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 . The following is a Final Office Action. Claims 1-9 are rejected below.
Applicant’s Amendments
Applicant’s amendments are acknowledged.
Applicant’s Arguments
Previous objections are withdrawn based on Applicant’s amendments.
Applicant’s arguments with respect to 101 Rejections have been fully considered but are not persuasive.
Applicant argues, “As described in the specification, there is an existing technological environment for forecasting the service demand potential for the service for each area. However, there is a need to predict the service demand potential by considering not only information related to own-company service provision but also information related to the service provision of competing companies.” Therefore, Applicant emphasizes that the present claims as a whole provide an improvement to this technological environment.
Examiner responds the considering of further information for predicting the service demand potential further narrows the abstract idea and still within a business process.
Applicant argues, “Aside from achieving this improvement, technical features of the claims are directed to the construction and implementation of the machine learning model. Additionally, the claims achieve the unique practical application of inputting characteristic amounts for dominant areas for teaching the machine learning to predict demand is more than a general use of machine learning. For instance, amended Claim 1 now specifies what the "characteristic amount" is referring to by reciting "perform machine learning using a characteristic amount representing characteristics of the selected dominant area as an explanatory variable and the number of service provision results of the own- company service in the selected dominant area as an objective variable, and constructs a machine learning model for predicting a demand for service provision in the dominant area, the characteristics including at least one of daytime and nighttime population by sex age in the dominant area aggregated on a time basis based on at least one of (a) the location information of terminals used by users, (b) a number of member stores corresponding to payment located in the dominant area, or (c) a characteristic amount representing the characteristics of the dominant area, the characteristics of areas including at least one of commercial areas, residential areas, industrial areas, urban centers, or suburban areas."
Examiner responds the limitations above further describe the information used in the model. While additional information used in the model narrow a model it does not improve machine learning technology and thus still not meaningful beyond a general link to machine learning technology.
Applicant argues, “Furthermore, amended Claim 1 recites "a display configured to output the service demand potential in the non-dominant area...This additional hardware element also makes it clear that the claims integrate any abstract idea into a practical application.
Examiner responds the limitation, “a display configured to output the service demand potential in the non-dominant area” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h); The display fails to change the function of the display in a meaningful way that goes beyond a general link to display technology or beyond generic and routine display functions for displaying information.
Applicant’s arguments with respect to 103 Rejections have been fully considered but are not persuasive.
Applicant argues, “While Gregory is related to determining a target market in a geographical area, Gregory does not disclose that a machine learning model is being constructed using specific characteristics of a company's past activity in geographic areas for which it is already dominant compared to another specific company, and then when utilizing the constructed machine learning model, using specific characteristics of a company's past activity in geographic areas for which it is already non-dominant, to determine the potential service demand.“
Examiner responds Gregory discloses machine learning is used to create a model using information from a first area, and apply the model to information associated with a second area to determine a market index in the second area based on the model (0078). Gregory determines particular areas (e.g., areas associated with market indices that satisfy a threshold (relative dominant area based on threshold) and areas of a darker shade areas indicating a higher market index (relative dominance) (0019, 0083, Figure 1E) Gregory does not explicitly state the “relative dominance of the service provision results of the own-company service is “in comparison to the other-company service.” ICN discloses market rivals with one supplier’s sales market share greater than (ie. dominant) than another supplier’s sales market share (Page 26, 36). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate ICN’s relative dominance (superior market share) of one supplier’s service provision results over another supplier to Gregory’s threshold, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Applicant’s arguments with respect to newly added amendments have been fully considered but are moot in view of Lee (2016/0063526).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically Claims 1-9 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-9 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claims 1 and 9 include limitations with the functionality:
Acquiring the number of service provision results...
Calculating the relative index of the own-company service to the other-company service...
Selecting a dominant area in which the number of service provision results of the own-company service is already relatively dominant...
Constructing a model for predicting demand for service provision in the dominant area...
