The subject innovation relates generally to sustainability enhancements, and more particularly to systems and/or methodologies for a dynamic sustainability search engine.
Consumers, manufacturers, and retailers are becoming increasingly interested in environmental and socio-environmental concerns, such as recycling, child labor practices, reducing greenhouse gas emissions, improving energy demand, and so forth. In addition, the current economic climate is making “green” solutions ever more attractive, particularly those with the potential to decrease cost or increase profitability. However, access to information regarding a number of these issues can be difficult to locate, assuming that said information is even readily available.
A variety of current approaches focus on broadly defining products or processes as “green”, conflict-free, environmentally friendly, etc. For example, a number of products are marked with seals that intend to confer to prospective purchasers that the products are somehow more ecologically friendly than comparable products. Similarly, a number of certifications are available in various areas, typically at an expense to the party seeking the certification. The merit of these seals and certifications can be difficult to ascertain, because details of what they intend to convey is often unknown or unclear.
The current approaches are often only vague or ambiguous indicators of a products actual environmental or socio-environmental impact. In addition, the rating or certification systems may ignore or give undesirable weight to any number of factors that are important to different people and groups. Consequently, it would be desirable to have a dynamic technique for determining the sustainability of products, and efficiently locating information regarding said sustainability.
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key or critical elements nor delineate the scope of such embodiments. Its purpose is to present some concepts of the described embodiments in a simplified form as a prelude to the more detailed description that is presented later.
Systems and methods are provided for facilitating dynamic sustainability searches. A sustainability search component executes a query, and returns one or more results that satisfy a set of search criteria and a set of sustainability factors. The search criteria can include keywords, such as product types, process types, and additional features related to the keywords, such as price, location, brand, and so forth. The sustainability factors are alternative cost measures for a given process, product, or plant element. The sustainability search component can analyze the returned data elements, and rank the data elements according to sustainability.
The sustainability can be determined based on a predetermined sustainability score, or can be determined based on weights assigned to one or more sustainability factors. The weights assigned to the sustainability factors can be predetermined, or can be determined based on one or more user preferences. The query results and rankings can be used to influence consumer purchasing, for management of industrial production, for regulatory oversight, for supply chain management, and product design. In addition, one or more interfaces can be provided to facilitate user interaction with the sustainability search component.
To the accomplishment of the foregoing and related ends, one or more embodiments comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects and are indicative of but a few of the various ways in which the principles of the embodiments may be employed. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings and the disclosed embodiments are intended to include all such aspects and their equivalents.
The subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the subject matter. It may be evident, however, that subject matter embodiments may be practiced without these specific details. In other instances, well-known structures and devices are illustrated in block diagram form in order to facilitate describing the embodiments.
As used in this application, the terms “component,” “system,” “object,” “model,” “policy,” and the like are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal).
Additionally or alternatively, the targets 106 can include a cloud-based data store 106e. The cloud-based data store 106e (e.g., cloud) is illustrative of a computing infrastructure having a plurality of devices capable of communicating data and/or virtualized resources across the communication infrastructure 104 (e.g., such as the Internet). It is to be appreciated that user devices (e.g., sustainability search component 102) obtaining data or resources from the cloud 106e do not need to have knowledge of, expertise in, or control over the technology infrastructure contributing to or contained in the cloud 106e. Furthermore, the communication link 104 can include public networks such as the Internet, Intranets, and automation networks such as Control and Information Protocol (CIP) networks including DeviceNet and ControlNet. Other networks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus, Profibus, wireless networks, serial protocols, and so forth.
In operation, the sustainability search component 102 queries one or more of the targets 106 for data elements satisfying a set of search criteria and a set of sustainability factors (e.g., sustainability criteria, sustainability metrics, etc.). The search criteria can include search terms (e.g., keywords), such as product types, process types, and so forth. For example, the sustainability search component 102 can execute a query for “motors.” In addition, the search criteria can include additional features of or related to the search terms, including but not limited to price, brand, location, etc. Continuing with the previous example, the search component 102 can execute a query for “motors” that have a price of less than or equal to five thousand dollars ($5000.00).
