DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 08/04/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0029] recites (bold emphasis added): “processor 120 may be configured to execute instructions …may also implement some or all of the risk mitigation described herein, including determining and mitigating BoS hazards.” Examiner does not find any instance of this acronym being explicated in either specification, abstract or claim limitations. The term will be interpreted to mean “Brake override System”, based on context analysis of application, for purposes of examination, but should the acronym should be defined to reflect Applicant’s intended meaning.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 5, 7- 9, 13, 17 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023).
With respect to Claim 1, BERMÚDEZ teaches:
A refrigeration system, comprising:
(See for context, [0002]: “present invention relates to a refrigeration system based on the use of a caloric organic-inorganic hybrid material”, thus, BURMÚDEZ GARCIA is in same technical field..)
a container holding a solid refrigerant comprised of a target molecule;
(BURMÚDEZ GARCIA teaches a container to hold material, [0078]: “samples…of the material are analyzed…with a pressure cell”; Examiner notes broadest reasonable interpretation for the term “container”, to be analogous to reference of “pressure cell”, to mean an enclosure containing a barocaloric material.; Examiner further notes interpretation of claim limitation language “target molecule” using broadest reasonable interpretation to mean a molecule selected as a molecule type based on some pre-determined quality. Examiner notes reference refers to a variety of barocaloric materials composed of molecules, see [0065].)
a structure configured to apply a pressure to the solid refrigerant;
(BURMÚDEZ GARCIA teaches pressurization and de-pressurization of solid refrigerant, see abstract: “refrigeration induced by an external stimulus comprising the application of an external stimulus selected among hydrostatic pressure, uniaxial pressure”, and as above, material was inside a pressure cell, see [0078].)
a fan configured to conduct an airflow relative to the container, the airflow moving at least one of heated air or cooled air generated by applying the pressure to the solid refrigerant,
(BURMÚDEZ GARCIA teaches airflow used for heat exchange between refrigerant and reservoir to be heated or cooled, see [0055]: “invention further comprises: (3) a heat sink that is responsible for removing heat to the outside; (4) optionally a heat exchanger fluid; and (5) a reservoir or enclosure that needs to be cooled. The sink could be for example a fan, a heat sink, etc.” )
However, BURMÚDEZ GARCIA is silent to the language of:
wherein the target molecule is determined based on a machine learning model predicting one or more properties of the target molecule, the one or more properties including at least one of a temperature of fusion (Tfusion) or an entropy of fusion (ΔSfusion).
Nevertheless, TAKAGI teaches:
wherein the target molecule is determined based on a machine learning model predicting one or more properties of the target molecule, the one or more properties including at least one of a temperature of fusion (Tfusion) or an entropy of fusion (ΔSfusion).
(For context, see abstract: “In this study, we constructed a machine learning framework to screen molecules that will exhibit solid–solid phase transitions in their crystalline states”, and see Fig. 1 with Pg.1126Col 2Par2: “motivated us to apply it to the search for a hidden trend of solid–solid phase transitions in molecular crystals (Fig. 1).”, thus TAKAGI is in same technical field.; TAKAGI teaches a method using machine learning to screen molecules for determining solid-solid phase transition see Pg1126,Col2,bottom of page: “Classification models were trained and then compared, and the best classifier suggested molecules that potentially exhibit solid–solid phase transitions. Among them, we found solid–solid phase transitions by literature search”; TAKAGI teaches modeling and consideration of at least transition temperature , see Pg1120,Fig.4: “Fig. 4: Scatter plot of experimental and predicted values of Tendo(max).”, and see Pg1131Col1Para1: “Among 9 substances found by molecular screening, 8 datasets were available for the inference of Tendo(max)”; Examiner notes interpretation of claim limitation language “target molecule” as above.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify BURMÚDEZ GARCIA to include a target molecule that is determined based on a machine learning model predicting one or more properties of the target molecule, the one or more properties including at least one of a temperature of fusion (Tfusion) or an entropy of fusion (ΔSfusion), such as that of TAKAGI.
One of ordinary skill would be motivated to modify BURMÚDEZ GARCIA to include a target molecule that is determined based on a machine learning model predicting one or more properties of the target molecule, the one or more properties including at least one of a temperature of fusion (Tfusion) or an entropy of fusion (ΔSfusion), as taught by TAKAGI because it would be understood as an effective and efficient way to improve the selection process for a barocaloric material for use as a solid refrigerant. TAKAGI discloses motivation for taking advantage of the computational power of machine learning algorithms for investigation of solid-solid phase transitions, based primarily in the fact that calculations of generally know quantities, (for example Gibbs free energy, as on Pg1126,Col2) for understanding material behavior are computationally costly, and are most often found “by chance after many trial and error experiments” (Pg 1126Col2). One of ordinary skill would be motivated to combine the machine learning method taught by TAKAGI with the practical application of barocaloric-based refrigeration system of BURMÚDEZ GARCIA realize, with a reasonable expectation of success, a means of efficiently narrowing down a list of potential materials for application in a refrigeration system, based on the positive predictive results disclosed in the TAKAGI reference, and would understand that specific algorithms could be easily modified to investigate and predict a wide range of material properties of interest.
