DETAILED ACTION
1. Claims 1-8 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made that this application is a 371 of PCT/JP2020/042529 filed 11/13/2020.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 5/4/23 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the Examiner has considered the IDS as to the merits.
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.
5. Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1 is directed to a statutory category as a machine, claim 7 is directed to a statutory category as a process, and claim 8 is directed to an article of manufacture as a non-transitory storage medium, which each represent a statutory category of invention. Therefore, claims 1-8 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1, 7 and 8 recite the abstract idea of optimizing a system based on given data which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper.
As per claim 1, and similarly recited in claims 7 and 8, the limitation of “estimate a state of the first digital twin after the first parameter is given;” and “optimize the first index value and the second index value” would be analogous to a person estimating a state value and then optimizing the value and thus fall under Mental Processes. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
That is, other than reciting a “processor” or “non-transitory storage medium” nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-6 further narrow the abstract ideas, identified in the independent claims.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. In Claim 1, the additional element of a “processor”, and the “non-transitory storage medium”, in claim 8, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claims 7 and 8 of “acquire a first index value of a first digital twin in a case where a first parameter is given;” and “acquire the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “acquire a first index value of a first digital twin in a case where a first parameter is given;” and “acquire the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin” are in claims 1, 7, and 8 alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-6 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1, 7, and 8 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1, the additional element of “processor”, and the “non-transitory storage medium”, in claim 8, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claims 7 and 8 of “acquire a first index value of a first digital twin in a case where a first parameter is given;” and “acquire the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) 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 mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “acquire a first index value of a first digital twin in a case where a first parameter is given;” and “acquire the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin” in claims 1, 7, and 8, alternatively can be viewed as an insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 2 recites the representation of the values being evaluated which merely narrows the abstract idea identified as a mental process.
Dependent claim 3 recites an additional value being evaluated which merely narrows the abstract idea identified as a mental process.
Dependent claim 4 recites an additional value of time being evaluated which merely narrows the abstract idea identified as a mental process.
Dependent claim 5 recites the representation of the values being evaluated which merely narrows the abstract idea identified as a mental process.
Dependent claim 6 recites an additional value being evaluated which merely narrows the abstract idea identified as a mental process.
v) Accordingly, claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
Claim Rejections - 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
6. Claims 1-8 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Yoshida, Takumi, and Toshiyuki Kaneda. "The ASSA project: an intelligent agent simulation of shop-around behavior." Agent-Based Approaches in Economic and Social Complex Systems VII: Post-Proceedings of The AESCS International Workshop 2012. Tokyo: Springer Japan, 2013.
Regarding Claim 1: The reference discloses An optimization device for optimizing a digital twin system including at least two digital twins, the optimization device comprising:
a memory; and at least one processor connected to the memory, wherein the processor is configured to (Page 4, Section 3, “System verification is used as the completeness of coding intuitively, or 'No Bug proof'. Agent simulators also got become complicated as computer softwares, so in such cases, software development framework should be applicable.”)
acquire a first index value of a first digital twin in a case where a first parameter is given; (Fig 3. Agent and their respective behavior represents the first digital twin and its respective parameter)
estimate a state of the first digital twin after the first parameter is given; (Page 4, “Impulse-Detour Action. Impulse-detour action is positioned opposite to improvisation. It is a planned route departure behavior, which is a higher-order function. The softmax method that uses Boltzmann distribution was used to model this behavior. The steps are as follows. First, when visiting a commercial district, locations that are different from the original destination are selected at random. Next, the values of the selected locations are calculated based on the distance between the current location and the selected locations, and the weight of each location (the weight for each selected location is the same as that given in the Detour at Plan-Making Action stage). Based on these values, the probability of selecting each location is determined using Boltzmann method.”)
acquire the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin; and (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.” See also Page 8, Section 4, “By employing ASSA as the simulation model we conducted simulation experiments and evaluated the experiments using the evaluation framework. The following two cases in Nagoya were applied: Asunal Kanayama, a three-storied shopping mall with 60 shops divide into 4 categories (28 commodity stores, 15 cafes and restaurants, 15 services and 2 wagons); and Osu district, a shopping street complex district with 685 shops falling into 8 categories (cafes and restaurants, grocers, household goods, electric appliances, clothing, parks and temples, second hands goods, and others). The latter is modeled as a network with 36 street-nodes.)”)