The term “sustainability factors” is intended to assess alternative cost measures for a given process, product, or plant element. The sustainability factors can be organized into categories or subgroups. For instance, the sustainability factors can be organized into at least three categories: planet, human, and financial sustainability. The planet sustainability factors can include resource consumption, such as water, air, gas, electricity, and steam (e.g., WAGES). In addition, the planet sustainability factors can include carbon emissions, recyclability (e.g., component, packaging, etc.), waste factor (e.g., product and/or process), and most any other factor that relates to the environmental impact of a product or process. The human sustainability factors, can include diversity factors (e.g., employment of minorities) for employers, training scores (e.g., skilled versus unskilled labor), union labor scores (e.g., use of union vs. non-union labor), fair labor practices, lost work day case rate (e.g., LWDCR, work related injuries and illnesses that result in the employee not being able to perform work), recordable case rate (e.g., RCR, work related injury or illness requiring attention beyond first aid), or most any other factor that relates to the safety performance of a product, machine, organization, and so forth. The financial sustainability factors can include utilities cost (e.g., energy demand, demand charges, etc.), marketing appeal (e.g., package design A tested better than package design B and is expected to be more profitable), or most any financial measurement impacting the cost or profitability of a product or process. Returning to a prior example, the sustainability search component 102 can query the targets 106 for motors costing less than or equal to $5000 (e.g., search criteria), and that were manufactured without child labor (e.g., human sustainability factor). The sustainability search component 102 obtains, retrieves, or otherwise acquires one or more results that satisfy one or more of the search criteria (e.g., motors, >=$5000), and sustainability factors (e.g., no child labor).
In addition, the sustainability search component 102 can analyze the resulting data elements (e.g., results). For example, the analysis can include locating a predetermined sustainability score contained in or associated with a data element. Additionally or alternatively, the sustainability search component 102 can determine the sustainability score based on one or more associated sustainability factors. Users can assign weights or prioritization values to the sustainability factors based on their personal or business preferences. For example, some users value human sustainability factors more than planet sustainability factors and others may place the most emphasis on a particular sustainability factor such as energy consumption. Furthermore, the sustainability search component 102 can rank the results based on their sustainability scores (discussed infra). The sustainability search component 102 can also base the results ranking on additional factors, such as relevance, user preferences, and so forth. It is to be appreciated that a user can be a human being, or an application or process (e.g., external or integrated).
Most any of the targets 106, such as the controller 106a, network device 106b, data store 106c, or website 106d can be initially responsible for measuring or acquiring measurements for the sustainability factors. As used herein, the term controller or PLC can include functionality that can be shared across multiple components, systems, or networks. For example, one or more controllers 106a can communicate and cooperate with various network devices 106b via the communication link 104. This can include substantially any type of control, communications module, computer, I/O device, sensors, Human Machine Interface (HMI) that communicate via the network that includes control, automation, or public networks. The controller 106a can also communicate to and control various other devices such as Input/output modules including Analog, Digital, Programmed/Intelligent I/O modules, other programmable controllers, communications modules, sensors, output devices, and the like. It is to be appreciated that the foregoing example is illustrated for brevity and clarity of explanation; a plurality of additional embodiments may be possible within the scope and spirit of the subject innovation.