With respect to Claim 2, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The refrigeration system of claim 1,
(See above, references as applied to Claim 1.)
BURMÚDEZ GARCIA further teaches:
wherein the target molecule is determined based on susceptibility to a Barocaloric effect.
(BURMÚDEZ GARCIA introduces the desired properties for application of barocaloric materials in a refrigeration system, see [0010]: “inventors have noticed a new family of organic-inorganic hybrid materials with general formula ABX.sub.3 (I) with an improved barocaloric effect that can be led to more efficient refrigeration applications” )
With respect to Claim 5, BURMÚDEZ GARCIA teaches:
BURMÚDEZ GARCIA teaches:
and determining a target molecule of the plurality of candidate molecules to implement in a refrigeration system.
(As above, BURMÚDEZ GARCIA used desired properties for application of barocaloric materials in a refrigeration system, see [0010])
However, BURMÚDEZ GARCIA is silent to the language of
A method, comprising:
training a machine learning model to predict one or more properties of a molecule, the one or more properties including at least one of a temperature of fusion (Tfusion) or an entropy of fusion (ΔSfusion),
the machine learning model trained based on a sample of molecules;
applying the machine learning model to a plurality of molecules to predict the one or more properties for molecules of the plurality of molecules;
determining a plurality of candidate molecules from the plurality of molecules, the plurality of candidate molecules determined based on the one or more properties predicted for molecules of the plurality of molecules;
determining a plurality of candidate molecules from the plurality of molecules, the plurality of candidate molecules determined based on the one or more properties predicted for molecules of the plurality of molecules;
Nevertheless, TAKAGI teaches:
A method, comprising:
training a machine learning model to predict one or more properties of a molecule,
(See as noted above, TAKAGI teaches using machine learning for predicting material properties of materials for barocaloric applications, see abstract: “In this study, we constructed a machine learning framework to screen molecules that will exhibit solid–solid phase transitions”)
the one or more properties including at least one of a temperature of fusion (Tfusion) or an entropy of fusion (ΔSfusion),
(TAKAGI teaches prediction and evaluation of solid-solid transition temperature as melting (fusion) temperature of selected molecules, see Pg1129Fig.2: “thermal profile of DSC showed peak-like behavior before reaching the melting point”)
the machine learning model trained based on a sample of molecules;
(TAKAGI teaches evaluation of various molecules with various compositions and structure, see Pg1127Secion:Materials and Methods,Col1,Para2: “total of 297 datasets were extracted from 91 papers, and the number of unique molecules was 88”)
applying the machine learning model to a plurality of molecules to predict the one or more properties for molecules of the plurality of molecules;
(TAKAGI teaches evaluation of molecules using machine learning techniques, see Pg1127-1128 Section: Machine learning implementation)
determining a plurality of candidate molecules from the plurality of molecules, the plurality of candidate molecules determined based on the one or more properties predicted for molecules of the plurality of molecules;
(TAKAGI teaches evaluation based on a selection of predicted quantities, see Pg1128Section:Results and discussion, Pg1129Fig.2, and Pg1131Fig.5, and Pg1131Section: Conclusion.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA to implement the machine learning processes as described above, such as that of TAKAGI.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA to implement the machine learning processes as described above, as taught by TAKAGI because it would be understood as a reliable, efficient and sensible way to take advantage of the power of machine learning to predict the most likely molecular materials to satisfy the performance outcomes of a barocaloric-based refrigeration system. One of ordinary skill would be motivated to implement a well-trained machine learning algorithm to efficiently and quickly evaluate multiple potential barocaloric candidate materials, as taught by TAKAGI, in combination with an actual system as disclosed by BURMÚDEZ GARCIA to avoid a costly, slow effort to use conventional trial and error based evaluation to find the best material. The method of TAKAGI combined with the system disclosed by BURMÚDEZ GARCIA would give one of ordinary skill seeking to solve a refrigeration problem reasonable expectation of success for an improved based on a reasonable expectation for success in realizing an improved and more efficient cooling/heating system.
With respect to Claim 7, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
TAKAGI further teaches:
further comprising: determining the plurality of molecules from a database of molecules, the plurality of molecules determined based on having predefined elements.