Predicting a service provision prediction number of the own-company service in a case where a non-dominant area is assumed to be a dominant area by inputting a characteristic amount representing the characteristic of the non-dominant area which is not the dominant area into the constructed model...
which is an abstract idea reasonably categorized as
Mental processes, because each of the limitations describes concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Certain methods of organizing human activity – because the limitations describe the fundamental economic principles or practices of market forecasting, and managing personal behavior (service provision results) (including social activities, teaching, and following rules or instructions); and
Mathematical concept, because the calculating a ratio describes mathematical relationships, mathematical formulas or equations mathematical calculations
Similarly, Claims 2-8 further narrow the same abstract concept identified above related to mental processes, certain methods of organizing human activity, and mathematical concepts. As a result, Claims 1-9 recite an abstract idea related to mental processes, certain methods of organizing human activity, and mathematical concepts under Step 2A Prong One.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claims 1 and 9 include various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include processing circuitry of a device. When considered in view of the claim as a whole, Examiner submits that the additional elements do not integrate the abstract idea into a practical application because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f).
The “performing machine learning using a characteristic amount representing a characteristic of the selected dominant area as an explanatory variable and the number of service provision results of the own-company service in the selected dominant area as an objective variable, and constructs a machine learning model” “the constructed machine learning model” and “the characteristics including...”
generally link the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h), and do not change machine learning is a meaningful way beyond a general link to machine learning technology or generic and routine machine learning functions (ie. constructing a machine learning model using an explanatory variable and an objective variable). Because the claimed machine learning functions are broadly claimed and are directed to basic functions of machine learning technology, the claims do not improve the functioning of the technology of machine learning.
Examiner notes “the characteristics including...” are further descriptive of the information used in the model. While improving accuracy of a machine learning model it is still not meaningful beyond a general link to machine learning technology and does not recite an improvement to the technology of machine learning.
The limitation, “a display configured to output the service demand potential in the non-dominant area” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The display fails to change the function of the display in a meaningful way that goes beyond a general link to display technology or beyond generic and routine display functions for displaying information. Because the claimed display functions are broadly claimed and are directed to the basic functions of display technology, the claims do not improve the functioning of the display and do not improve the technical field of visualization.
Claims 2-8 do not include any additional elements beyond those recited above. As a result, Claims 1-9 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1 and 9 include various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include processing circuitry of a device. When considered in view of the claim as a whole, Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
As stated above- “performing machine learning using a characteristic amount representing a characteristic of the selected dominant area as an explanatory variable and the number of service provision results of the own-company service in the selected dominant area as an objective variable, and constructs a machine learning model” “the constructed machine learning model” and “the characteristics including...” – while improving accuracy of a machine learning model - are not meaningful beyond a general link to machine learning technology or generic and routine machine learning functions and do not recite an improvement to, nor does the Spec describe improvements by comparison to prior art technology of machine learning.
Examiner notes “the characteristics including...” are further descriptive of the information used in the model. While improving accuracy of a machine learning model it is still not meaningful beyond a general link to machine learning technology and do not recite an improvement to the technology of machine learning.
The limitation, “a display configured to output the service demand potential in the non-dominant area” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).. Because the claimed display functions are broadly claimed and are directed to the basic functions of display technology, the claims do not improve the functioning of the display and do not improve the technical field of visualization.
Claims 2-9 do not include any additional elements beyond those recited above. As a result, Claims 2-9 do not include additional elements amounting to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1. As a result, Claims 1-9 do not amount to significantly more than the abstract idea.
Accordingly, Claims 1-9 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Gregory (20180144351) in view of ICN Merger Guidelines Workbook, Prepared for the Fifth Annual ICN Conference in Cape Town. April 2006, hereinafter “ICN” in view of Lee (20160063526).