The evaluation component 204 examines, rates, or otherwise analyzes the data elements and associated tags obtained by the query component 202. Analysis of the data elements can include determining sustainability scores for the data elements. The sustainability scores can be predetermined values that are associated with the data elements. For example, a query can return a data element for the product Y, where the product Y has a predetermined sustainability score of X. Additionally or alternatively, the evaluation component can determine the sustainability score for the data elements by weighting one or more sustainability factors of the data elements. For instance, users of the system 200 may have different sustainability needs, and therefore can assign (discussed infra) different weights to sustainability factors. Continuing with the example, a data element for a bottle of wine may have a plurality of sustainability factors associated with it, including a safety performance score (e.g., human sustainability factor) that has a value ranging for 0 to 10, a water consumption factor (e.g., planet sustainability factor) expressed in the number of gallons used in manufacture of the bottle of wine, and a true or false recyclable packaging factor (e.g., true=1, false=0). A first user may prioritize the human sustainability factor (e.g., safety performance) above the planet sustainability factors (e.g., water consumption, and recyclable packaging), and the first user can assign a weight of thirty (30) to the safety performance factor, a weight of twenty (20) to the water consumption factor, and a weight of ten (10) to the recyclable packaging factor. Therefore, the evaluation component can determine the first user's sustainability score for a bottle of wine using the following equation:
SS1=(30*spf)+(20*wcf)+(10*rpf)
where SS is the sustainability score, spf is the safety performance factor, wcf is the water consumption factor, and rpf is the recyclable packaging factor. Conversely, a second user, from a drought prone area, perhaps, can prioritize water consumption above the other factors. For instance, the second user can assign a weight of ten (10) to the safety performance factor, a weight of thirty (30) to the water consumption factor, and a weight of twenty (20) to the recyclable packaging factor. The evaluation component 204 can determine the second user's sustainability score for a bottle of wine using the following equation:
SS2=(10*spf)+(30*wcf)+(20*rpf)
The evaluation component 204 can determine different sustainability scores for query objects based on the weighting of one or more sustainability factors. It is to be appreciated the data elements can have additional un-weighted sustainability factors, wherein the evaluation component 204 can assign a default weight to those factors, or not use the factors in determining the sustainability score.
The ranking component 206 organizes the data elements based on the sustainability score determined by the evaluation component. For instance, the ranking component 206 can organize the bottles of wine from the previous example, in a list order from most desirable sustainability score to least desirable sustainability score. In addition, the ranking component 206 can restrict the list to data elements having sustainability scores above or below a threshold, or can restrict the list to a predetermined number of data elements. For example, the ranking component 206 may only include data elements having a sustainability score below (500) on the list, or can restrict the list to the ten (10) data elements having the most desirable sustainability scores. Furthermore, the ranking component 206 can use additional factors in organizing the data elements, including but not limited to relevance, user preferences, and so forth. It is to be appreciated that the ranking component 206 can organize the data elements in a plurality of ways and is not limited to a list. For example, the ranking component 206 can organize the data elements in tables, charts, graphs, matrices, and virtually any manner suitable for conveying the intended information.
The sustainability search component 102 can further include an interface component 208, which provides various adapters, connectors, channels, communication paths, etc. to integrate the sustainability search component 102 into virtually any operating and/or database system(s). In addition, the interface component 208 can provide various adapters, connectors, channels, communication paths, etc., that provide for interaction with the sustainability search component 102. For instance, the interface component 208 can enable interaction with the sustainability search component 102 via a set of inputs 210, where the inputs 210 can include explicit user inputs (e.g., configuration selections, question/answer) such as from mouse selections, keyboard selections, speech, and so forth. The inputs 210 can also include data uploads, wherein a data upload is the transfer of data from the user or a third party source (e.g. computer or a computer readable medium), to the system 200. In particular, the interface component 208 can receive any data relating to search criteria, sustainability factors, sustainability factor weighting, etc.
It is to be appreciated that although the interface component 208 is illustrated as being incorporated into the sustainability search component 102, such implementation is not so limited. For instance, the interface component 208 can be a stand-alone component to receive or transmit data in relation to the sustainability search component 102. In addition, the interface component 208 can include a display component 212 that facilitates displaying search criteria, sustainability factors, sustainability factor weighting, data elements, organization of data elements, and so forth.
Additionally or alternatively, the query results and/or rankings can be communicated, transmitted, or otherwise provided to an external application via a communication network (discussed above). For example, the results and rankings can be provided to an advertising application that obtains the results, and uses the results and ranking to implement a targeted advertising campaign to perspective consumers exhibiting sustainability concerns close to the sustainability scores of the query results. As an additional example, the results and rankings can be provided to a design application that can use the results to enhance or optimize a manufacturing design.