(As above, TAKAGI teaches evaluation of various molecules with various compositions and structure, see Pg1127Secion:Materials and Methods,Col1,Para2: “total of 297 datasets were extracted from 91 papers, and the number of unique molecules was 88”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA to implement the machine learning step of using a molecular properties database, such as that of TAKAGI.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA to implement the machine learning processes as described above, as taught by TAKAGI to develop a robust and reliable molecule selection process, and develop an improved refrigeration system.
With respect to Claim 8, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
TAKAGI further teaches:
further comprising: determining the sample of molecules from the plurality of molecules.
(TAKAGI teaches a process for selection of a subset of molecules, see Pg1128Section: Results and discussion, for process of evaluating model results, and Pg1128: “suggester must find molecules likely to exhibit solid–solid phase transitions.”, and Pg1129: “above results showed that at least 3/14 (21.4%) compounds with p $ 0.3 and 9/113 (8.0%) compounds with p $0.2 were positive.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA to implement the machine learning step narrowing down the number of molecular candidates, such as that of TAKAGI.
One of ordinary skill would be to further modify BURMÚDEZ to implement the machine learning step narrowing down the number of molecular candidates, as taught by TAKAGI because it would be understood as a way to save time and expense to find a smaller set of the most probably candidates with desired characteristics for a specific refrigeration system.
With respect to Claim 9, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
TAKAGI further teaches:
wherein the one or more properties further include a solid-to-solid phase transition temperature (TT).
(TAKAGI teaches evaluating molecular structure and targeted properties as related to transition temperature, see Pg1130Col1: “performed the regression of transition temperature…motivation was to determine whether transition temperature…related to the molecular structure.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA to implement the machine learning step of consideration of soli-solid transition temperature, such as that of TAKAGI.
One of ordinary skill would be to further modify BURMÚDEZ to implement the machine learning step of consideration of soli-solid transition temperature, TAKAGI because it would be understood as an essential characteristic relevant to the performance of a barocaloric material for implementation in a specific refrigeration system.
With respect to Claim 13, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
TAKAGI further teaches:
wherein determining the target molecule comprises: ranking candidate molecules of the plurality of candidate molecules based on the one or more properties of the candidate molecules.
(TAKAGI teaches a detailed ranking system beginning on Pg1128Section: Results and discussion through PG1131, where molecule candidates are scored and ranked based on predicted and experimentally determined properties based on “positive” result and “feature importance”; Examiner notes interpretation of claim limitation language “ideal” to mean broadly, preferred or important based on a specific application.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further include wherein determining the target molecule comprises: ranking candidate molecules of the plurality of candidate molecules based on the one or more properties of the candidate molecules, such as that further disclosed by TAKAGI
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further include ranking candidate molecules of the plurality of candidate molecules based on one or more properties of the candidate molecules and one or more ideal values, as further taught by TAKAGI because it would be understood as a meaningful way to skillfully evaluate multiple molecular candidates to ascertain which molecules would perform thermally in a preferred manner. One of ordinary skill would understand that detailed process taught by TAKAGI including experimental value comparisons, scoring, and multiple attribute comparison would be a powerful way to gain insight on the best materials to ultimately save money and time in developing an efficient barocaloric-based refrigeration system.
With respect to Claim 17, BURMÚDEZ GARCIA teaches:
determining a target molecule of the plurality of candidate molecules to implement in a refrigeration system.
(As above, BURMÚDEZ GARCIA used desired properties for application of barocaloric materials in a refrigeration system, see [0010])
However, BURMÚDEZ GARCIA is silent to the language of :
A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:
determining a plurality of molecules from a database;
determining a sample of molecules from the plurality of molecules;
training a machine learning model to predict one or more properties of a molecule, the one or more properties indicating susceptibility to a barocaloric effect, the machine learning model trained based on the sample of molecules;
applying the machine learning model to the plurality of molecules to predict the one or more properties for molecules of the plurality of molecules;
determining a plurality of candidate molecules from the plurality of molecules, the plurality of candidate molecules determined based on the one or more properties predicted for molecules of the plurality of molecules;
Nevertheless, TAKAGI teaches:
A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:
(TAKAGI teaches use of standard computational components for carrying out computer based modeling, see Pg1128,Col1: “computations were conducted on a computer (OS: Windows 10, memory: 16 GB, GPU: NVIDIA GeForce GTX 1650)”, and further teaches use of Python coding in same paragraph.)
