compares an upfront cost to procure and install each of the two or more air conditioning systems (Kashimoto: “the recommended air conditioning equipment output screen may display multiple air conditioning equipment as recommended equipment, ordered from highest to lowest recommendation level…as shown in Figure 6, the recommended air conditioning equipment output screen displays the external view, model name, sales price, and the reason for selecting the recommended air conditioning equipment (reason for recommendation).” [0061]).
Claim Rejection – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kashimoto, in view of Krishnan (AU 2015101191 A4).
Regarding claim 9, Kashimoto discloses the method of claim 6, wherein the method applies for air conditioning systems and other appliances (Kashimoto: [0022]), but does not teach that the narrative compares a total cost of ownership of each of the two or more appliances.
However, Krishnan teaches purchase recommendation techniques for powered appliances [Abstract], including that the narrative compares a total cost of ownership of each of the two or more appliances (Krishnan: “Using the data provided by the user, along with values returned from looking up central tables for annual energy use and energy price (if the user has not entered an energy price), a mathematical calculation is performed to determine for each model, the total ownership cost over the lifetime of the appliance (as specified by the user). This value will be referred to as "Lifetime Ownership Cost," and is the sum of the purchase price and the total energy use costs (at current energy prices) over the lifetime of the appliance for each model.” [0015] – See Figures 8-9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Kashimoto would continue to teach generating, using a generative comparison model, a narrative comparing the selected two or more air conditioning systems, except that now it would also teach that the narrative compares a total cost of ownership of each of the two or more appliances, according to the teachings of Krishnan. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to allow users to compare models of appliance (Krishnan: [0004]).
Regarding claim 10, Kashimoto/ Krishnan teach the method of claim 9, wherein the system parameters include air conditioning system size and resource requirements (Kashimoto: “extracts candidate air conditioning equipment that has an air conditioning capacity equal to or greater than the air conditioning capacity calculated in S12 … the control unit 11 may calculate the proportion of the area reached by the airflow within the room based on spatial information, installation position, airflow reach distance, and left-right airflow diffusion angle, and extract candidate air conditioning equipment for which this proportion is above a certain value …The control unit 11 extracts a candidate air conditioning unit (third candidate air conditioning unit) from the second candidate air conditioning unit that has an airflow reach distance greater than or equal to the distance calculated in S14, and whose left right angle is included within the left-right airflow diffusion angle (S15).” [0067] – “The estimated load field stores the estimated heat load per square meter. The control unit, for example, if the intended use is an LDK (living room, dining room, kitchen) and the floor is the top floor, will determine the approximate load to be 140 W/m2” [0064] – “The control unit 11 may also identify locations where an electrical outlet for an air conditioner exists as possible installation locations” [0097]),
Wherein Krishnan further teaches that the total cost of ownership of each of the two or more appliances is based on the resource requirements (Krishnan: “Using the data provided by the user, along with values returned from looking up central tables for annual energy use and energy price (if the user has not entered an energy price), a mathematical calculation is performed to determine for each model, the total ownership cost over the lifetime of the appliance (as specified by the user). This value will be referred to as "Lifetime Ownership Cost," and is the sum of the purchase price and the total energy use costs (at current energy prices) over the lifetime of the appliance for each model.” [0015] – See Figures 8-9), and
Wherein Kashimoto teaches that the appliances are air conditioning systems (Kashimoto: [0022]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Krishnan with Kashimoto for the reasons identified above with respect to claim 9.
Regarding claim 11, Kashimoto discloses the method of claim 6, wherein the method applies for air conditioning systems and other appliances (Kashimoto: [0022]), but does not teach that one of the two or more appliances is an appliance of a proprietor, and another one of the two or more appliances is an appliance of a competitor.
However, Krishnan teaches purchase recommendation techniques for powered appliances [Abstract], including that one of the two or more appliances is an appliance of a proprietor, and another one of the two or more appliances is an appliance of a competitor (Krishnan: “the visual interface could display the calculated fields in a form as outlined in Figure 8, wherein 801 and 802 combined form the Purchase Recommendation for each model, and wherein 803 and 804 are calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model. Figure 9 shows an alternative software embodiment, wherein the calculated fields are displayed in an online retailer's model comparison tools. 901 and 902 combined form the Purchase Recommendation, and 903 and 904 are calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model.” [0096-0097] – See Figures 8-9, where the models in each example are from competing manufacturers/brands.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Kashimoto would continue to teach generating, using a generative comparison model, a narrative comparing the selected two or more air conditioning systems, except that now it would also teach that one of the two or more appliances is an appliance of a proprietor, and another one of the two or more appliances is an appliance of a competitor, according to the teachings of Krishnan. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to allow users to compare models of appliance (Krishnan: [0004]).