Turning now to
System 300 can additionally comprise memory 302 that is operatively coupled to the sustainability search component 102 and that stores search criteria, sustainability factors, weights, user preferences, data elements, and so forth or information related to the data elements, search criteria, sustainability factors, weights, user preferences, and any other suitable information related to facilitating dynamic sustainability searches. A processor 304 can be operatively connected to the sustainability search component 102 (and/or memory 302) to facilitate storing and/or communicating content and the like. It is to be appreciated that processor 304 can be a processor dedicated to executing queries, analyzing results, ranking results and/or interfacing with the sustainability search component, a processor that controls one or more components of system 300, and/or a processor that obtains, analyzes, and ranks results, generates interfaces, and controls one or more components of system 300.
In additional embodiments, the data element 502 can be a sustainability factor, or sustainability score. For example, a sustainability search can be executed for a product Y in a data store containing a plurality of data associated with product Y. The data element 502 can be retrieved as part of the results of the search, wherein the data element 502 contains a predetermined sustainability score. It is to be appreciated that the foregoing represents but a few examples of possible data structures, and a plurality of additional data structures are possible within the scope and spirit of the subject application.
Referring to
The GUI 600 also includes a criteria field 604 that enables users to input additional search criteria and/or sustainability criteria. For instance, a user may desire to search for a product specified in the search field 602, based on a particular sustainability criteria. The criteria field 604 is illustrated as being a dropdown menu, but the implementation is not so limited, and it could include most any type of input field (e.g., field, text area, etc.). In addition, a weight field 606 associated with the criteria field is provided. As discussed previously, users can assign weights to the sustainability criteria based on their personal preferences, and the sustainability score of a query object can be determined based on the user assigned weights. An add button 608 is provided that allows users to add up to W criteria and associated weight fields, where W is an integer. For example, a user may desire to search for a given a product based on five sustainability factors, and the user can add five criteria fields to select the desired sustainability factors.
A go button 610 is provided that users can select when they desire to execute the specified query. As discussed previously, the sustainability search engine executes the specified query against a set of targets, analyses the results, and can rank them based on the sustainability scores. An example set of search results 612 are shown, wherein the results are ranked according to sustainability score, and related sustainability factors (e.g., percentage recycled, child labor, percentage using clean energy) are shown as well.
In view of the example systems described supra, methodologies that may be implemented in accordance with the disclosed subject matter will be better appreciated with reference to the flow chart of
Turning now to
At 704, a set of targets are searched, scanned or otherwise queried for one or more data satisfying the search criteria and sustainability factors. The targets can include data stores, controllers, network devices, websites, cloud-based data stores, and so forth. At 706, the results (e.g., data returned from the query) of the query are analyzed. The analysis includes locating a predetermined a sustainability score, or determining a sustainability score based on sustainability factors associated with the results. As discussed previously, users can assign weights or prioritization values to one or more sustainability factors, and the sustainability score can be calculated based on the user assigned weights.
At 708, the results are organized, ordered, or otherwise ranked according to their sustainability scores. For example, the results can be ranked from lowest sustainability score (e.g., more desirable) to highest sustainability score (e.g., least desirable). In addition, the rankings can be restricted to the results having sustainability scores below a threshold. At 710, one or more interfaces can be exposed to display the search criteria, sustainability criteria, weights, results, sustainability scores, rankings, or any combination thereof. Additionally or alternatively, the results can be transmitted, sent or otherwise communicated to another application or process. For example, an advertising application can obtain the results, and use them to provide targeted advertising services.
As used herein, the term “inference” refers generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Furthermore, inference can be based upon logical models or rules, whereby relationships between components or data are determined by an analysis of the data and drawing conclusions there from. For instance, by observing that one user interacts with a subset of other users over a network, it may be determined or inferred that this subset of users belongs to a desired social network of interest for the one user as opposed to a plurality of other users who are never or rarely interacted with.
Directed and undirected model classification approaches including, for example, naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.
As will be readily appreciated from the subject specification, the subject innovation can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing user behavior, receiving extrinsic information). Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to a predetermined criteria when to update or refine the previously inferred schema, tighten the criteria on the inferring algorithm based upon the kind of data being processed (e.g., financial versus non-financial, personal versus non-personal, . . . ), and at what time of day to implement tighter criteria controls (e.g., in the evening when system performance would be less impacted).