determining a plurality of molecules from a database;
(As above, parallel limitations in Claim 7, TAKAGI teaches evaluation of various molecules with various compositions and structure, see Pg1127Secion:Materials and Methods,Col1,Para2: “total of 297 datasets were extracted from 91 papers, and the number of unique molecules was 88”)
determining a sample of molecules from the plurality of molecules;
(As above, parallel limitation, Claim 5, TAKAGI teaches evaluation based on a selection of predicted quantities, see Pg1128Section:Results and discussion, Pg1129Fig.2, and Pg1131Fig.5, and Pg1131Section: Conclusion.)
training a machine learning model to predict one or more properties of a molecule, the one or more properties indicating susceptibility to a Barocaloric effect, the machine learning model trained based on the sample of molecules;
(See as above, parallel limitation in Claim 5, TAKAGI teaches training models using machine learning for predicting material properties of materials for barocaloric applications, see abstract: “In this study, we constructed a machine learning framework to screen molecules that will exhibit solid–solid phase transitions”; and as above, database of molecules was used as starting point, see Pg1127Secion:Materials and Methods, more specifically see Pg12128Col1: “the scratch model was trained on
a larger dataset of melting points (n = 22 404)26 through hyperparameter optimization, and then transferred to learn transition temperature and enthalpy”)
applying the machine learning model to the plurality of molecules to predict the one or more properties for molecules of the plurality of molecules;
(TAKAGI teaches evaluation of molecules using machine learning techniques, see Pg1127-1128 Section: Machine learning implementation)
determining a plurality of candidate molecules from the plurality of molecules, the plurality of candidate molecules determined based on the one or more properties predicted for molecules of the plurality of molecules; and
(TAKAGI teaches evaluation based on a selection of predicted quantities, see Pg1128Section:Results and discussion, Pg1129Fig.2, and Pg1131Fig.5, and Pg1131Section: Conclusion.)
Claims 3, 10, 12, and 14 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023), and further in view of LI (Li, et al. “Colossal barocaloric effects in plastic crystals”, Nature 567, 506–510, 2019)
With respect to Claim 3, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The refrigeration system of claim 1,
(See above, references as applied to Claim 1.)
However, BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, is silent to the language of:
wherein the target molecule is a plastic crystal that is a solid at room temperature.
Nevertheless, LI teaches:
wherein the target molecule is a plastic crystal that is a solid at room temperature.
(For context, see Pg506Col1, “Our study establishes the microscopic mechanism of CBCEs in plastic crystals and paves the way to next generation solid-state refrigeration technologies”, thus LI is in same technical field; LI teaches use of plastic crystal in refrigeration applications, see Pg509Col1(bottom): “we have discovered that plastic crystals exhibit CBCEs and revealed the microscopic origin through pressure-dependent QENS and INS measurements. Plastic crystals are very promising for practical refrigeration applications given that they are abundantly available”; and LI teaches these effects at room temperature, see Fig. 1c, and Pg507Col1(bottom)-Col2(top): “As shown in Fig. 1c, colossal entropy changes…at around room temperature can be obtained”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include a target molecule target molecule is a plastic crystal that is a solid at room temperature, such as that of LI.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include a target molecule target molecule is a plastic crystal that is a solid at room temperature, as taught by LI because it would be understood as an advantage to consider a material that exhibits a “colossal” barocaloric effect as a candidate for application in the refrigeration system of BURMÚDEZ GARCIA as modified by TAKAGI. One of ordinary skill would find logical reason to consider the results of LI to guide the selection of candidate materials for a refrigeration system to take advantage of the energy-saving performance of a material with a strong stress-induced thermal energy change.
With respect to Claim 10, BURMÚDEZ GARCIA in view of TAKAGI in view of teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
However, TAKAGI in view of BURMÚDEZ GARCIA teaches
wherein the one or more properties further include an entropy change during a solid-to-solid phase transition (ΔST).
Nevertheless, LI teaches:
wherein the one or more properties further include an entropy change during a solid-to-solid phase transition (ΔST).
(LI teaches molecule evaluation by consideration of entropy change in solid-solid phase transition, see Pg506: “we find that plastic crystals are promising next-generation BCE materials, with colossal entropy changes driven by relatively smaller pressures”, and see Fig.1(c) (same page): “Absolute values of maximum entropy changes, |ΔSmax|, for leading caloric materials.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein the one or more properties further include an entropy change during a solid-to-solid phase transition (ΔST), such as that of LI.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein the one or more properties further include an entropy change during a solid-to-solid phase transition (ΔST), as taught by LI because it would be understood as an important parameter to consider for understanding the expected thermal behavior of a potential barocaloric material. One of ordinary skill would find logical reason to consider the results of LI to guide the selection of candidate materials for a refrigeration system to take advantage of the energy-saving performance of a material with a strong stress-induced thermal energy change.