Regarding claim 12, Kashimoto/Krishnan discloses the method of claim 11, wherein Krishnan teaches that the generative comparison model generates a narrative providing benefits of the air conditioning system of the proprietor relative to the air conditioning system of the competitor (Krishnan: “the visual interface could display the calculated fields in a form as outlined in Figure 8, wherein 801 and 802 combined form the Purchase Recommendation for each model, and wherein 803 and 804 are calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model. Figure 9 shows an alternative software embodiment, wherein the calculated fields are displayed in an online retailer's model comparison tools. 901 and 902 combined form the Purchase Recommendation, and 903 and 904 are calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model.” [0096-0097]),
wherein Kashimoto further teaches that the appliances are air conditioning systems (Kashimoto: [0022]) and that the generative comparison model is trained (Kashimoto: “The recommended air conditioning equipment model M1 is trained using training data that links spatial information and installation locations of previously purchased air conditioning equipment with the model names of the air conditioning equipment” [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kashimoto with Krishnan for the reasons identified above with respect to claim 11.
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kashimoto, in view of Krishnan, and further in view of Gee et al (US 20220284470 A1), hereinafter Gee.
Regarding claim 13, Kashimoto/Krishnan teach the method of claim 12, further comprising training the optimization model, the generative comparison model, or both by searching one or more data sources for air conditioning system selection strategies of the manufacturer (Kashimoto: “The recommended air conditioning equipment model M1 is trained using training data that links spatial information and installation locations of previously purchased air conditioning equipment with the model names of the air conditioning equipment” [0076] – “a database server or the like may be provided outside the information processing device 1, and the database may be read from the database server… The memory unit 12 stores the program P, the air conditioning equipment table 121, the estimated load table 122, the required function table 123, and the reason table 124.” [0047-0048] - “simulates the temperature distribution, humidity distribution, or airflow when each candidate air conditioning device is installed in a room where the user wishes to install the air conditioning device, based on spatial information, installation location, and the performance of the candidate air conditioning devices” [0088] – See also [0109-0111]),
Wherein Krishnan further teaches that the manufacturer is the competitor (Krishnan: “calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model.” [0097] – See Figures 8-9, where the models in each example are from competing manufacturers/brands),
but do not teach that the training is retraining.
However, Gee teaches ML recommendation systems [Abstract], including that the training is retraining (Gee: “ the retraining is asynchronous in time and, as such, may be triggered by asynchronous events such as the arrival of new testing data.” [0176] – “Once trained, the neural network 3204 can be further refined and retrained as new data arrives. The fine-tuning, in examples, can occur periodically or, in other examples, every time new data arrives.” [0454]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Kashimoto/Krishnan would continue to teach training the optimization model, the generative comparison model, or both by searching one or more data sources for air conditioning system selection strategies of the competitor, except that now it would also teach that the training is retraining, according to the teachings of Gee. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to provide optimal recommendations (Gee: [0008]).
Regarding claim 14, Kashimoto/Krishnan/Gee teach the method of claim 13, wherein the optimization model, the generative comparison model, or both is communicatively coupled to an internal database, the internal database being one of the one or more data sources for air conditioning system selection strategies of the manufacturer (Kashimoto: “The recommended air conditioning equipment model M1 is trained using training data that links spatial information and installation locations of previously purchased air conditioning equipment with the model names of the air conditioning equipment” [0076] – “a database server or the like may be provided outside the information processing device 1, and the database may be read from the database server… The memory unit 12 stores the program P, the air conditioning equipment table 121, the estimated load table 122, the required function table 123, and the reason table 124.” [0047-0048] - “simulates the temperature distribution, humidity distribution, or airflow when each candidate air conditioning device is installed in a room where the user wishes to install the air conditioning device, based on spatial information, installation location, and the performance of the candidate air conditioning devices” [0088] – See also [0109-0111]),
Wherein Krishnan further teaches that the manufacturer is the competitor (Krishnan: “calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model.” [0097] – See Figures 8-9, where the models in each example are from competing manufacturers/brands)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kashimoto with Krishnan for the reasons identified above with respect to claim 13.