Referring now to
The system 900 also includes one or more server(s) 904. The server(s) 904 can also be hardware and/or software (e.g., threads, processes, computing devices). The servers 904 can house threads to perform transformations by employing the innovation, for example. One possible communication between a client 902 and a server 904 can be in the form of a data packet adapted to be transmitted between two or more computer processes. The data packet may include a cookie and/or associated contextual information, for example. The system 900 includes a communication framework 906 (e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s) 902 and the server(s) 904.
Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s) 902 are operatively connected to one or more client data store(s) 908 that can be employed to store information local to the client(s) 902 (e.g., cookie(s) and/or associated contextual information). Similarly, the server(s) 904 are operatively connected to one or more server data store(s) 910 that can be employed to store information local to the servers 904.
What has been described above includes examples of the innovation. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the subject innovation, but one of ordinary skill in the art may recognize that many further combinations and permutations of the innovation are possible. Accordingly, the innovation is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.
This application is a continuation of U.S. patent application Ser. No. 14/532,673, which was filed on Nov. 4, 2014, entitled “DYNAMIC SUSTAINABILITY SEARCH ENGINE”, which is a continuation of U.S. patent application Ser. No. 12/429,830, which was filed on Apr. 24, 2009, and issued as U.S. Pat. No. 8,892,540 on Nov. 18, 2014, entitled “DYNAMIC SUSTAINABILITY SEARCH ENGINE”, each of which is incorporated herein by reference in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
4039392 | Singh | Aug 1977 | A |
4300125 | Loshing et al. | Nov 1981 | A |
4341345 | Hammer et al. | Jul 1982 | A |
4383298 | Huff et al. | May 1983 | A |
4624685 | Lueckenotte et al. | Nov 1986 | A |
4827395 | Anders et al. | May 1989 | A |
5043929 | Kramer et al. | Aug 1991 | A |
5202996 | Sugino et al. | Apr 1993 | A |
5251205 | Callon et al. | Oct 1993 | A |
5297057 | Kramer et al. | Mar 1994 | A |
5646862 | Jolliffe et al. | Jul 1997 | A |
5736983 | Nakajima et al. | Apr 1998 | A |
5822207 | Hazama et al. | Oct 1998 | A |
5924486 | Ehlers et al. | Jul 1999 | A |
5983622 | Newburry et al. | Nov 1999 | A |
6012053 | Pant et al. | Jan 2000 | A |
6015783 | Von der Osten et al. | Jan 2000 | A |
6076108 | Courts et al. | Jun 2000 | A |
6263255 | Tan et al. | Jul 2001 | B1 |
6281784 | Redgate et al. | Aug 2001 | B1 |
6289252 | Wilson et al. | Sep 2001 | B1 |
6321983 | Katayanagi et al. | Nov 2001 | B1 |
6473893 | Kay et al. | Oct 2002 | B1 |
6507774 | Reifman et al. | Jan 2003 | B1 |
6633823 | Bartone et al. | Oct 2003 | B2 |
6701298 | Jutsen | Mar 2004 | B1 |
6732055 | Bagepalli et al. | May 2004 | B2 |
6747368 | Jarrett, Jr. | Jun 2004 | B2 |
6785592 | Smith et al. | Aug 2004 | B1 |
6857020 | Chaar et al. | Feb 2005 | B1 |