With respect to Claim 12, BURMÚDEZ GARCIA in view of TAKAGI in view of teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
Nevertheless, LI teaches:
wherein determining the plurality of candidate molecules comprises: determining plastic crystals that are solids at room temperature.
(LI suggests using plastic crystals, see abstract: “we report colossal barocaloric effects (CBCEs…in a class of disordered solids called plastic crystals…entropy changes in a representative plastic crystal, neopentylglycol, are about 389 joules per kilogram
per kelvin near room temperature.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein determining the plurality of candidate molecules comprises: determining plastic crystals that are solids at room temperature, such as that of LI.
One of ordinary skill would be motivated to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein determining the plurality of candidate molecules comprises: determining plastic crystals that are solids at room temperature, as taught by LI because it would be understood as an advantage to consider a material that exhibits a “colossal” barocaloric effect as a candidate for application in the refrigeration system of BURMÚDEZ GARCIA as modified by TAKAGI. One of ordinary skill would find logical reason to consider the results of LI to guide the selection of candidate materials for a refrigeration system to take advantage of the energy-saving performance of a material with a strong stress-induced thermal energy change.
With respect to Claim 14, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
However BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, is silent to the language of:
wherein determining the target molecule comprises: comparing TT of candidate molecules of the plurality of candidate molecules to an ideal TT;
and comparing ΔST of candidate molecules of the plurality of candidate molecules to an ideal ΔST.
Nevertheless, LI teaches:
wherein determining the target molecule comprises: comparing TT of candidate molecules of the plurality of candidate molecules to an ideal TT;
(LI teaches consideration of transition temperature, see P511Section:Methods “phase-transition temperature was defined as the temperature at which the heat flow peaked”, and see Pg512Col1: “final molecular dynamics simulation snapshots were analysed to identify the phase-transition temperatures through the computed structural features”)
and comparing ΔST of candidate molecules of the plurality of candidate molecules to an ideal ΔST.
(See Fig.4, LI teaches analysis of pressure-induced entropy changes, and see comparison table on Pg520, “Extended Data Table 1”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein determining the target molecule comprises: comparing TT of candidate molecules of the plurality of candidate molecules to an ideal TT; and comparing ΔST of candidate molecules of the plurality of candidate molecules to an ideal ΔST, such as that of LI.
One of ordinary skill would be motivated to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein determining the target molecule comprises: comparing TT of candidate molecules of the plurality of candidate molecules to an ideal TT; and comparing ΔST of candidate molecules of the plurality of candidate molecules to an ideal ΔST, as taught by LI because it would be understood as logical evaluation step to identify a best candidate for application in the refrigeration system of BURMÚDEZ GARCIA as modified by TAKAGI. One of ordinary skill would find logical reason to consider the results of LI to guide the selection of candidate materials for a refrigeration system to take advantage of the energy-saving performance of a material with a strong stress-induced thermal energy change.
Claim 4 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023), and further in view of STAUFFER (US 20250137734 A1).
With respect to Claim 4, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The refrigeration system of claim 1,
(See above, references as applied to Claim 1.)
However, BURMÚDEZ GARCIA, as modified by TAKAGI, as taught above, is silent to the language of:
wherein the container, the structure, and the fan are part of a heating, ventilation, and air conditioning (HVAC) system of a vehicle.
Nevertheless, STAUFFER teaches:
wherein the container, the structure, and the fan are part of a heating, ventilation, and air conditioning (HVAC) system of a vehicle.
(For context, see [0001]: “invention relates to the field of heating and cooling, in particular HVAC, e.g. for domestic and industrial application…relates to heating or cooling making use of phase changes, e.g. including a solid phase”, thus STAUFFER is in same technical field; STAUFFER teaches placement of cooling system in range of applications, including vehicle, see [0014]: “system can e.g. be applied in an HVAC system…system can be configured to heat and/or cool a gas…e.g. be air…for air-conditioning a space…interior in a vehicle.”; STAUFFER teaches other components: container, see [0018]: “PCM chamber”, and fan/airflow, see [0183]: “heat exchangers 50 is fluidly connected to the fan-coil”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein the container, the structure, and the fan are part of a heating, ventilation, and air conditioning (HVAC) system of a vehicle, such as that of STAUFFER.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include wherein the container, the structure, and the fan are part of a heating, ventilation, and air conditioning (HVAC) system of a vehicle, as taught by STAUFFER because it would be understood as a logical and important application of the refrigeration system as disclosed by BURMÚDEZ GARCIA with modifications for material selection as taught by TAKAGI. One of ordinary skill would find logical reason to use the disclosure of STAUFFER to realize in practice the hint found in BURMÚDEZ GARCIA of a vehicular application (see BURMÚDEZ GARCIA [0003]) and see the advantage of using a barocaloric system to replace conventional refrigeration system in a vehicle.
