Patent Application
20080046382
There are shown in the drawings, embodiments which are presently preferred, it being understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown.
The PPI 126 is not limited to CPI data sources for obtaining price change information. Other data sources can be used, such as sources used to calculate the Producer Price Index (PPI), the Consumer Confidence Index (CCI), the Index of Consumer Sentiment (ICS), the Consumer Spending and Saving Index (CSSI), the Commodities Research Bureau (CRB) index, the Rogers Raw Material Index (RRMI), the Goldman Sachs Commodities Index (GSCI), the Dow Jones-AIG Commodities Index (DJ-AIG), and the like. Additionally, the price change information can be calculated based upon multiple different sources and derivatives of the same. For the sake of simplicity, CPI based data sources and expense categories are generally utilized throughout the present invention to illustrate concepts involved in calculating the PPI 126.
A first stage in generating the PPI 126 can include identifying a spending data store 110 in which spending records collectively referred to as a spending data set are recorded. The spending data set can include credit card records, debit card records, checking account records, records maintained by a personal accounting software program, banking records, and any other set of records containing individual expenditures and/or records sufficient to model personal spending habits.
Mapping engine 120 will typically be required to convert records in the data store 110 to a format appropriate for calculating the PPI 126. Just like the CPI, the PPI 126 can be calculated based upon a discrete set of expense categories, which can be identical to the expense categories used to calculate the CPI (or those expense categories used to calculate other indices upon which PPI 126 is based) or can be derived from CPI based categories. For example, multiple CPI expense categories can be grouped together (in a hierarchy) to form a parent of CPI based expense categories, which can be used to calculate PPI 126. A parent category will be at a higher granularity level than child CPI based categories. The mapping engine 120 can utilize any variety of data mining, data migration, transcoding, and format conversion tools to normalize content of data store 110 into suitable expense categories.
The mapping engine 120 can, for example, convert expenses recorded at a store-level of granularity into an appropriate expense category. As shown in table 150, expenses, such as those appearing within a credit card expense report, often include a store and an amount. For example, records based upon credit card billings can include entries for EXXON in an amount of $80, BEST BUY for $100, PUBLIX for $100, and SUNPASS for $20. Since each billing entity is associated with a particular type of good and/or service, a mapping can be performed from a store name to a PPI expense category.
Table 152 shows a sample mapping, which can be obtained from an expenditure interpretation database 112. Multiple different mappings can be provided for each store, in embodiments where different granularity levels of expense categories can be selectively used. For example, table 152 can include columns for Store, Mapping A, and Mapping B. Rows in table 152 can include entries for EXXON having an entry in Mapping A for vehicle fuel and having a higher level mapping (Mapping B) to Energy. Other illustrative rows can include BEST BUY mapped to Electronics, PUBLIX mapped to supermarket food, and SUNPASS mapped to highway tolls.
When used to map store-level expense reports to expense categories, the mapping engine 120 is not limited to a one-to-one mapping. Instead, a store can be associated with multiple expense categories, each expense category receiving a percentage of expenses attributable to that store. For example, consumers often purchase food items, cigarettes, and other goods in gas station convenient marts. Food, gas, and cigarettes can each fall into different expense categories, such as “food”, “energy” and “recreation”. Expenses attributable to EXXON can therefore be allocated across these categories, such as eight percent to food, ninety percent to energy, and two percent to recreation. Percentage allocations can be adjusted based on national shopping habits for that store, store specific sales, and/or individual shopping habits at a particular store.
The mapping engine 120 can map expenses to expense categories regardless of the granularity level at which expenses are recorded. For example, when records in the spending data store specify product-level expenses, the expenditure interpretation database 112 can map products to expense categories. Results from the mapping engine 120 can include category based expenses, which are representative of an individual's spending habits for a fixed period of time. These category level expenses can be referred to as a personal expense profile.
It should be noted that statistically accurate spending habits can be obtained from a representative sample of expenses. Consequently, the mapping engine 120 need not access and/or process a complete data set for an individual's expenses, but can instead use a subset of information. For example, if an individual's spending habits using a credit card are approximately equivalent to their spending habits in general, using credit card expenses alone can result in a relatively accurate calculation of PPI 126.
Once personal expenses have been mapped to expense categories, the weighing engine 122 can assign weights to each expense category based upon the personal expense profile. Table 154 shows a list of expense categories, a personal category percentage, and a CPI category percentage. The categories used in table 154 include food and beverages, housing, apparel, transportation, medical care, recreation, energy, education and communication, and other goods and services. These categories are relatively high-level categories, which are parent categories of CPI categories. These parent categories can be used in lieu of finer grain categories in embodiments where the expenses recorded in data store 110 cannot be accurately mapped to more fine-grained categories.
In one embodiment, the weighing engine 122 can assign a multiplier or weight called the personal category weight to each category that is exclusively based upon the personal category percentage. Weighing engine 122 can also combine this personal category percentage with other factors, such as historically gathered personal expenditure data and the CPI percentage, to compute the personal category weight. Combining multiple factors in addition to the personal category percentage can ensure that abnormalities inherent in a particular expense period do not dominate assigned personal category weights. For example, if in a three month period, an individual become uncharacteristically sick and is forced to seek medical care, the expenses related to medical care for that period will be uncharacteristically high.