6859755 | Eryurek et al. | Feb 2005 | B2 |
7043316 | Farchmin et al. | May 2006 | B2 |
7274975 | Miller | Sep 2007 | B2 |
7277864 | Ohnemus et al. | Oct 2007 | B2 |
7409303 | Yeo et al. | Aug 2008 | B2 |
7451019 | Rodgers | Nov 2008 | B2 |
7477956 | Huang et al. | Jan 2009 | B2 |
7531254 | Hibbs et al. | May 2009 | B2 |
7565351 | Callaghan | Jul 2009 | B1 |
7587251 | Hopsecger | Sep 2009 | B2 |
7747416 | Deininger et al. | Jun 2010 | B2 |
7788189 | Budike, Jr. | Aug 2010 | B2 |
8010523 | Djabarov | Aug 2011 | B2 |
8068938 | Fujita | Nov 2011 | B2 |
8271363 | Branscomb | Sep 2012 | B2 |
8892540 | Walker et al. | Nov 2014 | B2 |
20010011368 | Graser et al. | Aug 2001 | A1 |
20020013744 | Tsunenari et al. | Jan 2002 | A1 |
20020026343 | Duenke | Feb 2002 | A1 |
20020052666 | Fukatsu et al. | May 2002 | A1 |
20020066072 | Crevatin | May 2002 | A1 |
20020099464 | O'Connor et al. | Jul 2002 | A1 |
20020099804 | O'Connor et al. | Jul 2002 | A1 |
20020116239 | Reinsma et al. | Aug 2002 | A1 |
20020128933 | Day et al. | Sep 2002 | A1 |
20020168621 | Cook et al. | Nov 2002 | A1 |
20020169582 | Eryurek et al. | Nov 2002 | A1 |
20020178047 | Or et al. | Nov 2002 | A1 |
20020198755 | Birkner et al. | Dec 2002 | A1 |
20030014500 | Schleiss | Jan 2003 | A1 |
20030028527 | Crosby et al. | Feb 2003 | A1 |
20030061091 | Amaratunga et al. | Mar 2003 | A1 |
20030088370 | Bagepalli et al. | May 2003 | A1 |
20030110065 | Twigge-Molecey | Jun 2003 | A1 |
20030110369 | Fish et al. | Jun 2003 | A1 |
20030171851 | Brickfield et al. | Sep 2003 | A1 |
20030221119 | Geiger et al. | Nov 2003 | A1 |
20040088119 | Landgraf | May 2004 | A1 |
20040107345 | Brandt et al. | Jun 2004 | A1 |
20040117240 | Ness et al. | Jun 2004 | A1 |
20040143467 | McAllister et al. | Jul 2004 | A1 |
20040158506 | Wille | Aug 2004 | A1 |
20040199294 | Fairlie et al. | Oct 2004 | A1 |
20040205412 | Staron et al. | Oct 2004 | A1 |
20040249697 | Ohnemus et al. | Dec 2004 | A1 |
20040260489 | Mansingh et al. | Dec 2004 | A1 |
20040261673 | Allen et al. | Dec 2004 | A1 |
20050015287 | Beaver | Jan 2005 | A1 |
20050034023 | Maturana et al. | Feb 2005 | A1 |
20050065971 | Honda | Mar 2005 | A1 |
20050143865 | Gardner | Jun 2005 | A1 |
20050144154 | DeMesa et al. | Jun 2005 | A1 |
20050171910 | Wu et al. | Aug 2005 | A1 |
20050198241 | Pavlik et al. | Sep 2005 | A1 |
20050198333 | Dinges | Sep 2005 | A1 |
20050234904 | Brill et al. | Oct 2005 | A1 |
20050278296 | Bostwick | Dec 2005 | A1 |
20060026145 | Beringer et al. | Feb 2006 | A1 |
20060248002 | Summer et al. | Nov 2006 | A1 |
20070038646 | Thota | Feb 2007 | A1 |
20070073750 | Chand et al. | Mar 2007 | A1 |
20070078736 | Chand et al. | Apr 2007 | A1 |
20070168213 | Comrie | Jul 2007 | A1 |
20070226068 | Keil et al. | Sep 2007 | A1 |
20070283030 | Deininger et al. | Dec 2007 | A1 |
20080015975 | Ivchenko et al. | Jan 2008 | A1 |
20080033841 | Wanker | Feb 2008 | A1 |
20080046387 | Gopal et al. | Feb 2008 | A1 |
20080046407 | Shah et al. | Feb 2008 | A1 |
20080059457 | Ohnemus et al. | Mar 2008 | A1 |
20080059897 | Dilorenzo | Mar 2008 | A1 |
20080079560 | Hall et al. | Apr 2008 | A1 |
20080127779 | Morales Cerda et al. | Jun 2008 | A1 |