With respect to Claim 16, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
Nevertheless, STAUFFER teaches: wherein implementing the target molecule in the refrigeration system comprises:
compressing a substance, comprising the target molecule, in a container; and conducting an airflow relative to the container.
(STAUFFER teaches solid refrigerant, see [0001]: “invention relates to the field of heating and cooling, in particular HVAC…heating or cooling making use of phase changes, e.g. including a solid phase”; STAUFFER teaches implementing cooling system in range of applications, including vehicle, see [0014]: “system can e.g. be applied in an HVAC system…system can be configured to heat and/or cool a gas…e.g. be air…for air-conditioning a space…interior in a vehicle.”; STAUFFER teaches other components: container, see [0018]: “PCM chamber”, and fan/airflow, see [0183]: “heat exchangers 50 is fluidly connected to the fan-coil”)
Claim 6 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023), and further in view of ASAHARA (US 20220358438 A1).
With respect to Claim 6, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
However, However, BURMÚDEZ GARCIA, as modified by TAKAGI, as taught above, is silent to the language of:
further comprising: determining the plurality of molecules from a database of molecules, the plurality of molecules determined based on exceeding a predefined molecular weight.
Nevertheless, ASAHARA teaches:
further comprising: determining the plurality of molecules from a database of molecules, the plurality of molecules determined based on exceeding a predefined molecular weight.
(For context, see abstract: “machine learning models for material property prediction”; ASAHARA is in same technical field, teaching selective method, see [0005]: “screening method, various sorts of experimental data are input to an information system, a model is built for predicting an experiment result through machine learning”; ASAHARA teaches molecular weight as a potential parameter for predictive modeling, see [0074]: “any other amount (e.g., molecular weight or charge) may be derived, added, and used.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include a modeling method further comprising determining the plurality of molecules from a database of molecules, the plurality of molecules determined based on exceeding a predefined molecular weight, such as that of ASAHARA.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include a modeling method further comprising determining the plurality of molecules from a database of molecules, the plurality of molecules determined based on exceeding a predefined molecular weight, as taught by ASAHARA because it would be understood that molecular weight is an important parameter to consider when evaluating thermal manifestations related to applied stress or pressure. One of ordinary skill would see the value of combining the suggestion of considering molecular weight in a set of predictive evaluation parameters in a machine learning process system as disclosed by BURMÚDEZ GARCIA with modifications for material selection as taught by TAKAGI. One of ordinary skill would find logical reason to use the disclosure of STAUFFER to realize in practice the hint found in BURMÚDEZ GARCIA of a vehicular application (see BURMÚDEZ GARCIA [0003]) and see the advantage of using a barocaloric system to replace conventional refrigeration system in a vehicle.
Claim 15 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023), and further in view of TIAN (Tian, et al., “Determining Multi-Component Phase Diagrams with Desired Characteristics Using Active Learning”, Adv. Sci. 2021, 8, 2003165).
With respect to Claim 15, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The method of claim 5,
(See above, references as applied to Claim 5.)
TAKAGI further teaches:
further comprising: testing the target molecule to determine one or more actual properties for the target molecule;
(As above, see Fig. 4, plotting experimental data with predicted values)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further include wherein determining the target molecule comprises: ranking candidate molecules of the plurality of candidate molecules based on the one or more properties of the candidate molecules, such as that further disclosed by TAKAGI
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further include ranking candidate molecules of the plurality of candidate molecules based on one or more properties of the candidate molecules and one or more ideal values, as further taught by TAKAGI because it would be understood as a meaningful way to skillfully evaluate multiple molecular candidates to ascertain which molecules would perform thermally in a preferred manner. One of ordinary skill would understand that detailed process taught by TAKAGI including experimental value comparisons, scoring, and multiple attribute comparison would be a powerful way to gain insight on the best materials to ultimately save money and time in developing an efficient barocaloric-based refrigeration system.
However BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, is silent to the language of:
and updating the machine learning model based on the testing.
Nevertheless, TIAN teaches:
and updating the machine learning model based on the testing.