Regarding Claim 1, Gregory discloses:
A service demand potential prediction device comprising: processing circuitry configured to: (0038, Figure 3 – device, processor)
acquire a number of service provision results for each of a target own-company service and an other-company service used for calculating a relative index with respect to the number of service provision results, for each predetermined area; (0011 – ...an insight platform (acquisition unit) to...determine market indices based on the data element scores.....A market index (relative index), as described elsewhere herein, may include information that identifies a potential value associated with a geographic area, identifies the likelihood of an entity realizing the potential value, (service provision results for target own- company) and/or the like; 0087 - ....insight platform 230 may determine a viability of offering the item in association with the area (e.g., an estimate as to the probability of the entity realizing potential revenue, gaining market share (service provision results for target own-company) or the like)
0011, 0061, 0087, Figure 1B-E – insight platform acquires other data source’s data elements (ie. concentration values, market share, etc.) (service provision results for other-company service) for each area
[0061] ... a data element may include payer concentration data, such as information that identifies a number of entities associated with a particular area, a market share of each of the respective entities, particular items offered by the respective entities..
calculates the relative index of the own-company service for each area based on the acquired number of service provision results for each of the target own-company service and the other-company service, for each area; (0011, 0061, Figure 1D-E (market index values) - ...an insight platform (calculation unit) to...determine market indices (relative indices) based on the data element scores.....A market index, as described elsewhere herein, may include information that identifies a potential value associated with a geographic area, identifies the likelihood of an entity realizing the potential value, (service provision results for target company) and/or the like;
select a dominant area in which the number of service provision results of the own-company service is already relatively dominant, based on information including the calculated relative index for each area; (0085 - insight platform 230 may determine particular areas (e.g., areas associated with market indices that satisfy a threshold (dominant area based on threshold))
0019, 0083, Figure 1E - a darker shaded portion of the heat map indicating a higher market index relative to a lighter shaded portion, or vice versa;
0083 - user device 210 may provide information that identifies areas associated with particular market indices (e.g., the top ten greatest market indices, the top ten lowest market indices, or the like).
perform machine learning using a characteristic amount representing characteristics of the selected dominant area as an explanatory variable and the number of service provision results of the own-company service in the selected dominant area as an objective variable, and constructs a machine learning model for predicting a demand for service provision in the dominant area, the characteristics including at least population by age in the dominant area [0078] In some implementations, insight platform 230 may determine a market index, for the area, based on a model. For example, insight platform 230 may use machine learning techniques to analyze data (e.g., training data, such as historical data, etc.) and create models. ... the technique may receive known data element scores (characteristic amount) and a known market index based as input, and may associate the known data element scores and the known market index (e.g., to train a model). .... insight platform 230 may create a model using information associated with a first area (e.g., training data), and may use the model in association with information relating to a second area.
0059 - a data element may include demographic data, such as information that identifies a number of individuals associated with an area, ages of the individuals, races of the individuals, income levels of the individuals, employment information for the individuals, or the like.
0011 - A market index (relative index), as described elsewhere herein, may include information that identifies a potential value associated with a geographic area, identifies the likelihood of an entity realizing the potential value, (service provision results of the own-company)
predicts a service provision prediction number of the own-company service in a case where a non-dominant area is assumed to be a dominant area by inputting a characteristic amount representing the characteristic of the non-dominant area which is not the dominant area into the constructed machine learning model, and sets the obtained service provision prediction number as a service demand potential in the non- dominant area; and [0079] .... Additionally, or alternatively, insight platform 230 may identify a first area and a second area, may determine that the second area is similar to the first area (e.g., similar geographic location, similar demographic information, or the like), (characteristic of non-dominant area) and may determine a market index for the second area based on data element scores associated with the first area (e.g., may use the data element scores associated with the first area, may combine data element scores, may weigh the data element scores, or the like).