20080154749 | D'hooghe et al. | Jun 2008 | A1 |
20080255889 | Geisler et al. | Oct 2008 | A1 |
20080270272 | Branscomb | Oct 2008 | A1 |
20080272934 | Wang et al. | Nov 2008 | A1 |
20080319812 | Sousa et al. | Dec 2008 | A1 |
20090083843 | Wilkinson, Jr. et al. | Mar 2009 | A1 |
20090099887 | Sklar et al. | Apr 2009 | A1 |
20090100159 | Extra | Apr 2009 | A1 |
20090132176 | McConnell et al. | May 2009 | A1 |
20090138415 | Lancaster | May 2009 | A1 |
20090177505 | Dietrich et al. | Jul 2009 | A1 |
20090222307 | Beaver | Sep 2009 | A1 |
20090281674 | Taft | Nov 2009 | A1 |
20090281677 | Biotich et al. | Nov 2009 | A1 |
20090313164 | Hoglund | Dec 2009 | A1 |
20090319315 | Branscomb | Dec 2009 | A1 |
20100023360 | Nadhan | Jan 2010 | A1 |
20100030601 | Warther et al. | Feb 2010 | A1 |
20100042455 | Liu et al. | Feb 2010 | A1 |
20100057480 | Arfin et al. | Mar 2010 | A1 |
20100088136 | Cheng et al. | Apr 2010 | A1 |
20100100402 | Shah | Apr 2010 | A1 |
20100100405 | Lepore et al. | Apr 2010 | A1 |
20100131343 | Hamilton | May 2010 | A1 |
20100138003 | August et al. | Jun 2010 | A1 |
20100138279 | Cohen | Jun 2010 | A1 |
20100217642 | Crabtree et al. | Aug 2010 | A1 |
20100217651 | Crabtree et al. | Aug 2010 | A1 |
20100218108 | Crabtree et al. | Aug 2010 | A1 |
20100249975 | Rezayat | Sep 2010 | A1 |
20100262445 | DeSorbo | Oct 2010 | A1 |
20100274367 | Kaufman et al. | Oct 2010 | A1 |
20100274377 | Kaufman et al. | Oct 2010 | A1 |
20100274602 | Kaufman et al. | Oct 2010 | A1 |
20100274603 | Walker et al. | Oct 2010 | A1 |
20100274611 | Kaufman et al. | Oct 2010 | A1 |
20100274612 | Walker et al. | Oct 2010 | A1 |
20100274629 | Walker et al. | Oct 2010 | A1 |
20100275147 | Kaufman et al. | Oct 2010 | A1 |
20100292856 | Fujita | Nov 2010 | A1 |
20100314940 | Palmer et al. | Dec 2010 | A1 |
20100318233 | Yunes et al. | Dec 2010 | A1 |
20100332373 | Crabtree et al. | Dec 2010 | A1 |
20110046800 | Imes et al. | Feb 2011 | A1 |
20110071721 | Gilfillan et al. | Mar 2011 | A1 |
20110172838 | Pai et al. | Jul 2011 | A1 |
20110273022 | Dennis et al. | Nov 2011 | A1 |
20130096727 | Brandt | Apr 2013 | A1 |
Number | Date | Country |
---|---|---|
0977137 | Feb 2000 | EP |
WO2004074954 | Sep 2004 | WO |
WO2008011427 | Jan 2008 | WO |
Entry |
---|
Kiritsis D, et al., Research issues on product lifecycle management and information tracking using smart embedded systems. Advanced Engineering Informatics, Elsevier Lnkd—DOI:10.1016/J, AEI2004.09.005, vol. 17, No. 3-4, Jul. 1, 2003, pp. 189-202, XP004595481 ISSN: 1474-0346. |
Dillenburg, S. et al., “Aproaching socially responsible investment with a comprehensive ratings scheme: total social impact,” Journal of Business Ethics 43.3 (2003): 167-177. |
GE Energy, “Energy and Asset Performance—Fact Sheet” Sep. 2005, General Electric Company, Published online at [http://www.gepower.com/prod_serv/serv/industrial_service/en/downloads/gea14163_eap.pdf], retrieved Apr. 13, 2009, 2 pages. |
Jawahir, I.S., et al, “Total life-cycle considerations in product design for sustainability: A framework for comprehensive evaluation,” Proc. 10th Int. Research/Expert Conf. (TMT 2006), Barcelona, Spain, 2006. |