(For context, see abstract: “demonstrate how to predict and experimentally validate phase diagrams for multi-component systems…uses machine learning methods to predict phase diagrams”, thus TIAN is in same technical field.; TIAN teaches standard iterative machine learning methods, including use of experimental data, best illustrated on Pg2Figure2: “strategy containing two parts. Part I, the upper panel, predicts unexplored phase diagrams from surrogate models of classification and regression. Part II, the lower panel, optimizes a preselected phase diagram based on some targeted features via iterative experiments….(7) synthesis and characterization. The results from (7) augment the initial training data in (1)”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include and updating the machine learning model based on the testing, such as that of TIAN.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to include and updating the machine learning model based on the testing, as taught by TIAN because it would be understood as a best practice way to take advantage of a robust machine modeling predictive method to select the best potential material for implementation into a refrigeration system. One of ordinary skill would understand that actually synthesizing a predicted material to compare experimentally acquired thermal properties with predicted properties and using that data to further refine predictive models is a strong way to get the best results from a machine modeling technique for materials selection.
Claim 19 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023), and further in view of ANDRADE (Andrade, et al., “Barocaloric effect of elastomers and plastic crystals: an outlook”, arXiv:2208.08474v1 [cond-mat.mtrl-sci] 17 Aug 2022)
With respect to Claim 19, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The non-transitory computer readable medium of claim 17,
(See above, references as applied to Claim 17.)
However BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, is silent to the language of:
wherein determining the plurality of candidate molecules comprises: determining molecules of the plurality of molecules having Tfusion between 350K and 450K and ΔSfusion less than a 30 J/(mol-K).
Nevertheless, ANDRADE teaches
wherein determining the plurality of candidate molecules comprises: determining molecules of the plurality of molecules having Tfusion between 350K and 450K and ΔSfusion less than a 30 J/(mol-K).
(For context, see Abstract: “attention has been turned to the Barocaloric effect in advanced materials …this review has the goal of collecting, organizing and comparing
the results from the literature to provide an outlook on the subject”, thus ANDRADE is in same technical field; ANDRADE teaches consideration of fusion temperatures for plastic crystals, see Pg2,Col1Para2: “material should present other features like plastic flow under stress, high self-diffusion, high compressibility and high vapour pressures close to their high melting temperature (above 350 K)”; and teaches consideration of entropy change at melting, see Pg2Col1Para2: “For a set of compositions of simple constituents, a low entropy of fusion/melting (∆Sf ) below 20 J/kgK was observed for systems composed of globular molecules”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to wherein determining the plurality of candidate molecules comprises: determining molecules of the plurality of molecules having Tfusion between 350K and 450K and ΔSfusion less than a 30 J/(mol-K), such as that of ANDRADE.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to wherein determining the plurality of candidate molecules comprises: determining molecules of the plurality of molecules having Tfusion between 350K and 450K and ΔSfusion less than a 30 J/(mol-K), as taught by ANDRADE because it would be understood that consideration of these well-known characteristic properties would provide insight into the expected thermal behavior of a material for implementation in heat exchange applications. Moreover, one of ordinary skill would see the value of the combination of the teaching of ANDRADE with the system of BURMÚDEZ GARCIA and machine modeling techniques taught by TAKAGI to take advantage of the evaluation of new or novel materials, such as plastic crystals and elastomers as suggested by ANDRADE (see abstract) that may perform better to produce a more reliable and efficient barocaloric refrigeration system.
Claim 20 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over BERMÚDEZ GARCIA (US 20200332167 A1) in view of TAKAGI (Takagi, et al., “Molecular screening for solid–solid phase transitions by machine learning”, Digital Discovery, 2023, 2, 1126; Open Access Article, Published 22 June 2023), and further in view of RAVINSON (US 20220406416 A1).
With respect to Claim 20, BURMÚDEZ GARCIA in view of TAKAGI teaches:
The non-transitory computer readable medium of claim 17,
(See above, references as applied to Claim 17.)
TAKAGI further teaches:
the operations further comprising: ranking candidate molecules of the plurality of candidate molecules based on one or more properties of the candidate molecules and one or more ideal values.
(See as above, parallel limitation in Claim 13, TAKAGI teaches a detailed ranking system beginning on Pg1128Section: Results and discussion through PG1131, where molecule candidates are scored and ranked based on predicted and experimentally determined properties based on “positive” result and “feature importance”; Examiner notes interpretation of claim limitation language “ideal” to mean broadly, preferred or important based on a specific application.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further include ranking candidate molecules of the plurality of candidate molecules based on one or more properties of the candidate molecules and one or more ideal values, such as that further disclosed by TAKAGI
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further include ranking candidate molecules of the plurality of candidate molecules based on one or more properties of the candidate molecules and one or more ideal values, as further taught by TAKAGI because it would be understood as a meaningful way to skillfully evaluate multiple molecular candidates to ascertain which molecules would perform thermally in a preferred manner. One of ordinary skill would understand that detailed process taught by TAKAGI including experimental value comparisons, scoring, and multiple attribute comparison would be a powerful way to gain insight on the best materials to ultimately save money and time in developing an efficient barocaloric-based refrigeration system.