optimize the first index value and the second index value. (Page 6, Section 3.3, “In both the simulation and survey attention was paid to the degree of behavior and the scheduled plan deviate from the optimum geographical distance solution, and by comparing them, the behavior and planning characteristics of the model were validated. The similarity analysis of visit sequences in the previous section paid attention to visiting order and examined the degree of similarity between samples, whereas this analysis focuses on geographical distance and compared the degree of deviation from the optimum route distance.”)
Regarding Claim 2: The reference discloses The optimization device according to claim 1, wherein:
the first digital twin is a digital twin of one person; (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.”)
the second digital twin is a digital twin of an arbitrary store; (Page 8, Section 4, “By employing ASSA as the simulation model we conducted simulation experiments and evaluated the experiments using the evaluation framework. The following two cases in Nagoya were applied: Asunal Kanayama, a three-storied shopping mall with 60 shops divide into 4 categories (28 commodity stores, 15 cafes and restaurants, 15 services and 2 wagons); and Osu district, a shopping street complex district with 685 shops falling into 8 categories (cafes and restaurants, grocers, household goods, electric appliances, clothing, parks and temples, second hands goods, and others). The latter is modeled as a network with 36 street-nodes.)”)
the state of the first digital twin is an action taken by the person; and (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.”)
the state of the second digital twin includes at least a degree of congestion of the store. (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.”)
Regarding Claim 3: The reference discloses The optimization device according to claim 2, wherein the processor is further configured to give a second parameter for changing the state of the first digital twin to the first digital twin. (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.”)
Regarding Claim 4: The reference discloses The optimization device according to claim 3, wherein the second parameter is a time at which the first digital twin is to start moving or a parameter that can change the time at which the first digital twin is to start moving. (Page 4, “Impulse-Detour Action. Impulse-detour action is positioned opposite to improvisation. It is a planned route departure behavior, which is a higher-order function. The softmax method that uses Boltzmann distribution was used to model this behavior. The steps are as follows. First, when visiting a commercial district, locations that are different from the original destination are selected at random. Next, the values of the selected locations are calculated based on the distance between the current location and the selected locations, and the weight of each location (the weight for each selected location is the same as that given in the Detour at Plan-Making Action stage). Based on these values, the probability of selecting each location is determined using Boltzmann method.”)
Regarding Claim 5: The reference discloses The optimization device according to claim 3, wherein the processor is further configured to acquire the second index value of the second digital twin and the state of the second digital twin when the second parameter is given to the first digital twin. (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.”)
Regarding Claim 6: The reference discloses The optimization device according to claim 1, wherein the first index value is a degree of comfort of the first digital twin, and the second index value is a degree of customer satisfaction of the second digital twin. (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.”)
Regarding Claim 7: The reference discloses An optimization method for optimizing a digital twin system including at least two digital twins, wherein a computer
acquires a first index value of a first digital twin in a case where a first parameter is given, (Fig 3. Agent and their respective behavior represents the first digital twin and its respective parameter)
estimates a state of the first digital twin after the first parameter is given, (Page 4, “Impulse-Detour Action. Impulse-detour action is positioned opposite to improvisation. It is a planned route departure behavior, which is a higher-order function. The softmax method that uses Boltzmann distribution was used to model this behavior. The steps are as follows. First, when visiting a commercial district, locations that are different from the original destination are selected at random. Next, the values of the selected locations are calculated based on the distance between the current location and the selected locations, and the weight of each location (the weight for each selected location is the same as that given in the Detour at Plan-Making Action stage). Based on these values, the probability of selecting each location is determined using Boltzmann method.”)
acquires the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin, and (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.” See also Page 8, Section 4, “By employing ASSA as the simulation model we conducted simulation experiments and evaluated the experiments using the evaluation framework. The following two cases in Nagoya were applied: Asunal Kanayama, a three-storied shopping mall with 60 shops divide into 4 categories (28 commodity stores, 15 cafes and restaurants, 15 services and 2 wagons); and Osu district, a shopping street complex district with 685 shops falling into 8 categories (cafes and restaurants, grocers, household goods, electric appliances, clothing, parks and temples, second hands goods, and others). The latter is modeled as a network with 36 street-nodes.)”)