Based on 0018-0019, Figure 1D-E – if the first area is identified as a “higher market index,” a new second area with similar demographical information would receive a similar “higher market index”
0019 - As shown in FIG. 1E, and by reference number 135, the insight platform may provide information that identifies the market indices to the user device (e.g., the user device may provide the information for display). In some implementations, the market indices may be displayed in the form of a geographic heat map that may include various colors and/or shades that may visually indicate the market indices. For example, a darker shaded portion of the heat map may indicate a higher market index relative to a lighter shaded portion, or vice versa....
a display configured to output the service demand potential in the non-dominant area (0082-0083 - ...insight platform 230 may provide, to user device 210 (e.g., which may provide information for display via a user interface), information that identifies the market index (e.g., to cause user device 210 to provide the information for display).
0011 - A market index (relative index), as described elsewhere herein, may include information that identifies a potential value associated with a geographic area, identifies the likelihood of an entity realizing the potential value)
Gregory does not explicitly state: the relative index indicates “a relative ratio of the own-company service to the other-company service with respect to the number of service provision results.”
Examiner interprets the relative ratio based on Claim 4 “a ratio of the number of service provision results of the own- company service to the sum of the number of service provision results of the other-company service and the number of service provision results of the own-company service”.
ICN discloses two entities as the only two entities in a market: Entity AB and Entity C. Entity AB’s market share is based on the ratio of its total sales divided by the sum of both Entities AB and C’s total sales (service provision results of the own-company service and other-company service)
Page 26 (Bottom) – “Market shares may be based on the total sales (number of service provision results) (or output or some other measure) of the product, in a defined area, to be held by the merging firms and each of their rivals in the relevant market...”
Page 36 - Even where the merging parties’ combined market shares appear reasonably low, for example below 25 per cent, a merger may still raise a competition issue. For example, supplier A (14 per cent share) merges with supplier B (10 per cent), leaving only two suppliers, the merged entity AB (24 per cent) and C (76 per cent)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gregory’s relative index to include ICN’s ratio of the service provision results of the own-company service to the sum of the service provision results of the own-company and other-company service, helping a merged entity determine market share and evaluate the status of a merger (ie. anti-competitive) (Page 4, 8).
Gregory does not explicitly state the “relative dominance of the service provision results of the own-company service (ie. meeting a threshold) is “in comparison to the other-company service.” ICN discloses market rivals with one supplier’s sales market share greater than (ie. dominant) than another suppliers market share (Page 26 (Bottom) – “Market shares may be based on the total sales (number of service provision results) (or output or some other measure) of the product, in a defined area, to be held by the merging firms and each of their rivals in the relevant market...”
Page 36 - ...supplier A (14 per cent share) merges with supplier B (10 per cent), leaving only two suppliers, the merged entity AB (24 per cent) and C (76 per cent) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate ICN’s relative dominance (superior market share) of one supplier’s service provision results over another supplier to Gregory’s threshold, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Gregory does not explicitly state: Lee, directed to forecasting the number of shoppers at a merchant’s store (0003), discloses: the population is at least one of daytime and nighttime by sex aggregated on a time basis based on at least one of (a) the location information of terminals used by users, (b) a number of member stores corresponding to payment located in the dominant area, or (c) a characteristic amount representing the characteristics of the dominant area, the characteristics of areas including at least one of commercial areas, residential areas, industrial areas, urban centers, or suburban areas (0036- retrieve from one or more databases a second set of information comprising merchant information (e.g., merchant name and merchant geolocation) (location information of terminals of users) of one or more merchants. Figure 7-8; 0098- FIG. 7 shows illustrative transactions at a merchant that have been tabulated according to the following areas: payment card number, date of payment card transaction, time of payment card transaction, merchant name, merchant location, gender of payment card holder, and age of payment card holder. 0099 - FIG. 8 shows a summary in a half day block of part of the information in FIG. 7. For the periods from 9:00 am-12:00 Noon and from 12:00 Noon-9:00 pm, (daytime) the summarized information shows the number of total payment card transactions, the total amount of the payment card transactions, the number of payment card holder customers, the payment card holder customer per time period (or hourly) metric, and gender of the payment card holder. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gregory’s in view of ICN’s population to include Lee’s daytime by sex aggregated on a time basis based on the location information of terminals used by users, helping create a predictive behavior model for merchant to customize promotions to shoppers and helping shoppers decide on times of future visits (0038, 0105)
Claim 2 stands rejected based on the same citations and rationale as applied to Claim 1. Examiner notes the demographic information (In Claim 2) could be used for the attribute data being collected with the relative index in a dominant area to train the model to determine a relative index (ie. market index) of a new (ie non-dominant) area with similar demographic information (0059, 0079)
0059 - a data element may include demographic data, such as information that identifies a number of individuals associated with an area, ages of the individuals, races of the individuals, income levels of the individuals, employment information for the individuals, or the like.