Yang, et al., “Eco-Design for Product Lifecycle Sustainability”, IEEE International Conference on Industrial Informatics, 2006, pp. 548-553. |
Y-S Ma et al., Product Lifecycle Analysis and Optimization in an Eco-value Based, Sustainable and Unified Approach. Industrial Informatics, 2006 IEEE International Conference on, IEEE, Pl. Aug. 1, 2006, pp. 537-541 XP031003409, ISBN: 978-0-7803-9700-2. |
Abb. “Energy Management Solution for the Process Industry—Energy Management and Optimization.” Apr. 6, 2007, Published online at [http://library.abb.com/global/scot/scot313.nsf/veritydisplay/5e48efb88a7e1cbac125734600737b02/$File/3BF1405000R4001_en_Energy_Management_and_Optimization_3.5.pdf], retrieved Apr. 13, 2009. 12 pages. |
Seref Erkayhan Ed—Ding Zhen-Hua et al., The Use of RFID enables a holistic information management within Product Lifecycle Management (PLM). RFID Eurasia, 2007 1st Annual, IEEE, Pl Sep. 1, 2007, pp. 1-4, XP031153342. ISBN: 978-975-01-5660-1. |
EPO: Notice from European Patent Office dated Oct. 1, 2007 concerning business methods. Official. |
Journal of the European Patent Office, vol. 30, No. 11, dated Nov. 1, 2007, pp. 592-593. |
Kennedy, Pat, et al., “In Pursuit of the Perfect Plant—A Business and Technical Guide”, Apr. 2008, Chapter 9—Energy Management, pp. 251-283; published by Evolved Technologist Press, New York, New York, USA. |
Kouloura, et al., “A Systems Approach to Corporate Sustainability in Energy Management of Industrial Units”, IEEE Systems Journal, vol. 2, No. 4, Dec. 2008, pp. 442-452. |
Abb, “Energy Management and Optimization for the Process Industries—Advanced IT Tools for Planning, Monitoring, Controlling, and Reporting Energy System Operations.” Published online at [http://library.abb.com/global/scot/scot296.nsf/veritydisplay/bd2a898a24267c46c12571c70070a851/$File/3BF1402000R3001_en_Advanced_IT_Tools_for_Energy_Management.pdf], retrieved Apr. 13, 2009, 6 pages. |
European Search Report for European Application No. 10160810, dated Aug. 6, 2010, 2 pgs. |
European Search Report for European Application No. 10160649.9-1238 dated Sep. 23, 2010, 8 pgs. |
European Search Report for European Application No. 10160581.4-1238 dated Sep. 23, 2010, 8 pgs. |
European Search Report for European Application No. 10160673.9-1238 dated Sep. 23, 2010, 9 pgs. |
European Search Report for European Application No. 10160811.5 dated Sep. 20, 2010, 9 pgs. |
European Search Report for European Application No. 10160737, dated Nov. 4, 2010, 9 pages. |
European Search Report for European Application No. 101605855-1527 / 2254061 dated Dec. 20, 2010, 9 pages. |
Dietmair, A., “Energy Consumption Modeling and Optimization for Production Machines,” ICSET 2008, pp. 574-579. |
File History for U.S. Appl. No. 12/429,813 as of Oct. 20, 2014. |
Jayal, A.D. et al., “Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels, CIRP Journal of Manufacturing Science and Technology,” vol. 2, Issue 3, 2010, pp. 144-152, ISSN 1755-5817. |
Number | Date | Country | |
---|---|---|---|
20180239764 A1 | Aug 2018 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 14532673 | Nov 2014 | US |
Child | 15958922 | US | |
Parent | 12429830 | Apr 2009 | US |
Child | 14532673 | US |