optimizes the first index value and the second index value. (Page 6, Section 3.3, “In both the simulation and survey attention was paid to the degree of behavior and the scheduled plan deviate from the optimum geographical distance solution, and by comparing them, the behavior and planning characteristics of the model were validated. The similarity analysis of visit sequences in the previous section paid attention to visiting order and examined the degree of similarity between samples, whereas this analysis focuses on geographical distance and compared the degree of deviation from the optimum route distance.”)
Regarding Claim 8: The reference discloses A non-transitory storage medium that stores a computer-executable program to execute an optimization processing for optimizing a digital twin system including at least two digital twins in which the optimization processing comprises:
acquiring a first index value of a first digital twin in a case where a first parameter is given, (Fig 3. Agent and their respective behavior represents the first digital twin and its respective parameter)
estimating a state of the first digital twin after the first parameter is given, (Page 4, “Impulse-Detour Action. Impulse-detour action is positioned opposite to improvisation. It is a planned route departure behavior, which is a higher-order function. The softmax method that uses Boltzmann distribution was used to model this behavior. The steps are as follows. First, when visiting a commercial district, locations that are different from the original destination are selected at random. Next, the values of the selected locations are calculated based on the distance between the current location and the selected locations, and the weight of each location (the weight for each selected location is the same as that given in the Detour at Plan-Making Action stage). Based on these values, the probability of selecting each location is determined using Boltzmann method.”)
acquiring the state of the first digital twin, a second index value of a second digital twin obtained in a case where the first parameter is given, and a state of the second digital twin, and (Page 4 “Detour at the Plan Making Action. As mentioned previously, detour behavior at the planning stage occurs during route planning. Therefore, we have removed the optimality assumption of path planning in ASSAver.1. The relaxation means that the visitor deliberately selects a longer route, even though the visitor knows the shortest route, based on considerations such as safety, degree of congestion, or preference toward particular streets. In order to implement the above behavior in the model, we weighted each agent relative to each link in the network of commercial districts. With this weight applied, each agent would select a psychologically or emotionally shortest route as opposed to the physically shortest route. Therefore, even though agents themselves believe that they planned to take the shortest routes, the actual routes planned would be the longer routes at the planning action stage. The weight applied to each link was determined by adding a randomization item to preference values against the facilities possessed by each agent.” See also Page 8, Section 4, “By employing ASSA as the simulation model we conducted simulation experiments and evaluated the experiments using the evaluation framework. The following two cases in Nagoya were applied: Asunal Kanayama, a three-storied shopping mall with 60 shops divide into 4 categories (28 commodity stores, 15 cafes and restaurants, 15 services and 2 wagons); and Osu district, a shopping street complex district with 685 shops falling into 8 categories (cafes and restaurants, grocers, household goods, electric appliances, clothing, parks and temples, second hands goods, and others). The latter is modeled as a network with 36 street-nodes.)”)
optimizing the first index value and the second index value. (Page 6, Section 3.3, “In both the simulation and survey attention was paid to the degree of behavior and the scheduled plan deviate from the optimum geographical distance solution, and by comparing them, the behavior and planning characteristics of the model were validated. The similarity analysis of visit sequences in the previous section paid attention to visiting order and examined the degree of similarity between samples, whereas this analysis focuses on geographical distance and compared the degree of deviation from the optimum route distance.”)
Conclusion
7. All Claims are rejected.
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) Bolton, Ruth N., et al. "Customer experience challenges: bringing together digital, physical and social realms." Journal of service management 29.5 (2018): 776-808.
ii) Zacharias, John, Torsten Bernhardt, and Luc De Montigny. "Computer-simulated pedestrian behavior in shopping environment." Journal of Urban Planning and Development 131.3 (2005): 195-200.
iii) Cairns, Sally. "Delivering supermarket shopping: more or less traffic?." Transport Reviews 25.1 (2005): 51-84.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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