0079 - .... Additionally, or alternatively, insight platform 230 may identify a first area and a second area, may determine that the second area is similar to the first area (e.g., similar geographic location, similar demographic information (user attribute information; characteristic of non-dominant area with respect to target user attribute) or the like), and may determine a market index for the second area based on data element scores associated with the first area (e.g., may use the data element scores associated with the first area, may combine data element scores, may weigh the data element scores, or the like).
Regarding Claim 3, Gregory discloses: The service demand potential prediction device according to claim 1, wherein the display is further configured to output: a difference between the service demand potential and the number of service provision results in the non-dominant area. (0082-0083 - ...insight platform 230 may provide, to user device 210 (e.g., which may provide information for display via a user interface), information that identifies the market index (e.g., to cause user device 210 to provide the information for display).
0079 - insight platform 230 may identify a first area and a second area, may determine that the second area is similar to the first area (e.g., similar geographic location, similar demographic information, or the like), and may determine a market index for the second area based on data element scores associated with the first area (e.g., may use the data element scores associated with the first area, may combine data element scores, may weigh the data element scores, or the like).
[0019] ... the market indices may be displayed in the form of a geographic heat map that may include various colors and/or shades that may visually indicate the market indices. For example, a darker shaded portion of the heat map may indicate a higher market index relative to a lighter shaded portion, or vice versa. (a number of service provision results in the non-dominant area) In some implementations, the user may interact with the user device to adjust weight values that are associated with particular data elements. In this way, the insight platform may determine updated market indices based on the adjusted weight values, and provide information that identifies the updated market indices. In this way, the user may ascertain the real-time impact of various data elements on market index determinations. (adjusting the data element scores for the non-dominant area to be the data element scores of the dominant area would show the service demand potential for the non-dominant area)
Claim 4 stands rejected based on the same citations and rationale as applied to Claim 1.
Regarding Claim 5, Gregory discloses: The service demand potential prediction device according to claim 1, wherein the processing circuitry is further configured to select, as the dominant area, an area where a multiplication result of: the calculated relative index for each area, and a weight exceeds a predetermined threshold. (0085 - insight platform 230 may determine particular areas (e.g., areas associated with market indices that satisfy a threshold (dominant area based on threshold))
0019, Figure 1E - ...a darker shaded portion of the heat map may indicate a higher market index relative to a lighter shaded portion....the user may interact with the user device to adjust weight values that are associated with particular data elements. In this way, the insight platform may determine updated market indices based on the adjusted weight values, and provide information that identifies the updated market indices. (adjusting a data element’s weight adjusts a market index by a multiplier) In this way, the user may ascertain the real-time impact of various data elements on market index determinations.
Gregory does not explicitly state the weight is a “pre-acquired combined market share rate of the own-company service and the other-company service.”
ICN discloses the only two entities in a market are Entity AB and Entity C and each share is currently weighted by 100% (if another entity joins the market, their relative share rate would be proportionally adjusted)
Page 26 (Bottom) – “Market shares may be based on the total sales (number of service provision results) (or output or some other measure) of the product, in a defined area, to be held by the merging firms and each of their rivals in the relevant market...”