Regarding claim 15, Kashimoto/Krishnan/Gee teach the method of claim 13, wherein the optimization model, the generative comparison model, or both is communicatively coupled to the internet, the optimization model, the generative comparison model, or both using publicly available data via the internet as one of the one or more data sources for air conditioning system selection strategies of the manufacturer (Kashimoto: “The recommended air conditioning equipment model M1 is trained using training data that links spatial information and installation locations of previously purchased air conditioning equipment with the model names of the air conditioning equipment” [0076] – “a database server or the like may be provided outside the information processing device 1, and the database may be read from the database server… The memory unit 12 stores the program P, the air conditioning equipment table 121, the estimated load table 122, the required function table 123, and the reason table 124.” [0047-0048] - “simulates the temperature distribution, humidity distribution, or airflow when each candidate air conditioning device is installed in a room where the user wishes to install the air conditioning device, based on spatial information, installation location, and the performance of the candidate air conditioning devices” [0088] -“The memory unit 22 stores an application program Pa that accepts spatial information input and presents recommended air conditioning equipment to the user. The application program Pa is provided to the user terminal 2, for example, using a storage medium 22a. The terminal control unit 21 of the user terminal 2 may also obtain the application program Pa using the internet and store it in the storage unit” [0051]– See also [0109-0111]),
Wherein Krishnan further teaches that the manufacturer is the competitor (Krishnan: “calculated measures of the Lifetime C02-e Emission Savings and Lifetime Ownership Cost Savings that the user could realise by purchasing a particular model.” [0097] – See Figures 8-9, where the models in each example are from competing manufacturers/brands)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kashimoto with Krishnan for the reasons identified above with respect to claim 13.
Claims 16 - 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kashimoto, in view of Gee.
Regarding claim 16, Kashimoto discloses the method of claim 6,
wherein the optimization model, the generative comparison model, or both is communicatively coupled to the internet (Kashimoto: “The memory unit 22 stores an application program Pa that accepts spatial information input and presents recommended air conditioning equipment to the user. The application program Pa is provided to the user terminal 2, for example, using a storage medium 22a. The terminal control unit 21 of the user terminal 2 may also obtain the application program Pa using the internet and store it in the storage unit” [0051]), and
wherein the method further comprises searching one or more data sources via the internet for data related to air conditioning systems and training the optimization model, the generative comparison model, or both using the data related to air conditioning systems (Kashimoto: “The recommended air conditioning equipment model M1 is trained using training data that links spatial information and installation locations of previously purchased air conditioning equipment with the model names of the air conditioning equipment” [0076] – “The memory unit 22 stores an application program Pa that accepts spatial information input and presents recommended air conditioning equipment to the user. The application program Pa is provided to the user terminal 2, for example, using a storage medium 22a. The terminal control unit 21 of the user terminal 2 may also obtain the application program Pa using the internet and store it in the storage unit” [0051]),
but does not teach that the training is retraining.
However, Gee teaches ML recommendation systems [Abstract], including that the training is retraining (Gee: “ the retraining is asynchronous in time and, as such, may be triggered by asynchronous events such as the arrival of new testing data.” [0176] – “Once trained, the neural network 3204 can be further refined and retrained as new data arrives. The fine-tuning, in examples, can occur periodically or, in other examples, every time new data arrives.” [0454]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Kashimoto would continue to teach training the optimization model, the generative comparison model, or both using the data related to air conditioning systems, except that now it would also teach that the training is retraining, according to the teachings of Gee. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to provide optimal recommendations (Gee: [0008]).
Regarding claim 17, Kashimoto/Gee teach the method of claim 16, wherein the data related to air conditioning systems includes regulatory data (Kashimoto: “the control unit 11 refers to the required function table 123 (see Figure 11) to identify the functions required for the recommended air conditioning equipment … The function field stores the functions that may be required for the recommended air conditioning equipment. The recommended conditions field stores the spatial information conditions required for each function to be used with the recommended air conditioning equipment.” [0068] – “The control unit 11 determines whether the building is a highly airtight and highly insulated house (S4), and if it is a highly airtight and highly insulated house (S4: YES), it corrects the heat load calculated in S3 by multiplying it by 0.8 (S5). If the house is not highly airtight and well insulated (S4: NO), the control unit 11 corrects the heat load by multiplying it by 1.0 (S6).” [0065]).
Regarding claim 18, Kashimoto/Gee teach the method of claim 16, wherein the narrative comparing the selected two or more air conditioning systems includes a trend derived from the data related to air conditioning systems (Kashimoto: “the recommended air conditioning equipment output screen may display multiple air conditioning equipment as recommended equipment, ordered from highest to lowest recommendation level…as shown in Figure 6, the recommended air conditioning equipment output screen displays the external view, model name, sales price, and the reason for selecting the recommended air conditioning equipment (reason for recommendation).” [0061] –“ a reason field and a condition field. The reason field stores a template that serves as the basis for the reason statement sent (output) to the user terminal 2.” [0069]– See Figure 12.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
CN117114813A discusses air conditioner recommendations based on factors including room size, which uses ML to perform calculations and finalized recommendations.
US20190114689A1 discusses machine learning recommendation systems that can recommend appliances including air conditioners to a user based on user inputs.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689