However BURMÚDEZ GARCIA as modified by TAKAGI and as taught above, is silent to the language of
[ranking candidate molecules based on ] Euclidean distances between the one or more properties of the candidate molecules and one or more ideal values.
Nevertheless, RAVINSON teaches:
[ranking candidate molecules based on ] Euclidean distances between the one or more properties of the candidate molecules and one or more ideal values.
(For context: abstract: “method for selecting a material having a desired molecular property comprises generating a combinatorial library of molecule structures derived from a core molecular structure, splitting the library into a training set configured to train a graph neural network (GNN) machine learning (ML) model”, thus RAVINSON is in same technical field; RAVINSON teaches use of Euclidean distance method for evaluation of molecule properties, see [0052]: “Two types of commonly used supervised ML methods mated with the 12NP33B featurization have been explored in one embodiment…[followed by equations depicting mathematical kernels]…where… d(xi,xj) and ∥xi,xj∥1 are the Euclidean and Manhattan distances.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to [ranking candidate molecules based on ] Euclidean distances between the one or more properties of the candidate molecules and one or more ideal values, such as that of RAVINSON.
One of ordinary skill would be to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to further modify BURMÚDEZ GARCIA as modified by TAKAGI, as taught above, to [ranking candidate molecules based on ] Euclidean distances between the one or more properties of the candidate molecules and one or more ideal values, as taught by RAVINSON because it would be understood that as a way to further validate the accuracy and reliability of a predictive model by providing insight into the similarity or dissimilarity between property predictions, and would be known as an option for an effective component for building a predictive algorithm.
Allowable Subject Matter
Claims 11 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Specifically, Claim 11, with dependency to Claim 5 (method), recites:
“wherein determining the plurality of candidate molecules comprises: determining molecules of the plurality of molecules having Tfusion greater than a first threshold and ΔSfusion less than a second threshold.” Examiner found the best prior art related to barocaloric materials screening to be that of TAKAGI. Further search did not result in an individual reference or an obvious combination of references that suggested or taught the specific comparative step recited in Claim 11, for a fusion/melt temperature “greater than a first threshold”, and a entropy change at fusion temperature “less than a second threshold”. TAKAGI and others (see below) do teach comparative analysis for screening and selection of barocaloric materials, but Examiner did not find references that were publicly available before the effective filing date of the claimed invention that disclosed the specific comparisons as claimed.
With regard to Claim 18, with dependency to Claim 17 (non-transitory computer readable medium), limitation recites:
“database includes at least 109 molecules, the plurality of molecules includes at least 106 molecules, and the plurality of candidate molecules includes at least 103 molecules, and wherein determining the plurality of molecules comprises:
determining molecules having elements restricted to one or more of Carbon, Hydrogen, Oxygen, Nitrogen, Bromine, Chlorine, Fluorine, and Sulfur.”
Examiner finds reference to teach the specific list of elements to be included in a desired barocaloric invention (see below MOYA, directed to same technical). However, no reference available at the time the invention was filed was discovered that specifically teaches a minimum number of molecules used for training (database) or a specific minimum number of molecules that must be present in selection process steps in a screening process of potential molecules by evaluation of desired properties. TAKAGI does teach ranking based on an overall evaluation score, but does not teach a minimum required number in a set of candidate molecules.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
BERMUDEZ GARCIA (US 20240336821 A1) – teaches evaluation and use of a specific type of barocaloric materials (MOF)
BRERETON (US 20220051759 A1) – teaches a machine learning based method for evaluating chemical properties of materials
MOYA (US 20200123426 A1) – teaches selection and use of a variety of barocaloric materials, including specific elements as disclosed in claimed invention.
LI (CN 109140821 A) – teaches plastic crystal applied in a pressure actuated solid state refrigeration method
ALKAHATIB (Alkhatib, et al., “Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning”, Ind Eng Chem Res. 2022 May 18;61(21):7414–7429) – teaches a machine learning method for evaluation and selection of materials based on specific desired performance properties indicators.
GARCIA-BEN (García-Ben et al., “Simple and Low-Cost Footstep Energy-Recover Barocaloric Heating and Cooling Device”, Materials 2021, 14, 5947.– teaches using barocaloric materials for implementation of a heating colling device.
GARCIA-BEN (Garcia-Ben, et al., "Discovery of Colossal Breathing-Caloric Effect under Low Applied Pressure in the Hybrid Organic" Inorganic MIL-53(Al) Material", Chem. Mater. 2022, 34, 3323-3332. (Year: 2022) – teaches further research on evaluation of barocaloric material properties.
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/TONI D SAUNCY/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863