The index calculation engine 124 can use the personal category weights and price movement data obtained from a price movement database 114 to generate the PPI 126. The price movement database 114 can include price movement values on a category-by-category basis. For example, and as shown by table 156, food and beverage costs may have risen for a given period by 2.3 percent, housing increased by 2.1 percent, apparel increased by 3.4 percent, transportation increased by 5.6 percent, medical care increased by 8.3 percent, recreation increased by 3.4 percent, energy increased by 10.2 percent, education decreased by 15 percent, and other goods and services could have increased by 2.1 percent. The Bureau of Labor Statistics (BLS) publishes and updates price movement values, which can be used by the index calculation engine 124 for embodiments using a CPI based data source. Other standard publications can be used for obtaining price change information for other embodiments of the present invention, where the publications used depend upon the data sources and algorithms involved in computing price changes for each expense category.
Many different algorithms can be used by the index calculation engine 124 to calculate the PPI 126. For example, the PPI 126 can be a weighed average of the categorized price movements, where the weighing is based on the personal category weights. It should be readily appreciated that any algorithm used to calculate CPI (for embodiments where expense categories are CPI based categories) can also be used to calculate the PPI 126. For example, the index calculation engine 124 can use algorithms that utilize chained weights (as is used to calculate the C-CPI-U index) or algorithms that utilize a base year (as is used to calculate the CPI-U index) to calculate the PPI 126.
Although examples provided thus far have based the PPI 126 upon an individual's expenses, the concept of the PPI 126 is not so limited. Instead, the PPI 126 can be applied to any set of people for whom spending data is available. This set of people represents a set of people having common spending habits and/or a set of people who share expenses. For example, the PPI 126 can be an indicator established for a household that includes multiple residents and one or more wage earners. In another example, the PPI 126 can be an indicator established for a communal living group, such as a fraternity/sorority consisting of multiple students who communally share many living expenses as part of their group dues. In still another example, the PPI 126 can be applied to a set of company employees, where spending records available through a company credit union can provide a reasonable estimate of employee spending habits. A company-level PPI 126 value can be tied to employee contracted benefits, which is often the case for benefit packages exempting that conventionally a CPI value is used instead of the PPI 126.
Data store 110 and databases 112-114 can be a physical or virtual storage spaces configured to store digital information. Data store 110 and databases 112-114 can be physically implemented within any type of hardware including, but not limited to, a magnetic disk, an optical disk, a semiconductor memory, a digitally encoded plastic memory, a holographic memory, or any other recording medium. Each of data store 110 and databases 112-114 can be a stand-alone storage unit as well as a storage unit formed from a plurality of physical devices. Additionally, information can be stored within each of data store 110 and databases 112-114 in a variety of manners. For example, information can be stored within a relational database structure or can be stored within one or more files of a file storage system, where each file may or may not be indexed for information searching purposes. Further, each of data store 110 and databases 112-114 can utilize one or more encryption mechanisms to protect stored information from unauthorized access.
Method 200 can begin in step 205, wherein data representative of an individual's spending habits can be gathered. In step 210, the gathered data can be normalized in accordance with expense categories. In step 215, a personal spending profile for the expense categories can be developed based upon the normalized data. The personal spending profile can show a relative percentage spent for each expense category within a defined period of time. The period of time should be sufficient to predict spending habits of the individual. In step 220, categories can be weighed according to the personal spending profile. In step 225, a personal price index can be computed using personal category weights and price movement values for each category. Each price movement value can show how the price for items of that expense category have increased and/or decreased since a base time period.
Method 300 can begin in step 305, when a customer initiates a service request. The service request can be a request for a service agent to configure or troubleshoot a software system that calculates or uses a PPI. In step 310, a human agent can be selected to respond to the service request. In step 315, the human agent can analyze a customer's current system and can develop a solution. In step 320, the human agent can configure the client system so that the system can generate and/or utilize a personal price index. For example, the human agent can link the client's system to a personal spending data store and to a data source for Bureau of Labor Statistics (BLS) CPI data. The human agent can also map data from a financial accounting program, an online banking source, or other source of personal spending information so that a PPI can be automatically calculated based upon the mapped data. In one embodiment, the automatically calculated PPI can be a parameter used by another client program, such as an investment management program. In step 325, the human agent can complete the service activities.
It should be noted that while the human agent may physically travel to a location local to adjust the customer's computer or application server, physical travel may be unnecessary. For example, the human agent can use a remote agent to remotely manipulate the customer's computer system.
The present invention may be realized in hardware, software, or a combination of hardware and software. The present invention may be realized in a centralized fashion in one computer system or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software may be a general purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein.
The present invention also may be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods. Computer program in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form.
This invention may be embodied in other forms without departing from the spirit or essential attributes thereof. Accordingly, reference should be made to the following claims, rather than to the foregoing specification, as indicating the scope of the invention.