Page 36 - Even where the merging parties’ combined market shares appear reasonably low, for example below 25 per cent, a merger may still raise a competition issue. For example, supplier A (14 per cent share) merges with supplier B (10 per cent), leaving only two suppliers, the merged entity AB (24 per cent) and C (76 per cent)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gregory’s weight to include ICN’s pre-acquired combined market share rate of the own-company service and the other-company service, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 6, Gregory discloses: The service demand potential prediction device according to claim l, wherein the processing circuitry is further configured to calculate a reference relative index indicating a relative index of the own-company service to the other-company service for all areas based on the number of service provision results of the own-company service and the number of service provision results of the other-company service in all areas, (Under BRI – “for all areas” and “in all areas” may still be interpreted as calculating for each of the areas;
(0011, 0061, 0087, Figure 1B-E – insight platform acquires other data source’s data elements (ie. concentration values, market share, etc.) (service provision results for other-company service) for each area;
0011, 0061, Figure 1D-E (market index values) - ...an insight platform to...determine market indices (relative indices) based on the data element scores.....A market index, as described elsewhere herein, may include information that identifies a potential value associated with a geographic area, identifies the likelihood of an entity realizing the potential value, (service provision results for target company) and/or the like)
and selects, as the dominant area, an area where the relative index for each area exceeds a multiplication result of the reference relative index and a predetermined coefficient for threshold adjustment. (“for threshold adjustment” is intended use language and given limited patentable weight; under BRI- “threshold adjustment” might be considered adjustment to the threshold or adjusting the relative index to ensure satisfying a threshold;
0085 - insight platform 230 may determine particular areas (e.g., areas associated with market indices that satisfy a threshold (dominant area based on threshold))
0019, Figure 1E - ...a darker shaded portion of the heat map may indicate a higher market index relative to a lighter shaded portion)....the user may interact with the user device to adjust weight values that are associated with particular data elements. In this way, the insight platform may determine updated market indices based on the adjusted weight values, and provide information that identifies the updated market indices. (adjusting a data element’s weight/multiplication adjusts a market index by a predetermined coefficient for satisfying a threshold) In this way, the user may ascertain the real-time impact of various data elements on market index determinations.
Gregory does not explicitly state: the relative index indicates “a relative ratio of the own-company service to the other-company service based on the number of service provision results of the own-company service and the number of service provision results of the other-company service”
ICN discloses Entity AB and Entity C as the only two entities in a market. Entity AB’s market share is based on the ratio of its total sales divided by the sum of both Entities AB and C’s total sales (service provision results of the own-company service and other-company service)
Page 26 (Bottom) – “Market shares may be based on the total sales (number of service provision results) (or output or some other measure) of the product, in a defined area, to be held by the merging firms and each of their rivals in the relevant market...”
Page 36 - Even where the merging parties’ combined market shares appear reasonably low, for example below 25 per cent, a merger may still raise a competition issue. For example, supplier A (14 per cent share) merges with supplier B (10 per cent), leaving only two suppliers, the merged entity AB (24 per cent) and C (76 per cent)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gregory’s relative index to include ICN’s ratio of the service provision results of the own-company service to the sum of the service provision results of the own-company and other-company service, helping a merged entity determine market share and evaluate the status of a merger (ie. anti-competitive) (Page 4, 8).
Regarding Claim 7, Gregory discloses: The service demand potential prediction device according to claim 1, wherein the processing circuitry is further configured to select, as the dominant area, an area where the calculated relative index for each area exceeds a predetermined threshold. (0085 - insight platform 230 may determine particular areas (e.g., areas associated with market indices that satisfy a threshold (dominant area based on threshold))
0018-0019, Figure 1D-E – using an area identified as a “higher market index”)
Claims 8 and 9 stands rejected based on the same citations and rationale as Claim 3 and 1, respectively
Conclusion
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT ROSS whose telephone number is (571) 270-1555. The examiner can normally be reached on Monday-Friday 8:00 AM - 5:00 PM E.S.T..
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623